Auto-update from GitHub

main_model.ipynb

@@ -949,7 +949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 1,
    "id": "5bf23e10-0a93-4b2f-9508-34bb0974d24c",
    "metadata": {},
    "outputs": [],
@@ -968,9 +968,6 @@
     "# Load test data (in list of dictionaries)\n",
     "with open(\"test_data.json\", \"r\") as file:\n",
     "    packing_data = json.load(file)\n",
-    "# Extract all trip descriptions and trip_types\n",
-    "# trip_descriptions = [trip['description'] for trip in packing_data]\n",
-    "# trip_types = [trip['trip_types'] for trip in packing_data]\n",
     "\n",
     "# Load packing item data\n",
     "with open(\"packing_templates_self_supported_offgrid_expanded.json\", \"r\") as file:\n",
@@ -979,7 +976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 2,
    "id": "61ebbe99-2563-4c99-ba65-d2312c9d5844",
    "metadata": {},
    "outputs": [
@@ -987,16 +984,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7863\n",
-      "Running on public URL: https://ffd87a6918736e600d.gradio.live\n",
+      "Running on local URL:  http://127.0.0.1:7860\n",
       "\n",
-      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+      "To create a public link, set `share=True` in `launch()`.\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"https://ffd87a6918736e600d.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1004,6 +1000,13 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
+     ]
     }
    ],
    "source": [

packing_templates_self_supported_offgrid_expanded.json

@@ -1,696 +1,695 @@
 {
   "hut trek (summer)": [
-    "lichtgewicht trekkingrugzak (30\u201345 liter)",
-    "waterzak of drinkflessen",
-    "sneldrogende kleding",
-    "regenjas",
-    "snacks/energierepen",
-    "wandelschoenen",
-    "hoed of pet",
-    "zonnebrandcr\u00e8me",
-    "lakenzak",
-    "oordoppen"
+    "lightweight trekking backpack (30–45 liters)",
+    "hydration bladder or water bottles",
+    "quick‑dry clothing",
+    "rain jacket",
+    "snacks / energy bars",
+    "hiking boots",
+    "hat or cap",
+    "sunscreen",
+    "sheet liner",
+    "earplugs"
   ],
   "hut trek (winter)": [
-    "slaapzak (lichte, warme variant)",
-    "lakenzak",
-    "binnenkleding voor hut (thermo / fleece)",
-    "hutpantoffels / Crocs",
-    "oordoppen",
-    "snacks voor onderweg",
-    "contant geld voor hutbetalingen"
+    "sleeping bag (light, warm variant)",
+    "sheet liner",
+    "indoor hut clothing (thermo / fleece)",
+    "hut slippers / Crocs",
+    "earplugs",
+    "snacks for along the way",
+    "cash for hut payments"
   ],
   "camping trip (wild camping)": [
-    "tent of tarp",
-    "slaapmat",
-    "slaapzak",
-    "hoofdlamp",
-    "mes of multitool",
-    "waterfilter",
-    "kookset + brander",
-    "voedsel en snacks",
-    "aansteker",
-    "toilettas met biologisch afbreekbare zeep"
+    "tent or tarp",
+    "sleeping pad",
+    "sleeping bag",
+    "headlamp",
+    "knife or multitool",
+    "water filter",
+    "cookset + stove",
+    "food and snacks",
+    "lighter",
+    "toiletry bag with biodegradable soap"
   ],
   "camping trip (campground)": [
     "tent",
-    "kussen",
-    "stoeltje",
-    "kampeertafel (optioneel)",
-    "lamp of lantaarn",
-    "verlengsnoer (voor stroomcamping)",
-    "koelbox",
-    "servies & bestek",
-    "afwasspullen",
-    "boodschappen"
+    "pillow",
+    "chair",
+    "camping table (optional)",
+    "lamp or lantern",
+    "extension cord (for powered campsites)",
+    "cooler box",
+    "dishes & cutlery",
+    "washing up supplies",
+    "groceries"
   ],
   "skitocht / skitour": [
-    "touringski's of splitboard",
-    "stijgvellen",
-    "lawinepieper (transceiver)",
-    "schep",
-    "sonde",
-    "gore-tex jas en broek",
-    "donsjas of warme isolatielaag",
-    "helm",
-    "handschoenen (2 paar)",
-    "ski- of zonnebril",
-    "thermosfles",
-    "sneldrogende baselayers",
-    "gamaschen (bij diepe sneeuw)",
-    "tourrugzak met ski-bevestiging",
-    "laagjes voor temperatuurregeling",
-    "energierepen of sportvoeding",
-    "GPS of offline kaarten",
-    "EHBO-set",
-    "lichte handschoenen voor het stijgen"
+    "touring skis or splitboard",
+    "climbing skins",
+    "avalanche beacon (transceiver)",
+    "shovel",
+    "probe",
+    "Gore‑Tex jacket and pants",
+    "down jacket or warm insulation layer",
+    "helmet",
+    "gloves (2 pairs)",
+    "ski or sunglasses",
+    "thermos bottle",
+    "quick‑dry base layers",
+    "gaiters (in deep snow)",
+    "touring backpack with ski attachment",
+    "layers for temperature control",
+    "energy bars or sports nutrition",
+    "GPS or offline maps",
+    "first aid kit",
+    "light gloves for climbing"
   ],
   "snowboard / splitboard trip": [
-    "splitboard of snowboard",
-    "lawinepieper",
-    "helm",
-    "dikke handschoenen",
-    "skibril",
-    "laagjes kleding",
-    "tourbindingen (voor splitboard)",
-    "stijgvellen",
-    "sonde en schep",
-    "lichte handschoenen voor het stijgen"
+    "splitboard or snowboard",
+    "avalanche beacon",
+    "helmet",
+    "thick gloves",
+    "ski goggles",
+    "layers of clothing",
+    "tour bindings (for splitboard)",
+    "climbing skins",
+    "probe and shovel",
+    "light gloves for climbing"
   ],
   "long-distance hike / thru-hike": [
-    "lichtgewicht rugzak",
-    "waterfilter",
-    "voedselvoorraad",
-    "navigatie (kaart/kompas/GPS)",
+    "lightweight backpack",
+    "water filter",
+    "food supply",
+    "navigation (map/compass/GPS)",
     "first aid kit",
-    "trail runners of lichte wandelschoenen",
-    "zonbescherming",
-    "regenbescherming",
-    "natte doekjes",
-    "sneldrogende handdoek"
+    "trail runners or lightweight hiking shoes",
+    "sun protection",
+    "rain protection",
+    "wet wipes",
+    "quick‑dry towel"
   ],
   "digital nomad trip": [
-    "laptop en oplader",
-    "wereldstekker",
-    "noise-cancelling koptelefoon",
-    "wifi-hotspot of lokale simkaart",
-    "powerbank",
-    "notitieboekje",
-    "lichte kleding",
-    "comfortabele rugzak of trolley",
-    "mifi-router of portable wifi-hotspot"
+    "laptop and charger",
+    "world adapter plug",
+    "noise‑cancelling headphones",
+    "WiFi hotspot or local SIM card",
+    "power bank",
+    "notebook",
+    "light clothing",
+    "comfortable backpack or trolley",
+    "MiFi router or portable WiFi hotspot"
   ],
   "city trip": [
-    "dagrugzak",
-    "oplader voor telefoon",
-    "stadsplattegrond / offline maps",
-    "betaalkaart / contant geld",
-    "comfortabele schoenen",
-    "waterfles",
-    "reisgids of highlightslijst",
-    "lichte jas of regenjas"
+    "daypack",
+    "phone charger",
+    "city map / offline maps",
+    "bank card / cash",
+    "comfortable shoes",
+    "water bottle",
+    "guidebook or highlights list",
+    "light jacket or raincoat"
   ],
   "road trip (car/camper)": [
-    "autopapieren",
-    "EHBO-kit",
-    "snacks en drinken",
-    "telefoonhouder / navigatie",
-    "kampeeruitrusting (indien overnachten)",
-    "reisgids of kaarten",
-    "zonnebril",
-    "stoel en tafel",
-    "vermaak (boek, muziek, spelletjes)"
+    "car documents",
+    "first aid kit",
+    "snacks and drinks",
+    "phone holder / navigation",
+    "camping gear (if staying overnight)",
+    "travel guide or maps",
+    "sunglasses",
+    "chair and table",
+    "entertainment (book, music, games)"
   ],
   "festivaltrip": [
     "tent",
-    "matje en slaapzak",
-    "oordoppen",
-    "poncho of regenjas",
+    "pad and sleeping bag",
+    "earplugs",
+    "poncho or rain jacket",
     "glitter / outfit",
-    "herbruikbare beker",
-    "zonnebrand",
-    "cash / pinpas",
-    "vriendenafspraken",
+    "reusable cup",
+    "sunscreen",
+    "cash / card",
+    "friend meetups",
     "snacks"
   ],
   "yoga / wellness retreat": [
-    "yogamat of yogahanddoek",
-    "comfortabele kleding",
-    "waterfles",
-    "dagboek / pen",
-    "warme trui of sjaal",
-    "sandalen",
-    "etherische olie (optioneel)",
-    "boek / meditatiemateriaal"
+    "yoga mat or yoga towel",
+    "comfortable clothing",
+    "water bottle",
+    "journal / pen",
+    "warm sweater or scarf",
+    "sandals",
+    "essential oils (optional)",
+    "book / meditation material"
   ],
   "micro-adventure / weekend trip": [
-    "dagrugzak",
-    "snacks en drinken",
-    "regenjas",
-    "warme laag",
-    "kaart of GPS",
-    "toilettasje",
-    "hoofddeksel",
-    "compacte slaapuitrusting (indien overnachting)"
+    "daypack",
+    "snacks and drinks",
+    "rain jacket",
+    "warm layer",
+    "map or GPS",
+    "toiletry bag",
+    "head cover",
+    "compact sleeping gear (if overnighting)"
   ],
   "beach vacation": [
-    "zwemkleding",
-    "strandlaken",
-    "zonnebrandcr\u00e8me",
-    "zonnebril",
-    "hoed of pet",
-    "slippers",
-    "luchtige kleding",
-    "waterfles",
-    "strandtas",
-    "boek of e-reader"
+    "swimwear",
+    "beach towel",
+    "sunscreen",
+    "sunglasses",
+    "hat or cap",
+    "flip flops",
+    "lightweight clothing",
+    "water bottle",
+    "beach bag",
+    "book or e‑reader"
   ],
   "cultural exploration": [
-    "dagrugzak",
-    "oplader voor telefoon",
-    "stadsplattegrond / offline maps",
-    "betaalkaart / contant geld",
-    "comfortabele schoenen",
-    "waterfles",
-    "reisgids of highlightslijst",
-    "lichte jas of regenjas"
+    "daypack",
+    "phone charger",
+    "city map / offline maps",
+    "bank card / cash",
+    "comfortable shoes",
+    "water bottle",
+    "guidebook or highlights list",
+    "light jacket or raincoat"
   ],
   "nature escape": [
-    "dagrugzak",
-    "oplader voor telefoon",
-    "betaalkaart / contant geld",
-    "comfortabele schoenen",
-    "waterfles",
-    "downtime entertainment (e.g. book/ebook, games, laptop, journal)"
+    "daypack",
+    "phone charger",
+    "bank card / cash",
+    "comfortable shoes",
+    "water bottle",
+    "downtime entertainment (e.g. book / ebook, games, laptop, journal)"
   ],
   "swimming": [
-    "badkleding",
-    "handdoek",
-    "zonnebrandcr\u00e8me",
-    "slippers",
-    "zwembril",
-    "waterfles"
+    "swimsuit",
+    "towel",
+    "sunscreen",
+    "flip flops",
+    "swimming goggles",
+    "water bottle"
   ],
   "going to the beach": [
-    "strandlaken",
-    "zwemkleding",
-    "zonnebrand",
-    "koeltas",
-    "strandstoel",
-    "zonnehoed"
+    "beach towel",
+    "swimwear",
+    "sunscreen",
+    "cooler",
+    "beach chair",
+    "sun hat"
   ],
   "relaxing": [
-    "comfortabele kleding",
-    "boek of e-reader",
-    "zitkussen of strandmat",
-    "muziek / koptelefoon"
+    "comfortable clothing",
+    "book or e‑reader",
+    "seat cushion or beach mat",
+    "music / headphones"
   ],
   "sightseeing": [
-    "dagrugzak",
-    "waterfles",
-    "camera of smartphone",
-    "kaart of offline maps",
-    "zonnebril"
+    "daypack",
+    "water bottle",
+    "camera or smartphone",
+    "map or offline maps",
+    "sunglasses"
   ],
   "biking": [
-    "fiets of huurfiets",
-    "helm",
-    "sportkleding",
-    "fietslicht en slot",
-    "reparatieset"
+    "bike or rental bike",
+    "helmet",
+    "sports clothing",
+    "bike light and lock",
+    "repair kit"
   ],
   "running": [
-    "hardloopschoenen",
-    "sportkleding",
-    "zweetband of pet",
-    "waterfles of belt",
-    "sporthorloge (optioneel)"
+    "running shoes",
+    "sports clothing",
+    "headband or cap",
+    "water bottle or belt",
+    "sports watch (optional)"
   ],
   "skiing": [
-    "ski\u2019s en stokken",
-    "skischoenen",
-    "helm",
-    "skibril",
-    "skipas",
-    "dikke handschoenen",
-    "gore-tex kleding"
+    "skis and poles",
+    "ski boots",
+    "helmet",
+    "ski goggles",
+    "ski pass",
+    "thick gloves",
+    "Gore‑Tex clothing"
   ],
   "cross-country skiing": [
-    "langlaufski's en stokken",
-    "langlaufschoenen",
-    "ademende thermokleding",
-    "winddichte en waterafstotende buitenlaag",
-    "dunne handschoenen",
-    "muts of hoofdband",
-    "zonnebril of sportbril",
-    "buff of nekwarmer",
-    "lichte rugzak met water en snacks",
-    "EHBO-set"
+    "cross-country skis and poles",
+    "cross-country ski boots",
+    "breathable thermal clothing",
+    "windproof and water-repellent outer layer",
+    "thin gloves",
+    "hat or headband",
+    "sunglasses or sport glasses",
+    "buff or neck warmer",
+    "light backpack with water and snacks",
+    "first aid kit"
   ],
   "ski touring": [
-    "algemene items voor deze situatie",
-    "extra kleding of uitrusting indien nodig"
+    "general items for this situation",
+    "extra clothing or gear if needed"
   ],
   "hiking": [
-    "wandelschoenen of trailrunners",
-    "hiking sokken (anti-blaren)",
-    "hikingstokken",
-    "dagrugzak",
-    "regenjas of poncho",
-    "waterfles of waterzak",
-    "snacks / energierepen",
-    "zonnebrand en zonnebril",
-    "pet of hoed",
-    "blarenpleisters of tape",
-    "EHBO-set",
-    "navigatie (kaart, kompas of GPS)"
+    "hiking boots or trail runners",
+    "hiking socks (anti-blister)",
+    "hiking poles",
+    "daypack",
+    "rain jacket or poncho",
+    "water bottle or hydration bladder",
+    "snacks / energy bars",
+    "sunscreen and sunglasses",
+    "cap or hat",
+    "blister plasters or tape",
+    "first aid kit",
+    "navigation (map, compass or GPS)"
   ],
   "hut-to-hut hiking": [
-    "lichtgewicht trekkingrugzak (30\u201345 liter)",
-    "waterflessen of waterzak",
-    "snacks voor onderweg",
-    "regenjas",
-    "licht donsjack of warme laag",
-    "sneldrogende kleding",
-    "wandelschoenen",
-    "lakenzak",
-    "pantoffels of slippers voor hut",
-    "oordoppen",
-    "reserveringsbevestiging / contant geld voor hut"
+    "lightweight trekking backpack (30–45 liters)",
+    "water bottles or hydration bladder",
+    "snacks for along the way",
+    "rain jacket",
+    "light down jacket or warm layer",
+    "quick‑dry clothing",
+    "hiking boots",
+    "sheet liner",
+    "slippers or indoor shoes for hut",
+    "earplugs",
+    "reservation confirmation / cash for hut"
   ],
   "rock climbing": [
-    "klimschoenen",
-    "klimgordel",
-    "zekeringsapparaat",
+    "climbing shoes",
+    "climbing harness",
+    "belay device",
     "chalk bag",
-    "helm"
+    "helmet"
   ],
   "ice climbing": [
-    "ijsbijlen",
+    "ice axes",
     "crampons",
-    "klimtouw",
-    "warme isolatielagen",
-    "klimgordel en helm"
+    "climbing rope",
+    "warm insulation layers",
+    "climbing harness and helmet"
   ],
   "snowshoe hiking": [
-    "algemene items voor deze situatie",
-    "extra kleding of uitrusting indien nodig"
+    "general items for this situation",
+    "extra clothing or gear if needed"
   ],
   "kayaking / canoeing": [
-    "kayak of kano",
-    "peddel",
-    "reddingsvest",
-    "drybag",
-    "waterschoenen"
+    "kayak or canoe",
+    "paddle",
+    "life jacket",
+    "dry bag",
+    "water shoes"
   ],
   "stand-up paddleboarding (SUP)": [
-    "SUP-board en peddel",
-    "reddingsvest",
-    "drybag",
-    "zonnebril met koord"
+    "SUP board and paddle",
+    "life jacket",
+    "dry bag",
+    "sunglasses with strap"
   ],
   "snorkeling": [
-    "snorkel en duikbril",
-    "zwemvliezen",
-    "badkleding",
-    "waterdichte tas"
+    "snorkel and mask",
+    "fins",
+    "swimwear",
+    "waterproof bag"
   ],
   "scuba diving": [
-    "duikbril en snorkel",
+    "mask and snorkel",
     "wetsuit",
-    "vinnen",
-    "duikcomputer",
-    "ademautomaat (indien eigen)"
+    "fins",
+    "dive computer",
+    "regulator (if own)"
   ],
   "surfing": [
     "surfboard",
     "wetsuit",
     "wax",
     "board leash",
-    "poncho of handdoek"
+    "poncho or towel"
   ],
   "paragliding": [
     "paraglider",
-    "helm",
-    "handschoenen",
-    "wandelschoenen",
-    "windjack"
+    "helmet",
+    "gloves",
+    "hiking boots",
+    "wind jacket"
   ],
   "horseback riding": [
-    "rijbroek",
-    "rijlaarzen of schoenen met hak",
-    "handschoenen",
-    "helm",
-    "eventueel eigen zadel of stijgbeugels"
+    "riding pants",
+    "riding boots or shoes with heel",
+    "gloves",
+    "helmet",
+    "optional own saddle or stirrups"
   ],
   "photography": [
-    "camera + lenzen",
-    "statief",
-    "geheugenkaart(en)",
-    "extra accu's",
-    "lensdoekje"
+    "camera + lenses",
+    "tripod",
+    "memory card(s)",
+    "extra batteries",
+    "lens cloth"
   ],
   "fishing": [
-    "hengel",
-    "aas / kunstaas",
-    "visvergunning",
-    "laarzen of waadpak",
-    "koelbox (optioneel)"
+    "fishing rod",
+    "bait / lures",
+    "fishing license",
+    "boots or waders",
+    "cooler box (optional)"
   ],
   "rafting": [
-    "reddingsvest",
-    "waterdichte tas",
-    "waterschoenen",
-    "sneldrogende kleding",
-    "helm"
+    "life jacket",
+    "waterproof bag",
+    "water shoes",
+    "quick‑dry clothing",
+    "helmet"
   ],
   "yoga": [
-    "algemene items voor deze situatie",
-    "extra kleding of uitrusting indien nodig"
+    "general items for this situation",
+    "extra clothing or gear if needed"
   ],
   "cold destination / winter": [
-    "thermokleding",
-    "muts en handschoenen",
-    "warme jas",
-    "waterdichte schoenen",
-    "lippenbalsem"
+    "thermal clothing",
+    "hat and gloves",
+    "warm jacket",
+    "waterproof shoes",
+    "lip balm"
   ],
   "warm destination / summer": [
-    "luchtige kleding",
-    "zonnebril",
-    "zonnecr\u00e8me",
-    "waterfles",
-    "hoed of pet"
+    "lightweight clothing",
+    "sunglasses",
+    "sunscreen",
+    "water bottle",
+    "hat or cap"
   ],
   "variable weather / spring / autumn": [
-    "regenjas",
-    "laagjeskleding",
-    "warme trui",
-    "waterdichte schoenen"
+    "rain jacket",
+    "layered clothing",
+    "warm sweater",
+    "waterproof shoes"
   ],
   "tropical / humid": [
-    "lichtgewicht kleding",
-    "insectenspray",
-    "zonnecr\u00e8me",
-    "regenponcho",
-    "sneldrogende handdoek"
+    "lightweight clothing",
+    "insect repellent",
+    "sunscreen",
+    "rain poncho",
+    "quick‑dry towel"
   ],
   "dry / desert-like": [
-    "zonnebril",
-    "zonnecr\u00e8me",
-    "hoofdbescherming",
-    "lichte lange mouwen",
-    "veel water"
+    "sunglasses",
+    "sunscreen",
+    "head protection",
+    "light long sleeves",
+    "lots of water"
   ],
   "ultralight": [
-    "lichtgewicht rugzak (< 1kg)",
-    "minimalistische shelter (tarp of tent)",
-    "lichtgewicht slaapmat",
-    "quilt of donsdeken",
-    "titanium kookset",
-    "beperkte kleding (laagjes!)",
-    "compacte regenjas",
-    "sneldrogende handdoek",
-    "tandenborstel (afgezaagd ;))"
+    "lightweight backpack (< 1kg)",
+    "minimalist shelter (tarp or tent)",
+    "lightweight sleeping pad",
+    "quilt or down blanket",
+    "titanium cookset",
+    "limited clothing (layers!)",
+    "compact rain jacket",
+    "quick‑dry towel",
+    "toothbrush (shortened ;))"
   ],
   "lightweight (but comfortable)": [
-    "lichte tent of tarp",
-    "comfortabele slaapmat",
-    "lichtgewicht kookset",
-    "compact kledingpakket",
-    "sneldrogende handdoek"
+    "light tent or tarp",
+    "comfortable sleeping pad",
+    "lightweight cookset",
+    "compact clothing pack",
+    "quick‑dry towel"
   ],
   "luxury (including evening wear)": [
-    "nette schoenen",
-    "jurk of overhemd",
-    "parfum/deodorant",
-    "accessoires",
-    "reistoilettas met essentials"
+    "dress shoes",
+    "dress or shirt",
+    "perfume / deodorant",
+    "accessories",
+    "toiletry bag with essentials"
   ],
   "minimalist": [
-    "1 set kleding voor elke situatie",
-    "compacte tandenborstel",
-    "alles-in-\u00e9\u00e9n zeep",
-    "lichtgewicht handdoek",
-    "kleine rugzak"
+    "1 set of clothing for every situation",
+    "compact toothbrush",
+    "all‑in‑one soap",
+    "lightweight towel",
+    "small backpack"
   ],
   "casual": [
-    "jeans of comfortabele broek",
+    "jeans or comfortable pants",
     "t-shirts",
-    "trui of hoodie",
+    "sweater or hoodie",
     "sneakers",
-    "zonnebril"
+    "sunglasses"
   ],
   "formal (business trip)": [
-    "overhemd / blouse",
-    "net jasje",
-    "nette schoenen",
-    "laptop en oplader",
-    "naamkaartjes / documenten"
+    "shirt / blouse",
+    "smart jacket",
+    "dress shoes",
+    "laptop and charger",
+    "business cards / documents"
   ],
   "conservative": [
-    "bedekkende kleding",
-    "sjaal of omslagdoek",
-    "lange broek of rok",
-    "gesloten schoenen"
+    "covering clothing",
+    "scarf or shawl",
+    "long pants or skirt",
+    "closed shoes"
   ],
   "outdoor": [
-    "regenjas",
-    "waterdichte schoenen",
-    "bivakzak of tarp",
-    "hoofdlamp",
-    "navigatie"
+    "rain jacket",
+    "waterproof shoes",
+    "bivvy bag or tarp",
+    "headlamp",
+    "navigation"
   ],
   "indoor": [
-    "pantoffels",
-    "comfortabele kleding",
-    "pyjama",
-    "oplader",
-    "toilettas"
+    "slippers",
+    "comfortable clothing",
+    "pajamas",
+    "charger",
+    "toiletry bag"
   ],
   "huts with half board": [
-    "reserveringsbevestiging",
-    "lakenzak (vaak verplicht)",
-    "pantoffels of slippers voor binnen",
-    "lichte pyjama of slaapkleding",
-    "oorstopjes",
-    "waterfles",
-    "snacks voor onderweg",
-    "kleine toilettas",
-    "contant geld voor betalingen",
-    "lichte handdoek",
-    "zaklamp of hoofdlamp"
+    "reservation confirmation",
+    "sheet liner (often required)",
+    "slippers or indoor shoes for inside",
+    "light pajamas or sleepwear",
+    "earplugs",
+    "water bottle",
+    "snacks for along the way",
+    "small toiletry bag",
+    "cash for payments",
+    "light towel",
+    "flashlight or headlamp"
   ],
   "sleeping in a tent": [
-    "tent (1- of 2-persoons, afhankelijk van reis)",
-    "grondzeil",
-    "slaapmat",
-    "slaapzak (passend bij temperatuur)",
-    "hoofdlamp of zaklamp",
-    "sneldrogende handdoek",
-    "kussen of opblaasbaar hoofdkussen",
-    "oorstopjes",
-    "toilettas",
-    "powerbank",
-    "waterfles",
-    "boek of e-reader",
-    "EHBO-set"
+    "tent (1‑ or 2‑person, depending on trip)",
+    "groundsheet",
+    "sleeping pad",
+    "sleeping bag (suitable for temperature)",
+    "headlamp or flashlight",
+    "quick‑dry towel",
+    "pillow or inflatable pillow",
+    "earplugs",
+    "toiletry bag",
+    "power bank",
+    "water bottle",
+    "book or e‑reader",
+    "first aid kit"
   ],
   "sleeping in a car": [
-    "slaapmat die past in auto",
-    "warme slaapzak",
-    "zonneschermen of verduistering",
-    "kussen",
-    "waterfles binnen handbereik",
-    "powerbank of 12V-lader",
-    "toilettas",
-    "snacks voor de nacht",
-    "thermische deken (voor koude nachten)"
+    "sleeping pad that fits in car",
+    "warm sleeping bag",
+    "sunshades or blackout covers",
+    "pillow",
+    "water bottle within reach",
+    "power bank or 12V charger",
+    "toiletry bag",
+    "snacks for the night",
+    "thermal blanket (for cold nights)"
   ],
   "own vehicle": [
-    "rijbewijs",
-    "autopapieren",
-    "EHBO-set",
-    "navigatie of smartphone",
-    "telefoonhouder"
+    "driver’s license",
+    "car documents",
+    "first aid kit",
+    "navigation or smartphone",
+    "phone holder"
   ],
   "no own vehicle": [
-    "rugzak",
-    "waterfles",
-    "lichte schoenen",
-    "openbaar vervoer app of ticket"
+    "backpack",
+    "water bottle",
+    "light shoes",
+    "public transport app or ticket"
   ],
   "off-grid / no electricity": [
-    "powerbank (minstens 10.000 mAh)",
-    "zonnepaneel of draagbaar laadsysteem",
-    "hoofdlamp + extra batterijen",
-    "oplaadbare batterijen en oplader",
-    "back-up verlichting (bijv. kleine zaklamp)",
-    "papieren kaart en kompas",
-    "notitieboekje + pen",
-    "noodcommunicatie (bijv. GPS beacon of satellietboodschapper)",
-    "opvouwbaar zonnepaneel (indien langere tochten)",
-    "navigatieapparaat met offline kaarten",
-    "extra opladerkabels",
-    "USB-hub (voor meerdere devices)",
-    "verpakking om elektronica droog te houden"
+    "power bank (at least 10,000 mAh)",
+    "solar panel or portable charging system",
+    "headlamp + extra batteries",
+    "rechargeable batteries and charger",
+    "backup lighting (e.g. small flashlight)",
+    "paper map and compass",
+    "notebook + pen",
+    "emergency communication (e.g. GPS beacon or satellite messenger)",
+    "foldable solar panel (if on longer trips)",
+    "navigation device with offline maps",
+    "extra charger cables",
+    "USB hub (for multiple devices)",
+    "packaging to keep electronics dry"
   ],
   "self-supported (bring your own cooking gear)": [
-    "lichtgewicht kooktoestel (gas, benzine of alcohol)",
-    "brandstof (voldoende voor aantal dagen)",
-    "pan of keteltje",
-    "spork of lepel",
-    "opvouwbaar snijplankje (optioneel)",
-    "aandrukbare kom of beker",
-    "aansteker + lucifers (waterdicht verpakt)",
-    "gedroogde of gevriesdroogde maaltijden",
-    "snacks / noodbars",
-    "afwasmiddel (biologisch afbreekbaar) + sponsje",
-    "opslagzakken voor afval",
-    "waterfilter of -pomp",
-    "chloordruppels of zuiveringstabletten",
-    "minstens 2 liter wateropslag per persoon",
-    "food bag of hangzak voor voedsel (wild-safe)"
+    "lightweight stove (gas, petrol or alcohol)",
+    "fuel (enough for several days)",
+    "pan or small pot",
+    "spork or spoon",
+    "foldable cutting board (optional)",
+    "press‑on bowl or mug",
+    "lighter + matches (waterproof packed)",
+    "dried or freeze‑dried meals",
+    "snacks / emergency bars",
+    "biodegradable soap + sponge",
+    "bags for waste",
+    "water filter or pump",
+    "chlorine drops or purification tablets",
+    "at least 2 liters of water storage per person",
+    "food bag or hanging bag (wild-safe)"
   ],
   "travel with children": [
-    "snacks en speelgoed",
-    "EHBO-set met pleisters",
-    "extra kleding",
-    "kinderdrager of buggy",
-    "zonbescherming",
-    "extra snacks voor kinderen",
-    "favoriet speelgoed of knuffel",
-    "reisstoel of draagzak",
-    "natte doekjes",
-    "luiers of potje (afhankelijk van leeftijd)",
-    "extra set kleding per dag",
-    "reisapotheek voor kinderen",
-    "kinderzonnebrand",
-    "activiteitenboekje of tablet met filmpjes",
-    "snelle snacks voor onderweg",
-    "kinder slaapzak of slaapmat",
-    "reisbedje of matrasje (voor jonge kinderen)",
-    "luiers of oefenbroekjes",
-    "flesjes en voeding (indien van toepassing)",
-    "babyfoon (voor verblijf op locatie)",
-    "speen of fopspeen",
-    "kinder EHBO-set (inclusief thermometer)",
-    "zonnehoedje of muts",
-    "regenhoes voor kinderwagen of draagzak",
-    "hydraterende cr\u00e8me (voor gevoelige huid)"
+    "snacks and toys",
+    "first aid kit with bandages",
+    "extra clothing",
+    "child carrier or stroller",
+    "sun protection",
+    "extra snacks for children",
+    "favorite toy or stuffed animal",
+    "travel chair or sling",
+    "wet wipes",
+    "diapers or potty (depending on age)",
+    "extra clothing set per day",
+    "children’s travel pharmacy",
+    "children’s sunscreen",
+    "activity book or tablet with films",
+    "quick snacks for en route",
+    "child sleeping bag or pad",
+    "travel crib or mattress (for young children)",
+    "diapers or training pants",
+    "bottles and food (if applicable)",
+    "baby monitor (for staying at location)",
+    "pacifier or dummy",
+    "kids first aid kit (including thermometer)",
+    "sun hat or cap",
+    "rain cover for stroller or carrier",
+    "hydrating cream (for sensitive skin)"
   ],
   "pet-friendly": [
-    "voer en waterbak",
-    "hondenriem of tuigje",
-    "poepzakjes",
-    "reismatje of deken",
-    "vaccinatieboekje"
+    "food and water bowl",
+    "dog leash or harness",
+    "poop bags",
+    "travel mat or blanket",
+    "vaccination booklet"
   ],
   "rainy climate": [
-    "regenjas of poncho",
-    "waterdichte rugzakhoes",
-    "sneldrogende kleding",
-    "rubberen schoenen"
+    "rain jacket or poncho",
+    "waterproof backpack cover",
+    "quick‑dry clothing",
+    "rubber shoes"
   ],
   "snow and ice": [
-    "warme laarzen",
-    "dikke handschoenen",
-    "sneeuwbril",
-    "thermokleding",
-    "gripzolen / spikes"
+    "warm boots",
+    "thick gloves",
+    "snow goggles",
+    "thermal clothing",
+    "traction soles / spikes"
   ],
   "high alpine terrain": [
-    "wandelstokken",
-    "kaart en kompas",
-    "wind- en waterdichte jas",
-    "extra voeding",
-    "EHBO-kit"
+    "trekking poles",
+    "map and compass",
+    "wind‑ and waterproof jacket",
+    "extra food",
+    "first aid kit"
   ],
   "snow, ice and avalanche-prone terrain": [
-    "lawinepieper",
-    "schep",
-    "sonde",
-    "airbag rugzak (indien beschikbaar)",
-    "kennis van lawineveiligheid / cursus",
-    "kaart en kompas / GPS",
-    "partner check voor vertrek"
-  ],
-  "no special conditions": [
-  ],
+    "avalanche beacon",
+    "shovel",
+    "probe",
+    "airbag backpack (if available)",
+    "knowledge of avalanche safety / course",
+    "map and compass / GPS",
+    "partner check before departure"
+  ],
+  "no special conditions": [],
   "1 day": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "2 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "3 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "4 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "5 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "6 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "7 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "7+ days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "default_items": [
-    "telefoon + oplader",
-    "powerbank",
-    "identiteitsbewijs of paspoort",
-    "betaalmiddelen (pinpas / contant geld)",
-    "verzekeringspas / reisverzekering-info",
-    "toilettas (tandpasta, borstel, deodorant, zeep)",
-    "zonnebrandcr\u00e8me",
-    "lippenbalsem",
-    "ondergoed",
-    "sokken",
-    "regenjas of windjack",
-    "waterfles",
+    "phone + charger",
+    "power bank",
+    "identity document or passport",
+    "payment methods (debit card / cash)",
+    "insurance card / travel insurance info",
+    "toiletry bag (toothpaste, brush, deodorant, soap)",
+    "sunscreen",
+    "lip balm",
+    "underwear",
+    "socks",
+    "rain jacket or windbreaker",
+    "water bottle",
     "snacks",
-    "zonnebril",
-    "oorstopjes",
-    "slaapmasker",
-    "herbruikbare tas",
-    "zakdoekjes of toiletpapier"
+    "sunglasses",
+    "earplugs",
+    "sleep mask",
+    "reusable bag",
+    "tissues or toilet paper"
   ]
-}
+}

packing_templates_self_supported_offgrid_expanded_dutch_original.json

@@ -0,0 +1,696 @@
+{
+  "hut trek (summer)": [
+    "lichtgewicht trekkingrugzak (30\u201345 liter)",
+    "waterzak of drinkflessen",
+    "sneldrogende kleding",
+    "regenjas",
+    "snacks/energierepen",
+    "wandelschoenen",
+    "hoed of pet",
+    "zonnebrandcr\u00e8me",
+    "lakenzak",
+    "oordoppen"
+  ],
+  "hut trek (winter)": [
+    "slaapzak (lichte, warme variant)",
+    "lakenzak",
+    "binnenkleding voor hut (thermo / fleece)",
+    "hutpantoffels / Crocs",
+    "oordoppen",
+    "snacks voor onderweg",
+    "contant geld voor hutbetalingen"
+  ],
+  "camping trip (wild camping)": [
+    "tent of tarp",
+    "slaapmat",
+    "slaapzak",
+    "hoofdlamp",
+    "mes of multitool",
+    "waterfilter",
+    "kookset + brander",
+    "voedsel en snacks",
+    "aansteker",
+    "toilettas met biologisch afbreekbare zeep"
+  ],
+  "camping trip (campground)": [
+    "tent",
+    "kussen",
+    "stoeltje",
+    "kampeertafel (optioneel)",
+    "lamp of lantaarn",
+    "verlengsnoer (voor stroomcamping)",
+    "koelbox",
+    "servies & bestek",
+    "afwasspullen",
+    "boodschappen"
+  ],
+  "skitocht / skitour": [
+    "touringski's of splitboard",
+    "stijgvellen",
+    "lawinepieper (transceiver)",
+    "schep",
+    "sonde",
+    "gore-tex jas en broek",
+    "donsjas of warme isolatielaag",
+    "helm",
+    "handschoenen (2 paar)",
+    "ski- of zonnebril",
+    "thermosfles",
+    "sneldrogende baselayers",
+    "gamaschen (bij diepe sneeuw)",
+    "tourrugzak met ski-bevestiging",
+    "laagjes voor temperatuurregeling",
+    "energierepen of sportvoeding",
+    "GPS of offline kaarten",
+    "EHBO-set",
+    "lichte handschoenen voor het stijgen"
+  ],
+  "snowboard / splitboard trip": [
+    "splitboard of snowboard",
+    "lawinepieper",
+    "helm",
+    "dikke handschoenen",
+    "skibril",
+    "laagjes kleding",
+    "tourbindingen (voor splitboard)",
+    "stijgvellen",
+    "sonde en schep",
+    "lichte handschoenen voor het stijgen"
+  ],
+  "long-distance hike / thru-hike": [
+    "lichtgewicht rugzak",
+    "waterfilter",
+    "voedselvoorraad",
+    "navigatie (kaart/kompas/GPS)",
+    "first aid kit",
+    "trail runners of lichte wandelschoenen",
+    "zonbescherming",
+    "regenbescherming",
+    "natte doekjes",
+    "sneldrogende handdoek"
+  ],
+  "digital nomad trip": [
+    "laptop en oplader",
+    "wereldstekker",
+    "noise-cancelling koptelefoon",
+    "wifi-hotspot of lokale simkaart",
+    "powerbank",
+    "notitieboekje",
+    "lichte kleding",
+    "comfortabele rugzak of trolley",
+    "mifi-router of portable wifi-hotspot"
+  ],
+  "city trip": [
+    "dagrugzak",
+    "oplader voor telefoon",
+    "stadsplattegrond / offline maps",
+    "betaalkaart / contant geld",
+    "comfortabele schoenen",
+    "waterfles",
+    "reisgids of highlightslijst",
+    "lichte jas of regenjas"
+  ],
+  "road trip (car/camper)": [
+    "autopapieren",
+    "EHBO-kit",
+    "snacks en drinken",
+    "telefoonhouder / navigatie",
+    "kampeeruitrusting (indien overnachten)",
+    "reisgids of kaarten",
+    "zonnebril",
+    "stoel en tafel",
+    "vermaak (boek, muziek, spelletjes)"
+  ],
+  "festivaltrip": [
+    "tent",
+    "matje en slaapzak",
+    "oordoppen",
+    "poncho of regenjas",
+    "glitter / outfit",
+    "herbruikbare beker",
+    "zonnebrand",
+    "cash / pinpas",
+    "vriendenafspraken",
+    "snacks"
+  ],
+  "yoga / wellness retreat": [
+    "yogamat of yogahanddoek",
+    "comfortabele kleding",
+    "waterfles",
+    "dagboek / pen",
+    "warme trui of sjaal",
+    "sandalen",
+    "etherische olie (optioneel)",
+    "boek / meditatiemateriaal"
+  ],
+  "micro-adventure / weekend trip": [
+    "dagrugzak",
+    "snacks en drinken",
+    "regenjas",
+    "warme laag",
+    "kaart of GPS",
+    "toilettasje",
+    "hoofddeksel",
+    "compacte slaapuitrusting (indien overnachting)"
+  ],
+  "beach vacation": [
+    "zwemkleding",
+    "strandlaken",
+    "zonnebrandcr\u00e8me",
+    "zonnebril",
+    "hoed of pet",
+    "slippers",
+    "luchtige kleding",
+    "waterfles",
+    "strandtas",
+    "boek of e-reader"
+  ],
+  "cultural exploration": [
+    "dagrugzak",
+    "oplader voor telefoon",
+    "stadsplattegrond / offline maps",
+    "betaalkaart / contant geld",
+    "comfortabele schoenen",
+    "waterfles",
+    "reisgids of highlightslijst",
+    "lichte jas of regenjas"
+  ],
+  "nature escape": [
+    "dagrugzak",
+    "oplader voor telefoon",
+    "betaalkaart / contant geld",
+    "comfortabele schoenen",
+    "waterfles",
+    "downtime entertainment (e.g. book/ebook, games, laptop, journal)"
+  ],
+  "swimming": [
+    "badkleding",
+    "handdoek",
+    "zonnebrandcr\u00e8me",
+    "slippers",
+    "zwembril",
+    "waterfles"
+  ],
+  "going to the beach": [
+    "strandlaken",
+    "zwemkleding",
+    "zonnebrand",
+    "koeltas",
+    "strandstoel",
+    "zonnehoed"
+  ],
+  "relaxing": [
+    "comfortabele kleding",
+    "boek of e-reader",
+    "zitkussen of strandmat",
+    "muziek / koptelefoon"
+  ],
+  "sightseeing": [
+    "dagrugzak",
+    "waterfles",
+    "camera of smartphone",
+    "kaart of offline maps",
+    "zonnebril"
+  ],
+  "biking": [
+    "fiets of huurfiets",
+    "helm",
+    "sportkleding",
+    "fietslicht en slot",
+    "reparatieset"
+  ],
+  "running": [
+    "hardloopschoenen",
+    "sportkleding",
+    "zweetband of pet",
+    "waterfles of belt",
+    "sporthorloge (optioneel)"
+  ],
+  "skiing": [
+    "ski\u2019s en stokken",
+    "skischoenen",
+    "helm",
+    "skibril",
+    "skipas",
+    "dikke handschoenen",
+    "gore-tex kleding"
+  ],
+  "cross-country skiing": [
+    "langlaufski's en stokken",
+    "langlaufschoenen",
+    "ademende thermokleding",
+    "winddichte en waterafstotende buitenlaag",
+    "dunne handschoenen",
+    "muts of hoofdband",
+    "zonnebril of sportbril",
+    "buff of nekwarmer",
+    "lichte rugzak met water en snacks",
+    "EHBO-set"
+  ],
+  "ski touring": [
+    "algemene items voor deze situatie",
+    "extra kleding of uitrusting indien nodig"
+  ],
+  "hiking": [
+    "wandelschoenen of trailrunners",
+    "hiking sokken (anti-blaren)",
+    "hikingstokken",
+    "dagrugzak",
+    "regenjas of poncho",
+    "waterfles of waterzak",
+    "snacks / energierepen",
+    "zonnebrand en zonnebril",
+    "pet of hoed",
+    "blarenpleisters of tape",
+    "EHBO-set",
+    "navigatie (kaart, kompas of GPS)"
+  ],
+  "hut-to-hut hiking": [
+    "lichtgewicht trekkingrugzak (30\u201345 liter)",
+    "waterflessen of waterzak",
+    "snacks voor onderweg",
+    "regenjas",
+    "licht donsjack of warme laag",
+    "sneldrogende kleding",
+    "wandelschoenen",
+    "lakenzak",
+    "pantoffels of slippers voor hut",
+    "oordoppen",
+    "reserveringsbevestiging / contant geld voor hut"
+  ],
+  "rock climbing": [
+    "klimschoenen",
+    "klimgordel",
+    "zekeringsapparaat",
+    "chalk bag",
+    "helm"
+  ],
+  "ice climbing": [
+    "ijsbijlen",
+    "crampons",
+    "klimtouw",
+    "warme isolatielagen",
+    "klimgordel en helm"
+  ],
+  "snowshoe hiking": [
+    "algemene items voor deze situatie",
+    "extra kleding of uitrusting indien nodig"
+  ],
+  "kayaking / canoeing": [
+    "kayak of kano",
+    "peddel",
+    "reddingsvest",
+    "drybag",
+    "waterschoenen"
+  ],
+  "stand-up paddleboarding (SUP)": [
+    "SUP-board en peddel",
+    "reddingsvest",
+    "drybag",
+    "zonnebril met koord"
+  ],
+  "snorkeling": [
+    "snorkel en duikbril",
+    "zwemvliezen",
+    "badkleding",
+    "waterdichte tas"
+  ],
+  "scuba diving": [
+    "duikbril en snorkel",
+    "wetsuit",
+    "vinnen",
+    "duikcomputer",
+    "ademautomaat (indien eigen)"
+  ],
+  "surfing": [
+    "surfboard",
+    "wetsuit",
+    "wax",
+    "board leash",
+    "poncho of handdoek"
+  ],
+  "paragliding": [
+    "paraglider",
+    "helm",
+    "handschoenen",
+    "wandelschoenen",
+    "windjack"
+  ],
+  "horseback riding": [
+    "rijbroek",
+    "rijlaarzen of schoenen met hak",
+    "handschoenen",
+    "helm",
+    "eventueel eigen zadel of stijgbeugels"
+  ],
+  "photography": [
+    "camera + lenzen",
+    "statief",
+    "geheugenkaart(en)",
+    "extra accu's",
+    "lensdoekje"
+  ],
+  "fishing": [
+    "hengel",
+    "aas / kunstaas",
+    "visvergunning",
+    "laarzen of waadpak",
+    "koelbox (optioneel)"
+  ],
+  "rafting": [
+    "reddingsvest",
+    "waterdichte tas",
+    "waterschoenen",
+    "sneldrogende kleding",
+    "helm"
+  ],
+  "yoga": [
+    "algemene items voor deze situatie",
+    "extra kleding of uitrusting indien nodig"
+  ],
+  "cold destination / winter": [
+    "thermokleding",
+    "muts en handschoenen",
+    "warme jas",
+    "waterdichte schoenen",
+    "lippenbalsem"
+  ],
+  "warm destination / summer": [
+    "luchtige kleding",
+    "zonnebril",
+    "zonnecr\u00e8me",
+    "waterfles",
+    "hoed of pet"
+  ],
+  "variable weather / spring / autumn": [
+    "regenjas",
+    "laagjeskleding",
+    "warme trui",
+    "waterdichte schoenen"
+  ],
+  "tropical / humid": [
+    "lichtgewicht kleding",
+    "insectenspray",
+    "zonnecr\u00e8me",
+    "regenponcho",
+    "sneldrogende handdoek"
+  ],
+  "dry / desert-like": [
+    "zonnebril",
+    "zonnecr\u00e8me",
+    "hoofdbescherming",
+    "lichte lange mouwen",
+    "veel water"
+  ],
+  "ultralight": [
+    "lichtgewicht rugzak (< 1kg)",
+    "minimalistische shelter (tarp of tent)",
+    "lichtgewicht slaapmat",
+    "quilt of donsdeken",
+    "titanium kookset",
+    "beperkte kleding (laagjes!)",
+    "compacte regenjas",
+    "sneldrogende handdoek",
+    "tandenborstel (afgezaagd ;))"
+  ],
+  "lightweight (but comfortable)": [
+    "lichte tent of tarp",
+    "comfortabele slaapmat",
+    "lichtgewicht kookset",
+    "compact kledingpakket",
+    "sneldrogende handdoek"
+  ],
+  "luxury (including evening wear)": [
+    "nette schoenen",
+    "jurk of overhemd",
+    "parfum/deodorant",
+    "accessoires",
+    "reistoilettas met essentials"
+  ],
+  "minimalist": [
+    "1 set kleding voor elke situatie",
+    "compacte tandenborstel",
+    "alles-in-\u00e9\u00e9n zeep",
+    "lichtgewicht handdoek",
+    "kleine rugzak"
+  ],
+  "casual": [
+    "jeans of comfortabele broek",
+    "t-shirts",
+    "trui of hoodie",
+    "sneakers",
+    "zonnebril"
+  ],
+  "formal (business trip)": [
+    "overhemd / blouse",
+    "net jasje",
+    "nette schoenen",
+    "laptop en oplader",
+    "naamkaartjes / documenten"
+  ],
+  "conservative": [
+    "bedekkende kleding",
+    "sjaal of omslagdoek",
+    "lange broek of rok",
+    "gesloten schoenen"
+  ],
+  "outdoor": [
+    "regenjas",
+    "waterdichte schoenen",
+    "bivakzak of tarp",
+    "hoofdlamp",
+    "navigatie"
+  ],
+  "indoor": [
+    "pantoffels",
+    "comfortabele kleding",
+    "pyjama",
+    "oplader",
+    "toilettas"
+  ],
+  "huts with half board": [
+    "reserveringsbevestiging",
+    "lakenzak (vaak verplicht)",
+    "pantoffels of slippers voor binnen",
+    "lichte pyjama of slaapkleding",
+    "oorstopjes",
+    "waterfles",
+    "snacks voor onderweg",
+    "kleine toilettas",
+    "contant geld voor betalingen",
+    "lichte handdoek",
+    "zaklamp of hoofdlamp"
+  ],
+  "sleeping in a tent": [
+    "tent (1- of 2-persoons, afhankelijk van reis)",
+    "grondzeil",
+    "slaapmat",
+    "slaapzak (passend bij temperatuur)",
+    "hoofdlamp of zaklamp",
+    "sneldrogende handdoek",
+    "kussen of opblaasbaar hoofdkussen",
+    "oorstopjes",
+    "toilettas",
+    "powerbank",
+    "waterfles",
+    "boek of e-reader",
+    "EHBO-set"
+  ],
+  "sleeping in a car": [
+    "slaapmat die past in auto",
+    "warme slaapzak",
+    "zonneschermen of verduistering",
+    "kussen",
+    "waterfles binnen handbereik",
+    "powerbank of 12V-lader",
+    "toilettas",
+    "snacks voor de nacht",
+    "thermische deken (voor koude nachten)"
+  ],
+  "own vehicle": [
+    "rijbewijs",
+    "autopapieren",
+    "EHBO-set",
+    "navigatie of smartphone",
+    "telefoonhouder"
+  ],
+  "no own vehicle": [
+    "rugzak",
+    "waterfles",
+    "lichte schoenen",
+    "openbaar vervoer app of ticket"
+  ],
+  "off-grid / no electricity": [
+    "powerbank (minstens 10.000 mAh)",
+    "zonnepaneel of draagbaar laadsysteem",
+    "hoofdlamp + extra batterijen",
+    "oplaadbare batterijen en oplader",
+    "back-up verlichting (bijv. kleine zaklamp)",
+    "papieren kaart en kompas",
+    "notitieboekje + pen",
+    "noodcommunicatie (bijv. GPS beacon of satellietboodschapper)",
+    "opvouwbaar zonnepaneel (indien langere tochten)",
+    "navigatieapparaat met offline kaarten",
+    "extra opladerkabels",
+    "USB-hub (voor meerdere devices)",
+    "verpakking om elektronica droog te houden"
+  ],
+  "self-supported (bring your own cooking gear)": [
+    "lichtgewicht kooktoestel (gas, benzine of alcohol)",
+    "brandstof (voldoende voor aantal dagen)",
+    "pan of keteltje",
+    "spork of lepel",
+    "opvouwbaar snijplankje (optioneel)",
+    "aandrukbare kom of beker",
+    "aansteker + lucifers (waterdicht verpakt)",
+    "gedroogde of gevriesdroogde maaltijden",
+    "snacks / noodbars",
+    "afwasmiddel (biologisch afbreekbaar) + sponsje",
+    "opslagzakken voor afval",
+    "waterfilter of -pomp",
+    "chloordruppels of zuiveringstabletten",
+    "minstens 2 liter wateropslag per persoon",
+    "food bag of hangzak voor voedsel (wild-safe)"
+  ],
+  "travel with children": [
+    "snacks en speelgoed",
+    "EHBO-set met pleisters",
+    "extra kleding",
+    "kinderdrager of buggy",
+    "zonbescherming",
+    "extra snacks voor kinderen",
+    "favoriet speelgoed of knuffel",
+    "reisstoel of draagzak",
+    "natte doekjes",
+    "luiers of potje (afhankelijk van leeftijd)",
+    "extra set kleding per dag",
+    "reisapotheek voor kinderen",
+    "kinderzonnebrand",
+    "activiteitenboekje of tablet met filmpjes",
+    "snelle snacks voor onderweg",
+    "kinder slaapzak of slaapmat",
+    "reisbedje of matrasje (voor jonge kinderen)",
+    "luiers of oefenbroekjes",
+    "flesjes en voeding (indien van toepassing)",
+    "babyfoon (voor verblijf op locatie)",
+    "speen of fopspeen",
+    "kinder EHBO-set (inclusief thermometer)",
+    "zonnehoedje of muts",
+    "regenhoes voor kinderwagen of draagzak",
+    "hydraterende cr\u00e8me (voor gevoelige huid)"
+  ],
+  "pet-friendly": [
+    "voer en waterbak",
+    "hondenriem of tuigje",
+    "poepzakjes",
+    "reismatje of deken",
+    "vaccinatieboekje"
+  ],
+  "rainy climate": [
+    "regenjas of poncho",
+    "waterdichte rugzakhoes",
+    "sneldrogende kleding",
+    "rubberen schoenen"
+  ],
+  "snow and ice": [
+    "warme laarzen",
+    "dikke handschoenen",
+    "sneeuwbril",
+    "thermokleding",
+    "gripzolen / spikes"
+  ],
+  "high alpine terrain": [
+    "wandelstokken",
+    "kaart en kompas",
+    "wind- en waterdichte jas",
+    "extra voeding",
+    "EHBO-kit"
+  ],
+  "snow, ice and avalanche-prone terrain": [
+    "lawinepieper",
+    "schep",
+    "sonde",
+    "airbag rugzak (indien beschikbaar)",
+    "kennis van lawineveiligheid / cursus",
+    "kaart en kompas / GPS",
+    "partner check voor vertrek"
+  ],
+  "no special conditions": [
+  ],
+  "1 day": [
+    "ondergoed per dag",
+    "sokken per dag",
+    "toilettas",
+    "extra kledinglaag",
+    "aantal maaltijden/snacks afgestemd op duur"
+  ],
+  "2 days": [
+    "ondergoed per dag",
+    "sokken per dag",
+    "toilettas",
+    "extra kledinglaag",
+    "aantal maaltijden/snacks afgestemd op duur"
+  ],
+  "3 days": [
+    "ondergoed per dag",
+    "sokken per dag",
+    "toilettas",
+    "extra kledinglaag",
+    "aantal maaltijden/snacks afgestemd op duur"
+  ],
+  "4 days": [
+    "ondergoed per dag",
+    "sokken per dag",
+    "toilettas",
+    "extra kledinglaag",
+    "aantal maaltijden/snacks afgestemd op duur"
+  ],
+  "5 days": [
+    "ondergoed per dag",
+    "sokken per dag",
+    "toilettas",
+    "extra kledinglaag",
+    "aantal maaltijden/snacks afgestemd op duur"
+  ],
+  "6 days": [
+    "ondergoed per dag",
+    "sokken per dag",
+    "toilettas",
+    "extra kledinglaag",
+    "aantal maaltijden/snacks afgestemd op duur"
+  ],
+  "7 days": [
+    "ondergoed per dag",
+    "sokken per dag",
+    "toilettas",
+    "extra kledinglaag",
+    "aantal maaltijden/snacks afgestemd op duur"
+  ],
+  "7+ days": [
+    "ondergoed per dag",
+    "sokken per dag",
+    "toilettas",
+    "extra kledinglaag",
+    "aantal maaltijden/snacks afgestemd op duur"
+  ],
+  "default_items": [
+    "telefoon + oplader",
+    "powerbank",
+    "identiteitsbewijs of paspoort",
+    "betaalmiddelen (pinpas / contant geld)",
+    "verzekeringspas / reisverzekering-info",
+    "toilettas (tandpasta, borstel, deodorant, zeep)",
+    "zonnebrandcr\u00e8me",
+    "lippenbalsem",
+    "ondergoed",
+    "sokken",
+    "regenjas of windjack",
+    "waterfles",
+    "snacks",
+    "zonnebril",
+    "oorstopjes",
+    "slaapmasker",
+    "herbruikbare tas",
+    "zakdoekjes of toiletpapier"
+  ]
+}

space/.ipynb_checkpoints/app-checkpoint.py

@@ -13,15 +13,14 @@ keys_list = list(candidate_labels.keys())
 with open("test_data.json", "r") as file:
     packing_data = json.load(file)
 
-# function and gradio app
-model_name = "facebook/bart-large-mnli"
-classifier = pipeline("zero-shot-classification", model=model_name)
-cut_off = 0.5  # used to choose which activities are relevant
+# Load packing item data
+with open("packing_templates_self_supported_offgrid_expanded.json", "r") as file:
+    packing_items = json.load(file)
 
-def classify(#model_name, 
-             trip_descr, cut_off):
-    
-    # Create an empty DataFrame with specified columns
+# function and gradio app
+def classify(model_name, trip_descr, cut_off = 0.5):
+    classifier = pipeline("zero-shot-classification", model=model_name)
+    ## Create and fill dataframe with class predictions
     df = pd.DataFrame(columns=['superclass', 'pred_class'])
     for i, key in enumerate(keys_list):
         if key == 'activities':
@@ -32,16 +31,31 @@ def classify(#model_name,
             result = classifier(trip_descr, candidate_labels[key])
             classes = result["labels"][0]
         df.loc[i] = [key, classes]
-    return df
+
+    ## Look up and return list of items to pack based on class predictions
+    # make list from dataframe column
+    all_classes = [elem for x in df["pred_class"] for elem in (x if isinstance(x, list) else [x])]
+    # look up packing items for each class/key
+    list_of_list_of_items = [packing_items.get(k, []) for k in all_classes]
+    # combine lists and remove doubble entries
+    flat_unique = []
+    for sublist in list_of_list_of_items:
+        for item in sublist:
+            if item not in flat_unique:
+                flat_unique.append(item)
+    # sort alphabetically to notice duplicates
+    sorted_list = sorted(flat_unique)  
+    return df, "\n".join(sorted_list)
 
 demo = gr.Interface(
     fn=classify,
     inputs=[
-        #gr.Textbox(label="Model name", value = "facebook/bart-large-mnli"),
+        gr.Textbox(label="Model name", value = "facebook/bart-large-mnli"),
         gr.Textbox(label="Trip description"),
         gr.Number(label="Activity cut-off", value = 0.5),
     ],
-    outputs="dataframe",
+    # outputs="dataframe",
+    outputs=[gr.Dataframe(label="DataFrame"), gr.Textbox(label="List of words")],
     title="Trip classification",
     description="Enter a text describing your trip",
 )

space/.ipynb_checkpoints/labels-checkpoint.txt

@@ -0,0 +1,93 @@
+sunscreen
+sunglasses
+sunhat
+flip-flops
+swimsuit
+beach towel
+waterproof phone case
+beach bag
+snorkel gear
+tent
+sleeping bag
+camping stove
+flashlight
+hiking boots
+water filter
+compass
+first aid kit
+bug spray
+multi-tool
+thermal clothing
+ski jacket
+ski goggles
+snow boots
+gloves
+hand warmers
+beanie
+lip balm
+snowboard
+base layers
+passport
+visa documents
+travel adapter
+currency
+sim card
+travel pillow
+neck wallet
+travel insurance documents
+power bank
+laptop
+business attire
+dress shoes
+charging cables
+pen
+headphones
+lightweight backpack
+travel-sized toiletries
+packable rain jacket
+dry bag
+trekking poles
+hostel lock
+quick-dry towel
+travel journal
+snacks
+blanket
+emergency roadside kit
+reusable coffee mug
+reusable shopping bags
+earplugs
+fanny pack
+poncho
+bandana
+comfortable shoes
+bathing suit
+sandals
+light jacket
+entertainment for downtime (e.g. book/ebook, games, laptop, journal)
+short pants/skirts
+t-shirts/tops
+thin scarf
+pants
+shirts
+cardigan/sweater
+gifts
+winter shoes
+long pants
+short pants
+malaria medication
+mosquito repellant
+local currency
+wallet
+tickets
+phone and charger
+painkiller
+necessary medication
+personal toiletries (e.g. toothbrush, toothpaste)
+underwear
+socks
+sleep wear
+snacks for the journey
+refillable water bottle
+day pack
+big backpack/suitcase
+

space/app.py

@@ -13,15 +13,14 @@ keys_list = list(candidate_labels.keys())
 with open("test_data.json", "r") as file:
     packing_data = json.load(file)
 
-# function and gradio app
-model_name = "facebook/bart-large-mnli"
-classifier = pipeline("zero-shot-classification", model=model_name)
-cut_off = 0.5  # used to choose which activities are relevant
+# Load packing item data
+with open("packing_templates_self_supported_offgrid_expanded.json", "r") as file:
+    packing_items = json.load(file)
 
-def classify(#model_name, 
-             trip_descr, cut_off):
-    
-    # Create an empty DataFrame with specified columns
+# function and gradio app
+def classify(model_name, trip_descr, cut_off = 0.5):
+    classifier = pipeline("zero-shot-classification", model=model_name)
+    ## Create and fill dataframe with class predictions
     df = pd.DataFrame(columns=['superclass', 'pred_class'])
     for i, key in enumerate(keys_list):
         if key == 'activities':
@@ -32,16 +31,31 @@ def classify(#model_name,
             result = classifier(trip_descr, candidate_labels[key])
             classes = result["labels"][0]
         df.loc[i] = [key, classes]
-    return df
+
+    ## Look up and return list of items to pack based on class predictions
+    # make list from dataframe column
+    all_classes = [elem for x in df["pred_class"] for elem in (x if isinstance(x, list) else [x])]
+    # look up packing items for each class/key
+    list_of_list_of_items = [packing_items.get(k, []) for k in all_classes]
+    # combine lists and remove doubble entries
+    flat_unique = []
+    for sublist in list_of_list_of_items:
+        for item in sublist:
+            if item not in flat_unique:
+                flat_unique.append(item)
+    # sort alphabetically to notice duplicates
+    sorted_list = sorted(flat_unique)  
+    return df, "\n".join(sorted_list)
 
 demo = gr.Interface(
     fn=classify,
     inputs=[
-        #gr.Textbox(label="Model name", value = "facebook/bart-large-mnli"),
+        gr.Textbox(label="Model name", value = "facebook/bart-large-mnli"),
         gr.Textbox(label="Trip description"),
         gr.Number(label="Activity cut-off", value = 0.5),
     ],
-    outputs="dataframe",
+    # outputs="dataframe",
+    outputs=[gr.Dataframe(label="DataFrame"), gr.Textbox(label="List of words")],
     title="Trip classification",
     description="Enter a text describing your trip",
 )

space/blog_post/article.md

@@ -0,0 +1,45 @@
+# Working with Large Language Models
+## Introduction
+* What are LLMs and why are they popular
+* How to use the open source platform to use LLMs for own application; example of creating a packing list.
+
+## Why bother to adept models to your application
+* Problems of LLMS: Hallucination and wrong outputs
+* Controll outputs by using zero-shot-calssification
+  * briefly mention other types of classification
+* How we do it with our packing list model
+  * Why not use packing items as classes
+  * Use superclasses to categories trip and have packing items correspond to superclasses
+  * Asses performance with small test data set
+  * mention gradio app to make it user friendly and spaces to share model
+
+## Implementation of packing list model
+### Prerequisites before you start to code
+* Hugging face account also use access token, mention api
+* Use anaconda
+* Install python and libraries 
+* create jupyter notebook
+* pip install stuff
+
+### Predefine outputs/classes
+
+### Model implementation
+
+### Using gradio app
+
+### Share your model
+* created space
+* created github access token on huffing face and then github actions workflow
+* you need to install the required dependencies in the environment where your Hugging Face Space is running.
+* create requirements.txt file with necessary libraries
+
+
+## Performance assessment
+* Test data creation
+
+## Side notes
+* Hugging face API (or mention during implementation)
+
+## Closing
+* Summary
+* Limitations

space/main_model.ipynb

@@ -5,7 +5,7 @@
    "id": "e25090fa-f990-4f1a-84f3-b12159eedae8",
    "metadata": {},
    "source": [
-    "# Implement and test prediciton model"
+    "# Working with a Large Language Model (LLM)"
    ]
   },
   {
@@ -18,41 +18,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "f8c28d2d-8458-49fd-8ebf-5e729d6e861f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Prerequisites\n",
-    "from tabulate import tabulate\n",
-    "from transformers import pipeline\n",
+    "import math\n",
     "import json\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
     "import pickle\n",
     "import os\n",
     "import time\n",
-    "import math\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tabulate import tabulate\n",
+    "from transformers import pipeline\n",
     "\n",
-    "# get candidate labels\n",
+    "# Get candidate labels\n",
     "with open(\"packing_label_structure.json\", \"r\") as file:\n",
     "    candidate_labels = json.load(file)\n",
     "keys_list = list(candidate_labels.keys())\n",
     "\n",
-    "# Load test data (in list of dictionaries)\n",
+    "# Load test data (list of dictionaries)\n",
     "with open(\"test_data.json\", \"r\") as file:\n",
     "    packing_data = json.load(file)\n",
-    "# Extract all trip descriptions and trip_types\n",
+    "# Extract trip descriptions and classification (trip_types)\n",
     "trip_descriptions = [trip['description'] for trip in packing_data]\n",
-    "trip_types = [trip['trip_types'] for trip in packing_data]\n",
-    "\n",
-    "# Access the first trip description\n",
-    "first_trip = trip_descriptions[0]\n",
-    "# Get the packing list for the secondfirst trip\n",
-    "first_trip_type = trip_types[0]\n",
-    "\n",
-    "# print(f\"First trip: {first_trip} \\n\")\n",
-    "# print(f\"Trip type: {first_trip_type}\")"
+    "trip_types = [trip['trip_types'] for trip in packing_data]"
    ]
   },
   {
@@ -229,20 +220,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "fac51224-9575-4b4b-8567-4ad4e759ecc9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# function that returns pandas data frame with predictions and true values\n",
-    "\n",
-    "cut_off = 0.5  # used to choose which activities are relevant\n",
+    "def pred_trip(model_name, trip_descr, trip_type, cut_off = 0.5):\n",
+    "    \"\"\"\n",
+    "    Classifies trip\n",
+    "    \n",
+    "    Parameters:\n",
+    "    model_name: name of hugging-face model\n",
+    "    trip_descr: text describing the trip\n",
+    "    trip_type: true trip classification\n",
+    "    cut_off: cut_off for choosing activities\n",
     "\n",
-    "def pred_trip(model_name, trip_descr, trip_type, cut_off):\n",
+    "    Returns:\n",
+    "    pd Dataframe: with class predictions and true values\n",
+    "    \"\"\"\n",
+    "    \n",
     "    classifier = pipeline(\"zero-shot-classification\", model=model_name)\n",
-    "    # Create an empty DataFrame with specified columns\n",
     "    df = pd.DataFrame(columns=['superclass', 'pred_class'])\n",
     "    for i, key in enumerate(keys_list):\n",
+    "        print(i)\n",
     "        if key == 'activities':\n",
     "            result = classifier(trip_descr, candidate_labels[key], multi_label=True)\n",
     "            indices = [i for i, score in enumerate(result['scores']) if score > cut_off]\n",
@@ -250,9 +250,6 @@
     "        else:\n",
     "            result = classifier(trip_descr, candidate_labels[key])\n",
     "            classes = result[\"labels\"][0]\n",
-    "        print(result)\n",
-    "        print(classes)\n",
-    "        print(i)\n",
     "        df.loc[i] = [key, classes]\n",
     "    df['true_class'] = trip_type\n",
     "    return df"
@@ -260,14 +257,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "b36ab806-2f35-4950-ac5a-7c192190cba7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# function for accuracy, perc true classes identified and perc wrong pred classes\n",
-    "\n",
     "def perf_measure(df):\n",
+    "    \"\"\"\n",
+    "    Calculates performance measures:\n",
+    "    Accuracy of classification excluding activities superclass\n",
+    "    Percentage of correctly identified activities (#correctly predicted/#true activities)\n",
+    "    Percentage of wrongly identified activities (#wrongly predicted/#predicted activities)\n",
+    "\n",
+    "    Parameters:\n",
+    "    df: pd Dataframe returned from pred_trip()\n",
+    "\n",
+    "    Returns:\n",
+    "    pd Dataframe: containing performance measures\n",
+    "    \"\"\"\n",
+    "    \n",
     "    df['same_value'] = df['pred_class'] == df['true_class']\n",
     "    correct = sum(df.loc[df.index != 1, 'same_value'])\n",
     "    total = len(df['same_value'])\n",
@@ -365,10 +373,10 @@
     "model_names = [\n",
     "    \"facebook/bart-large-mnli\",\n",
     "    \"MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli\",\n",
-    "    ##\"cross-encoder/nli-deberta-v3-base\",\n",
+    "    \"cross-encoder/nli-deberta-v3-base\",\n",
     "    \"cross-encoder/nli-deberta-v3-large\",\n",
     "    \"MoritzLaurer/mDeBERTa-v3-base-mnli-xnli\",\n",
-    "    ##\"joeddav/bart-large-mnli-yahoo-answers\",\n",
+    "    \"joeddav/bart-large-mnli-yahoo-answers\",\n",
     "    \"MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli\",\n",
     "    \"MoritzLaurer/deberta-v3-large-zeroshot-v2.0\",\n",
     "    \"valhalla/distilbart-mnli-12-1\",\n",
@@ -386,31 +394,24 @@
     "        current_trip = trip_descriptions[i]\n",
     "        current_type = trip_types[i]\n",
     "        df = pred_trip(model_name, current_trip, current_type, cut_off = 0.5)\n",
-    "        print(df)\n",
-    "        # accuracy, perc true classes identified and perc wrong pred classes\n",
     "        performance = pd.concat([performance, perf_measure(df)])\n",
-    "        print(performance)\n",
-    "        \n",
     "        result_list.append(df)\n",
     "    end_time = time.time()\n",
     "    elapsed_time = end_time - start_time\n",
-    "    # Extract \"same_value\" column from each DataFrame\n",
-    "    sv_columns = [df['same_value'] for df in result_list]  # 'same' needs to be changed\n",
+    "    \n",
+    "    # Extract and combine columns identifying correct prediction (for each trip)\n",
+    "    sv_columns = [df['same_value'] for df in result_list]\n",
     "    sv_columns.insert(0, result_list[0]['superclass'])\n",
-    "    # Combine into a new DataFrame (columns side-by-side)\n",
     "    sv_df = pd.concat(sv_columns, axis=1)\n",
-    "    print(sv_df)\n",
-    "    # Compute accuracy per superclass (row means of same_value matrix excluding the first column)\n",
+    "    # Compute accuracy per superclass\n",
     "    row_means = sv_df.iloc[:, 1:].mean(axis=1)\n",
     "    df_row_means = pd.DataFrame({\n",
     "        'superclass': sv_df['superclass'],\n",
     "        'accuracy': row_means\n",
     "    })\n",
-    "    print(df_row_means)\n",
     "    # Compute performance measures per trip (mean for each column of performance table)\n",
     "    column_means = performance.mean()\n",
-    "    print(column_means)\n",
-    "    # save results\n",
+    "    # Save results\n",
     "    model = model_name.replace(\"/\", \"-\")\n",
     "    model_result = {\n",
     "        'model': model,\n",
@@ -420,9 +421,7 @@
     "        'perf_superclass': df_row_means,\n",
     "        'elapsed_time': elapsed_time\n",
     "    }\n",
-    "    # File path with folder\n",
     "    filename = os.path.join('results', f'{model}_results.pkl')\n",
-    "    # Save the object\n",
     "    with open(filename, 'wb') as f:\n",
     "        pickle.dump(model_result, f)\n",
     "\n",
@@ -440,116 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "62ca82b0-6909-4e6c-9d2c-fed87971e5b6",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: cross-encoder-nli-deberta-v3-base\n",
-      "Performance Summary:\n",
-      "accuracy      0.444444\n",
-      "true_ident    0.533333\n",
-      "false_pred    0.712500\n",
-      "dtype: float64\n",
-      "----------------------------------------\n",
-      "Model: joeddav-bart-large-mnli-yahoo-answers\n",
-      "Performance Summary:\n",
-      "accuracy      0.344444\n",
-      "true_ident    0.650000\n",
-      "false_pred    0.553792\n",
-      "dtype: float64\n",
-      "----------------------------------------\n",
-      "Model: cross-encoder-nli-deberta-v3-large\n",
-      "Performance Summary:\n",
-      "accuracy      0.466667\n",
-      "true_ident    0.566667\n",
-      "false_pred    0.541667\n",
-      "dtype: float64\n",
-      "----------------------------------------\n",
-      "Model: MoritzLaurer-DeBERTa-v3-large-mnli-fever-anli-ling-wanli\n",
-      "Performance Summary:\n",
-      "accuracy      0.566667\n",
-      "true_ident    0.841667\n",
-      "false_pred    0.546667\n",
-      "dtype: float64\n",
-      "----------------------------------------\n",
-      "Model: MoritzLaurer-mDeBERTa-v3-base-mnli-xnli\n",
-      "Performance Summary:\n",
-      "accuracy      0.466667\n",
-      "true_ident    0.408333\n",
-      "false_pred    0.481250\n",
-      "dtype: float64\n",
-      "----------------------------------------\n",
-      "Model: MoritzLaurer-deberta-v3-large-zeroshot-v2.0\n",
-      "Performance Summary:\n",
-      "accuracy      0.455556\n",
-      "true_ident    0.325000\n",
-      "false_pred    0.500000\n",
-      "dtype: float64\n",
-      "----------------------------------------\n",
-      "Model: facebook-bart-large-mnli\n",
-      "Performance Summary:\n",
-      "accuracy      0.466667\n",
-      "true_ident    0.708333\n",
-      "false_pred    0.400000\n",
-      "dtype: float64\n",
-      "----------------------------------------\n",
-      "Model: valhalla-distilbart-mnli-12-1\n",
-      "Performance Summary:\n",
-      "accuracy      0.522222\n",
-      "true_ident    0.300000\n",
-      "false_pred    0.533333\n",
-      "dtype: float64\n",
-      "----------------------------------------\n",
-      "Model: MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\n",
-      "Performance Summary:\n",
-      "accuracy      0.522222\n",
-      "true_ident    0.841667\n",
-      "false_pred    0.572381\n",
-      "dtype: float64\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Folder where .pkl files are saved\n",
-    "results_dir = 'results/before'\n",
-    "\n",
-    "# Dictionary to store all loaded results\n",
-    "all_results = {}\n",
-    "\n",
-    "# Loop through all .pkl files in the folder\n",
-    "for filename in os.listdir(results_dir):\n",
-    "    if filename.endswith('.pkl'):\n",
-    "        model_name = filename.replace('_results.pkl', '')  # Extract model name\n",
-    "        file_path = os.path.join(results_dir, filename)\n",
-    "        \n",
-    "        # Load the result\n",
-    "        with open(file_path, 'rb') as f:\n",
-    "            result = pickle.load(f)\n",
-    "            all_results[model_name] = result\n",
-    "\n",
-    "# Compare performance across models\n",
-    "for model, data in all_results.items():\n",
-    "    print(f\"Model: {model}\")\n",
-    "    print(f\"Performance Summary:\\n{data['perf_summary']}\")\n",
-    "    print(\"-\" * 40)\n",
-    "\n",
-    "# Compare performance across models\n",
-    "#for model, data in all_results.items():\n",
-    "#    print(f\"Model: {model}\")\n",
-    "#    print(f\"Performance Summary:\\n{data['perf_superclass']}\")\n",
-    "#    print(\"-\" * 40)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "37849e0b-864e-4377-b06c-0ac70c3861f9",
    "metadata": {
     "scrolled": true
@@ -701,6 +591,8 @@
    ],
    "source": [
     "def get_difficult_trips(model_result, cut_off = 0.6):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
     "    # model_result is a dict with dict_keys(['model', 'predictions', \n",
     "    # 'performance', 'perf_summary', 'perf_superclass', 'elapsed_time'])\n",
     "    # get performance dataframe and repair index\n",
@@ -808,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "adb491b1-3ac3-4c32-934f-5eb6171f2ec9",
    "metadata": {},
    "outputs": [
@@ -904,26 +796,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 10,
    "id": "b020f584-1468-4c84-9dac-7ca7fac6e8ca",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "[]\n",
-      "<class 'list'>\n",
-      "   accuracy true_ident false_pred  \\\n",
-      "0  0.444444   0.533333     0.7125   \n",
-      "1  0.355556       0.65   0.553792   \n",
-      "2  0.466667   0.566667   0.541667   \n",
-      "3  0.611111   0.841667   0.546667   \n",
-      "4  0.455556   0.408333    0.48125   \n",
-      "5       0.5      0.325        0.5   \n",
-      "6  0.466667   0.708333        0.4   \n",
-      "7       0.5        0.3   0.533333   \n",
-      "8  0.522222   0.841667   0.572381   \n",
+      "   accuracy  true_ident  false_pred  \\\n",
+      "0  0.444444    0.533333    0.712500   \n",
+      "1  0.355556    0.650000    0.553792   \n",
+      "2  0.466667    0.566667    0.541667   \n",
+      "3  0.611111    0.841667    0.546667   \n",
+      "4  0.455556    0.408333    0.481250   \n",
+      "5  0.500000    0.325000    0.500000   \n",
+      "6  0.466667    0.708333    0.400000   \n",
+      "7  0.500000    0.300000    0.533333   \n",
+      "8  0.522222    0.841667    0.572381   \n",
       "\n",
       "                                               model  \n",
       "0                  cross-encoder-nli-deberta-v3-base  \n",
@@ -968,20 +861,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
+   "id": "222a70fc-8d3c-4ebb-9954-d5c72baed9e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# return packing list additionally to classes\n",
+    "# Load packing item data\n",
+    "with open(\"packing_templates_self_supported_offgrid_expanded.json\", \"r\") as file:\n",
+    "    packing_items = json.load(file)\n",
+    "\n",
+    "# function and gradio app\n",
+    "def classify(model_name, trip_descr, cut_off = 0.5):\n",
+    "    classifier = pipeline(\"zero-shot-classification\", model=model_name)\n",
+    "    ## Create and fill dataframe with class predictions\n",
+    "    df = pd.DataFrame(columns=['superclass', 'pred_class'])\n",
+    "    for i, key in enumerate(keys_list):\n",
+    "        if key == 'activities':\n",
+    "            result = classifier(trip_descr, candidate_labels[key], multi_label=True)\n",
+    "            indices = [i for i, score in enumerate(result['scores']) if score > cut_off]\n",
+    "            classes = [result['labels'][i] for i in indices]\n",
+    "        else:\n",
+    "            result = classifier(trip_descr, candidate_labels[key])\n",
+    "            classes = result[\"labels\"][0]\n",
+    "        df.loc[i] = [key, classes]\n",
+    "\n",
+    "    ## Look up and return list of items to pack based on class predictions\n",
+    "    # make list from dataframe column\n",
+    "    all_classes = [elem for x in df[\"pred_class\"] for elem in (x if isinstance(x, list) else [x])]\n",
+    "    # look up packing items for each class/key\n",
+    "    list_of_list_of_items = [packing_items.get(k, []) for k in all_classes]\n",
+    "    # combine lists and remove doubble entries\n",
+    "    flat_unique = []\n",
+    "    for sublist in list_of_list_of_items:\n",
+    "        for item in sublist:\n",
+    "            if item not in flat_unique:\n",
+    "                flat_unique.append(item)\n",
+    "    # sort alphabetically to notice duplicates\n",
+    "    sorted_list = sorted(flat_unique)  \n",
+    "    return df, sorted_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
    "id": "0f7376bd-a50b-47cc-8055-48a6de5dfee6",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<class 'dict'>\n"
+      "(           superclass                              pred_class\n",
+      "0       activity_type                          beach vacation\n",
+      "1          activities  [going to the beach, relaxing, hiking]\n",
+      "2   climate_or_season               warm destination / summer\n",
+      "3    style_or_comfort                              minimalist\n",
+      "4          dress_code                                  casual\n",
+      "5       accommodation                    huts with half board\n",
+      "6      transportation                          no own vehicle\n",
+      "7  special_conditions               off-grid / no electricity\n",
+      "8    trip_length_days                                 7+ days, ['1 set kleding voor elke situatie', 'EHBO-set', 'USB-hub (voor meerdere devices)', 'aantal maaltijden/snacks afgestemd op duur', 'alles-in-één zeep', 'back-up verlichting (bijv. kleine zaklamp)', 'blarenpleisters of tape', 'boek of e-reader', 'comfortabele kleding', 'compacte tandenborstel', 'contant geld voor betalingen', 'dagrugzak', 'extra kledinglaag', 'extra opladerkabels', 'hiking sokken (anti-blaren)', 'hikingstokken', 'hoed of pet', 'hoofdlamp + extra batterijen', 'jeans of comfortabele broek', 'kleine rugzak', 'kleine toilettas', 'koeltas', 'lakenzak (vaak verplicht)', 'lichte handdoek', 'lichte pyjama of slaapkleding', 'lichte schoenen', 'lichtgewicht handdoek', 'luchtige kleding', 'muziek / koptelefoon', 'navigatie (kaart, kompas of GPS)', 'navigatieapparaat met offline kaarten', 'noodcommunicatie (bijv. GPS beacon of satellietboodschapper)', 'notitieboekje + pen', 'ondergoed per dag', 'oorstopjes', 'openbaar vervoer app of ticket', 'oplaadbare batterijen en oplader', 'opvouwbaar zonnepaneel (indien langere tochten)', 'pantoffels of slippers voor binnen', 'papieren kaart en kompas', 'pet of hoed', 'powerbank (minstens 10.000 mAh)', 'regenjas of poncho', 'reserveringsbevestiging', 'rugzak', 'slippers', 'snacks / energierepen', 'snacks voor onderweg', 'sneakers', 'sokken per dag', 'strandlaken', 'strandstoel', 'strandtas', 't-shirts', 'toilettas', 'trui of hoodie', 'verpakking om elektronica droog te houden', 'wandelschoenen of trailrunners', 'waterfles', 'waterfles of waterzak', 'zaklamp of hoofdlamp', 'zitkussen of strandmat', 'zonnebrand', 'zonnebrand en zonnebril', 'zonnebrandcrème', 'zonnebril', 'zonnecrème', 'zonnehoed', 'zonnepaneel of draagbaar laadsysteem', 'zwemkleding'])\n"
      ]
     }
    ],
    "source": [
-    "print(type(all_results))"
+    "# Access the first trip description\n",
+    "first_trip = trip_descriptions[0]\n",
+    "tmp = classify(\"facebook/bart-large-mnli\", first_trip )\n",
+    "print(tmp)"
    ]
   },
   {
@@ -1013,9 +968,10 @@
     "# Load test data (in list of dictionaries)\n",
     "with open(\"test_data.json\", \"r\") as file:\n",
     "    packing_data = json.load(file)\n",
-    "# Extract all trip descriptions and trip_types\n",
-    "# trip_descriptions = [trip['description'] for trip in packing_data]\n",
-    "# trip_types = [trip['trip_types'] for trip in packing_data]"
+    "\n",
+    "# Load packing item data\n",
+    "with open(\"packing_templates_self_supported_offgrid_expanded.json\", \"r\") as file:\n",
+    "    packing_items = json.load(file)"
    ]
   },
   {
@@ -1029,15 +985,14 @@
      "output_type": "stream",
      "text": [
       "Running on local URL:  http://127.0.0.1:7860\n",
-      "Running on public URL: https://91493051ab28db8a5a.gradio.live\n",
       "\n",
-      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+      "To create a public link, set `share=True` in `launch()`.\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"https://91493051ab28db8a5a.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1056,11 +1011,9 @@
    ],
    "source": [
     "# function and gradio app\n",
-    "cut_off = 0.5  # used to choose which activities are relevant\n",
-    "\n",
-    "def classify(model_name, trip_descr, cut_off):\n",
+    "def classify(model_name, trip_descr, cut_off = 0.5):\n",
     "    classifier = pipeline(\"zero-shot-classification\", model=model_name)\n",
-    "    # Create an empty DataFrame with specified columns\n",
+    "    ## Create and fill dataframe with class predictions\n",
     "    df = pd.DataFrame(columns=['superclass', 'pred_class'])\n",
     "    for i, key in enumerate(keys_list):\n",
     "        if key == 'activities':\n",
@@ -1071,7 +1024,21 @@
     "            result = classifier(trip_descr, candidate_labels[key])\n",
     "            classes = result[\"labels\"][0]\n",
     "        df.loc[i] = [key, classes]\n",
-    "    return df\n",
+    "\n",
+    "    ## Look up and return list of items to pack based on class predictions\n",
+    "    # make list from dataframe column\n",
+    "    all_classes = [elem for x in df[\"pred_class\"] for elem in (x if isinstance(x, list) else [x])]\n",
+    "    # look up packing items for each class/key\n",
+    "    list_of_list_of_items = [packing_items.get(k, []) for k in all_classes]\n",
+    "    # combine lists and remove doubble entries\n",
+    "    flat_unique = []\n",
+    "    for sublist in list_of_list_of_items:\n",
+    "        for item in sublist:\n",
+    "            if item not in flat_unique:\n",
+    "                flat_unique.append(item)\n",
+    "    # sort alphabetically to notice duplicates\n",
+    "    sorted_list = sorted(flat_unique)  \n",
+    "    return df, \"\\n\".join(sorted_list)\n",
     "\n",
     "demo = gr.Interface(\n",
     "    fn=classify,\n",
@@ -1080,15 +1047,49 @@
     "        gr.Textbox(label=\"Trip description\"),\n",
     "        gr.Number(label=\"Activity cut-off\", value = 0.5),\n",
     "    ],\n",
-    "    outputs=\"dataframe\",\n",
+    "    # outputs=\"dataframe\",\n",
+    "    outputs=[gr.Dataframe(label=\"DataFrame\"), gr.Textbox(label=\"List of words\")],\n",
     "    title=\"Trip classification\",\n",
     "    description=\"Enter a text describing your trip\",\n",
     ")\n",
     "\n",
     "# Launch the Gradio app\n",
     "if __name__ == \"__main__\":\n",
-    "    demo.launch(share=True)\n"
+    "    demo.launch()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "1f5df949-a527-4b11-8e5e-23786e1cde12",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(first_trip)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d74d4e7-80f6-44d3-a5c7-b41025b7d8fc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

space/packing_templates_self_supported_offgrid_expanded.json

@@ -1,696 +1,695 @@
 {
   "hut trek (summer)": [
-    "lichtgewicht trekkingrugzak (30\u201345 liter)",
-    "waterzak of drinkflessen",
-    "sneldrogende kleding",
-    "regenjas",
-    "snacks/energierepen",
-    "wandelschoenen",
-    "hoed of pet",
-    "zonnebrandcr\u00e8me",
-    "lakenzak",
-    "oordoppen"
+    "lightweight trekking backpack (30–45 liters)",
+    "hydration bladder or water bottles",
+    "quick‑dry clothing",
+    "rain jacket",
+    "snacks / energy bars",
+    "hiking boots",
+    "hat or cap",
+    "sunscreen",
+    "sheet liner",
+    "earplugs"
   ],
   "hut trek (winter)": [
-    "slaapzak (lichte, warme variant)",
-    "lakenzak",
-    "binnenkleding voor hut (thermo / fleece)",
-    "hutpantoffels / Crocs",
-    "oordoppen",
-    "snacks voor onderweg",
-    "contant geld voor hutbetalingen"
+    "sleeping bag (light, warm variant)",
+    "sheet liner",
+    "indoor hut clothing (thermo / fleece)",
+    "hut slippers / Crocs",
+    "earplugs",
+    "snacks for along the way",
+    "cash for hut payments"
   ],
   "camping trip (wild camping)": [
-    "tent of tarp",
-    "slaapmat",
-    "slaapzak",
-    "hoofdlamp",
-    "mes of multitool",
-    "waterfilter",
-    "kookset + brander",
-    "voedsel en snacks",
-    "aansteker",
-    "toilettas met biologisch afbreekbare zeep"
+    "tent or tarp",
+    "sleeping pad",
+    "sleeping bag",
+    "headlamp",
+    "knife or multitool",
+    "water filter",
+    "cookset + stove",
+    "food and snacks",
+    "lighter",
+    "toiletry bag with biodegradable soap"
   ],
   "camping trip (campground)": [
     "tent",
-    "kussen",
-    "stoeltje",
-    "kampeertafel (optioneel)",
-    "lamp of lantaarn",
-    "verlengsnoer (voor stroomcamping)",
-    "koelbox",
-    "servies & bestek",
-    "afwasspullen",
-    "boodschappen"
+    "pillow",
+    "chair",
+    "camping table (optional)",
+    "lamp or lantern",
+    "extension cord (for powered campsites)",
+    "cooler box",
+    "dishes & cutlery",
+    "washing up supplies",
+    "groceries"
   ],
   "skitocht / skitour": [
-    "touringski's of splitboard",
-    "stijgvellen",
-    "lawinepieper (transceiver)",
-    "schep",
-    "sonde",
-    "gore-tex jas en broek",
-    "donsjas of warme isolatielaag",
-    "helm",
-    "handschoenen (2 paar)",
-    "ski- of zonnebril",
-    "thermosfles",
-    "sneldrogende baselayers",
-    "gamaschen (bij diepe sneeuw)",
-    "tourrugzak met ski-bevestiging",
-    "laagjes voor temperatuurregeling",
-    "energierepen of sportvoeding",
-    "GPS of offline kaarten",
-    "EHBO-set",
-    "lichte handschoenen voor het stijgen"
+    "touring skis or splitboard",
+    "climbing skins",
+    "avalanche beacon (transceiver)",
+    "shovel",
+    "probe",
+    "Gore‑Tex jacket and pants",
+    "down jacket or warm insulation layer",
+    "helmet",
+    "gloves (2 pairs)",
+    "ski or sunglasses",
+    "thermos bottle",
+    "quick‑dry base layers",
+    "gaiters (in deep snow)",
+    "touring backpack with ski attachment",
+    "layers for temperature control",
+    "energy bars or sports nutrition",
+    "GPS or offline maps",
+    "first aid kit",
+    "light gloves for climbing"
   ],
   "snowboard / splitboard trip": [
-    "splitboard of snowboard",
-    "lawinepieper",
-    "helm",
-    "dikke handschoenen",
-    "skibril",
-    "laagjes kleding",
-    "tourbindingen (voor splitboard)",
-    "stijgvellen",
-    "sonde en schep",
-    "lichte handschoenen voor het stijgen"
+    "splitboard or snowboard",
+    "avalanche beacon",
+    "helmet",
+    "thick gloves",
+    "ski goggles",
+    "layers of clothing",
+    "tour bindings (for splitboard)",
+    "climbing skins",
+    "probe and shovel",
+    "light gloves for climbing"
   ],
   "long-distance hike / thru-hike": [
-    "lichtgewicht rugzak",
-    "waterfilter",
-    "voedselvoorraad",
-    "navigatie (kaart/kompas/GPS)",
+    "lightweight backpack",
+    "water filter",
+    "food supply",
+    "navigation (map/compass/GPS)",
     "first aid kit",
-    "trail runners of lichte wandelschoenen",
-    "zonbescherming",
-    "regenbescherming",
-    "natte doekjes",
-    "sneldrogende handdoek"
+    "trail runners or lightweight hiking shoes",
+    "sun protection",
+    "rain protection",
+    "wet wipes",
+    "quick‑dry towel"
   ],
   "digital nomad trip": [
-    "laptop en oplader",
-    "wereldstekker",
-    "noise-cancelling koptelefoon",
-    "wifi-hotspot of lokale simkaart",
-    "powerbank",
-    "notitieboekje",
-    "lichte kleding",
-    "comfortabele rugzak of trolley",
-    "mifi-router of portable wifi-hotspot"
+    "laptop and charger",
+    "world adapter plug",
+    "noise‑cancelling headphones",
+    "WiFi hotspot or local SIM card",
+    "power bank",
+    "notebook",
+    "light clothing",
+    "comfortable backpack or trolley",
+    "MiFi router or portable WiFi hotspot"
   ],
   "city trip": [
-    "dagrugzak",
-    "oplader voor telefoon",
-    "stadsplattegrond / offline maps",
-    "betaalkaart / contant geld",
-    "comfortabele schoenen",
-    "waterfles",
-    "reisgids of highlightslijst",
-    "lichte jas of regenjas"
+    "daypack",
+    "phone charger",
+    "city map / offline maps",
+    "bank card / cash",
+    "comfortable shoes",
+    "water bottle",
+    "guidebook or highlights list",
+    "light jacket or raincoat"
   ],
   "road trip (car/camper)": [
-    "autopapieren",
-    "EHBO-kit",
-    "snacks en drinken",
-    "telefoonhouder / navigatie",
-    "kampeeruitrusting (indien overnachten)",
-    "reisgids of kaarten",
-    "zonnebril",
-    "stoel en tafel",
-    "vermaak (boek, muziek, spelletjes)"
+    "car documents",
+    "first aid kit",
+    "snacks and drinks",
+    "phone holder / navigation",
+    "camping gear (if staying overnight)",
+    "travel guide or maps",
+    "sunglasses",
+    "chair and table",
+    "entertainment (book, music, games)"
   ],
   "festivaltrip": [
     "tent",
-    "matje en slaapzak",
-    "oordoppen",
-    "poncho of regenjas",
+    "pad and sleeping bag",
+    "earplugs",
+    "poncho or rain jacket",
     "glitter / outfit",
-    "herbruikbare beker",
-    "zonnebrand",
-    "cash / pinpas",
-    "vriendenafspraken",
+    "reusable cup",
+    "sunscreen",
+    "cash / card",
+    "friend meetups",
     "snacks"
   ],
   "yoga / wellness retreat": [
-    "yogamat of yogahanddoek",
-    "comfortabele kleding",
-    "waterfles",
-    "dagboek / pen",
-    "warme trui of sjaal",
-    "sandalen",
-    "etherische olie (optioneel)",
-    "boek / meditatiemateriaal"
+    "yoga mat or yoga towel",
+    "comfortable clothing",
+    "water bottle",
+    "journal / pen",
+    "warm sweater or scarf",
+    "sandals",
+    "essential oils (optional)",
+    "book / meditation material"
   ],
   "micro-adventure / weekend trip": [
-    "dagrugzak",
-    "snacks en drinken",
-    "regenjas",
-    "warme laag",
-    "kaart of GPS",
-    "toilettasje",
-    "hoofddeksel",
-    "compacte slaapuitrusting (indien overnachting)"
+    "daypack",
+    "snacks and drinks",
+    "rain jacket",
+    "warm layer",
+    "map or GPS",
+    "toiletry bag",
+    "head cover",
+    "compact sleeping gear (if overnighting)"
   ],
   "beach vacation": [
-    "zwemkleding",
-    "strandlaken",
-    "zonnebrandcr\u00e8me",
-    "zonnebril",
-    "hoed of pet",
-    "slippers",
-    "luchtige kleding",
-    "waterfles",
-    "strandtas",
-    "boek of e-reader"
+    "swimwear",
+    "beach towel",
+    "sunscreen",
+    "sunglasses",
+    "hat or cap",
+    "flip flops",
+    "lightweight clothing",
+    "water bottle",
+    "beach bag",
+    "book or e‑reader"
   ],
   "cultural exploration": [
-    "dagrugzak",
-    "oplader voor telefoon",
-    "stadsplattegrond / offline maps",
-    "betaalkaart / contant geld",
-    "comfortabele schoenen",
-    "waterfles",
-    "reisgids of highlightslijst",
-    "lichte jas of regenjas"
+    "daypack",
+    "phone charger",
+    "city map / offline maps",
+    "bank card / cash",
+    "comfortable shoes",
+    "water bottle",
+    "guidebook or highlights list",
+    "light jacket or raincoat"
   ],
   "nature escape": [
-    "dagrugzak",
-    "oplader voor telefoon",
-    "betaalkaart / contant geld",
-    "comfortabele schoenen",
-    "waterfles",
-    "downtime entertainment (e.g. book/ebook, games, laptop, journal)"
+    "daypack",
+    "phone charger",
+    "bank card / cash",
+    "comfortable shoes",
+    "water bottle",
+    "downtime entertainment (e.g. book / ebook, games, laptop, journal)"
   ],
   "swimming": [
-    "badkleding",
-    "handdoek",
-    "zonnebrandcr\u00e8me",
-    "slippers",
-    "zwembril",
-    "waterfles"
+    "swimsuit",
+    "towel",
+    "sunscreen",
+    "flip flops",
+    "swimming goggles",
+    "water bottle"
   ],
   "going to the beach": [
-    "strandlaken",
-    "zwemkleding",
-    "zonnebrand",
-    "koeltas",
-    "strandstoel",
-    "zonnehoed"
+    "beach towel",
+    "swimwear",
+    "sunscreen",
+    "cooler",
+    "beach chair",
+    "sun hat"
   ],
   "relaxing": [
-    "comfortabele kleding",
-    "boek of e-reader",
-    "zitkussen of strandmat",
-    "muziek / koptelefoon"
+    "comfortable clothing",
+    "book or e‑reader",
+    "seat cushion or beach mat",
+    "music / headphones"
   ],
   "sightseeing": [
-    "dagrugzak",
-    "waterfles",
-    "camera of smartphone",
-    "kaart of offline maps",
-    "zonnebril"
+    "daypack",
+    "water bottle",
+    "camera or smartphone",
+    "map or offline maps",
+    "sunglasses"
   ],
   "biking": [
-    "fiets of huurfiets",
-    "helm",
-    "sportkleding",
-    "fietslicht en slot",
-    "reparatieset"
+    "bike or rental bike",
+    "helmet",
+    "sports clothing",
+    "bike light and lock",
+    "repair kit"
   ],
   "running": [
-    "hardloopschoenen",
-    "sportkleding",
-    "zweetband of pet",
-    "waterfles of belt",
-    "sporthorloge (optioneel)"
+    "running shoes",
+    "sports clothing",
+    "headband or cap",
+    "water bottle or belt",
+    "sports watch (optional)"
   ],
   "skiing": [
-    "ski\u2019s en stokken",
-    "skischoenen",
-    "helm",
-    "skibril",
-    "skipas",
-    "dikke handschoenen",
-    "gore-tex kleding"
+    "skis and poles",
+    "ski boots",
+    "helmet",
+    "ski goggles",
+    "ski pass",
+    "thick gloves",
+    "Gore‑Tex clothing"
   ],
   "cross-country skiing": [
-    "langlaufski's en stokken",
-    "langlaufschoenen",
-    "ademende thermokleding",
-    "winddichte en waterafstotende buitenlaag",
-    "dunne handschoenen",
-    "muts of hoofdband",
-    "zonnebril of sportbril",
-    "buff of nekwarmer",
-    "lichte rugzak met water en snacks",
-    "EHBO-set"
+    "cross-country skis and poles",
+    "cross-country ski boots",
+    "breathable thermal clothing",
+    "windproof and water-repellent outer layer",
+    "thin gloves",
+    "hat or headband",
+    "sunglasses or sport glasses",
+    "buff or neck warmer",
+    "light backpack with water and snacks",
+    "first aid kit"
   ],
   "ski touring": [
-    "algemene items voor deze situatie",
-    "extra kleding of uitrusting indien nodig"
+    "general items for this situation",
+    "extra clothing or gear if needed"
   ],
   "hiking": [
-    "wandelschoenen of trailrunners",
-    "hiking sokken (anti-blaren)",
-    "hikingstokken",
-    "dagrugzak",
-    "regenjas of poncho",
-    "waterfles of waterzak",
-    "snacks / energierepen",
-    "zonnebrand en zonnebril",
-    "pet of hoed",
-    "blarenpleisters of tape",
-    "EHBO-set",
-    "navigatie (kaart, kompas of GPS)"
+    "hiking boots or trail runners",
+    "hiking socks (anti-blister)",
+    "hiking poles",
+    "daypack",
+    "rain jacket or poncho",
+    "water bottle or hydration bladder",
+    "snacks / energy bars",
+    "sunscreen and sunglasses",
+    "cap or hat",
+    "blister plasters or tape",
+    "first aid kit",
+    "navigation (map, compass or GPS)"
   ],
   "hut-to-hut hiking": [
-    "lichtgewicht trekkingrugzak (30\u201345 liter)",
-    "waterflessen of waterzak",
-    "snacks voor onderweg",
-    "regenjas",
-    "licht donsjack of warme laag",
-    "sneldrogende kleding",
-    "wandelschoenen",
-    "lakenzak",
-    "pantoffels of slippers voor hut",
-    "oordoppen",
-    "reserveringsbevestiging / contant geld voor hut"
+    "lightweight trekking backpack (30–45 liters)",
+    "water bottles or hydration bladder",
+    "snacks for along the way",
+    "rain jacket",
+    "light down jacket or warm layer",
+    "quick‑dry clothing",
+    "hiking boots",
+    "sheet liner",
+    "slippers or indoor shoes for hut",
+    "earplugs",
+    "reservation confirmation / cash for hut"
   ],
   "rock climbing": [
-    "klimschoenen",
-    "klimgordel",
-    "zekeringsapparaat",
+    "climbing shoes",
+    "climbing harness",
+    "belay device",
     "chalk bag",
-    "helm"
+    "helmet"
   ],
   "ice climbing": [
-    "ijsbijlen",
+    "ice axes",
     "crampons",
-    "klimtouw",
-    "warme isolatielagen",
-    "klimgordel en helm"
+    "climbing rope",
+    "warm insulation layers",
+    "climbing harness and helmet"
   ],
   "snowshoe hiking": [
-    "algemene items voor deze situatie",
-    "extra kleding of uitrusting indien nodig"
+    "general items for this situation",
+    "extra clothing or gear if needed"
   ],
   "kayaking / canoeing": [
-    "kayak of kano",
-    "peddel",
-    "reddingsvest",
-    "drybag",
-    "waterschoenen"
+    "kayak or canoe",
+    "paddle",
+    "life jacket",
+    "dry bag",
+    "water shoes"
   ],
   "stand-up paddleboarding (SUP)": [
-    "SUP-board en peddel",
-    "reddingsvest",
-    "drybag",
-    "zonnebril met koord"
+    "SUP board and paddle",
+    "life jacket",
+    "dry bag",
+    "sunglasses with strap"
   ],
   "snorkeling": [
-    "snorkel en duikbril",
-    "zwemvliezen",
-    "badkleding",
-    "waterdichte tas"
+    "snorkel and mask",
+    "fins",
+    "swimwear",
+    "waterproof bag"
   ],
   "scuba diving": [
-    "duikbril en snorkel",
+    "mask and snorkel",
     "wetsuit",
-    "vinnen",
-    "duikcomputer",
-    "ademautomaat (indien eigen)"
+    "fins",
+    "dive computer",
+    "regulator (if own)"
   ],
   "surfing": [
     "surfboard",
     "wetsuit",
     "wax",
     "board leash",
-    "poncho of handdoek"
+    "poncho or towel"
   ],
   "paragliding": [
     "paraglider",
-    "helm",
-    "handschoenen",
-    "wandelschoenen",
-    "windjack"
+    "helmet",
+    "gloves",
+    "hiking boots",
+    "wind jacket"
   ],
   "horseback riding": [
-    "rijbroek",
-    "rijlaarzen of schoenen met hak",
-    "handschoenen",
-    "helm",
-    "eventueel eigen zadel of stijgbeugels"
+    "riding pants",
+    "riding boots or shoes with heel",
+    "gloves",
+    "helmet",
+    "optional own saddle or stirrups"
   ],
   "photography": [
-    "camera + lenzen",
-    "statief",
-    "geheugenkaart(en)",
-    "extra accu's",
-    "lensdoekje"
+    "camera + lenses",
+    "tripod",
+    "memory card(s)",
+    "extra batteries",
+    "lens cloth"
   ],
   "fishing": [
-    "hengel",
-    "aas / kunstaas",
-    "visvergunning",
-    "laarzen of waadpak",
-    "koelbox (optioneel)"
+    "fishing rod",
+    "bait / lures",
+    "fishing license",
+    "boots or waders",
+    "cooler box (optional)"
   ],
   "rafting": [
-    "reddingsvest",
-    "waterdichte tas",
-    "waterschoenen",
-    "sneldrogende kleding",
-    "helm"
+    "life jacket",
+    "waterproof bag",
+    "water shoes",
+    "quick‑dry clothing",
+    "helmet"
   ],
   "yoga": [
-    "algemene items voor deze situatie",
-    "extra kleding of uitrusting indien nodig"
+    "general items for this situation",
+    "extra clothing or gear if needed"
   ],
   "cold destination / winter": [
-    "thermokleding",
-    "muts en handschoenen",
-    "warme jas",
-    "waterdichte schoenen",
-    "lippenbalsem"
+    "thermal clothing",
+    "hat and gloves",
+    "warm jacket",
+    "waterproof shoes",
+    "lip balm"
   ],
   "warm destination / summer": [
-    "luchtige kleding",
-    "zonnebril",
-    "zonnecr\u00e8me",
-    "waterfles",
-    "hoed of pet"
+    "lightweight clothing",
+    "sunglasses",
+    "sunscreen",
+    "water bottle",
+    "hat or cap"
   ],
   "variable weather / spring / autumn": [
-    "regenjas",
-    "laagjeskleding",
-    "warme trui",
-    "waterdichte schoenen"
+    "rain jacket",
+    "layered clothing",
+    "warm sweater",
+    "waterproof shoes"
   ],
   "tropical / humid": [
-    "lichtgewicht kleding",
-    "insectenspray",
-    "zonnecr\u00e8me",
-    "regenponcho",
-    "sneldrogende handdoek"
+    "lightweight clothing",
+    "insect repellent",
+    "sunscreen",
+    "rain poncho",
+    "quick‑dry towel"
   ],
   "dry / desert-like": [
-    "zonnebril",
-    "zonnecr\u00e8me",
-    "hoofdbescherming",
-    "lichte lange mouwen",
-    "veel water"
+    "sunglasses",
+    "sunscreen",
+    "head protection",
+    "light long sleeves",
+    "lots of water"
   ],
   "ultralight": [
-    "lichtgewicht rugzak (< 1kg)",
-    "minimalistische shelter (tarp of tent)",
-    "lichtgewicht slaapmat",
-    "quilt of donsdeken",
-    "titanium kookset",
-    "beperkte kleding (laagjes!)",
-    "compacte regenjas",
-    "sneldrogende handdoek",
-    "tandenborstel (afgezaagd ;))"
+    "lightweight backpack (< 1kg)",
+    "minimalist shelter (tarp or tent)",
+    "lightweight sleeping pad",
+    "quilt or down blanket",
+    "titanium cookset",
+    "limited clothing (layers!)",
+    "compact rain jacket",
+    "quick‑dry towel",
+    "toothbrush (shortened ;))"
   ],
   "lightweight (but comfortable)": [
-    "lichte tent of tarp",
-    "comfortabele slaapmat",
-    "lichtgewicht kookset",
-    "compact kledingpakket",
-    "sneldrogende handdoek"
+    "light tent or tarp",
+    "comfortable sleeping pad",
+    "lightweight cookset",
+    "compact clothing pack",
+    "quick‑dry towel"
   ],
   "luxury (including evening wear)": [
-    "nette schoenen",
-    "jurk of overhemd",
-    "parfum/deodorant",
-    "accessoires",
-    "reistoilettas met essentials"
+    "dress shoes",
+    "dress or shirt",
+    "perfume / deodorant",
+    "accessories",
+    "toiletry bag with essentials"
   ],
   "minimalist": [
-    "1 set kleding voor elke situatie",
-    "compacte tandenborstel",
-    "alles-in-\u00e9\u00e9n zeep",
-    "lichtgewicht handdoek",
-    "kleine rugzak"
+    "1 set of clothing for every situation",
+    "compact toothbrush",
+    "all‑in‑one soap",
+    "lightweight towel",
+    "small backpack"
   ],
   "casual": [
-    "jeans of comfortabele broek",
+    "jeans or comfortable pants",
     "t-shirts",
-    "trui of hoodie",
+    "sweater or hoodie",
     "sneakers",
-    "zonnebril"
+    "sunglasses"
   ],
   "formal (business trip)": [
-    "overhemd / blouse",
-    "net jasje",
-    "nette schoenen",
-    "laptop en oplader",
-    "naamkaartjes / documenten"
+    "shirt / blouse",
+    "smart jacket",
+    "dress shoes",
+    "laptop and charger",
+    "business cards / documents"
   ],
   "conservative": [
-    "bedekkende kleding",
-    "sjaal of omslagdoek",
-    "lange broek of rok",
-    "gesloten schoenen"
+    "covering clothing",
+    "scarf or shawl",
+    "long pants or skirt",
+    "closed shoes"
   ],
   "outdoor": [
-    "regenjas",
-    "waterdichte schoenen",
-    "bivakzak of tarp",
-    "hoofdlamp",
-    "navigatie"
+    "rain jacket",
+    "waterproof shoes",
+    "bivvy bag or tarp",
+    "headlamp",
+    "navigation"
   ],
   "indoor": [
-    "pantoffels",
-    "comfortabele kleding",
-    "pyjama",
-    "oplader",
-    "toilettas"
+    "slippers",
+    "comfortable clothing",
+    "pajamas",
+    "charger",
+    "toiletry bag"
   ],
   "huts with half board": [
-    "reserveringsbevestiging",
-    "lakenzak (vaak verplicht)",
-    "pantoffels of slippers voor binnen",
-    "lichte pyjama of slaapkleding",
-    "oorstopjes",
-    "waterfles",
-    "snacks voor onderweg",
-    "kleine toilettas",
-    "contant geld voor betalingen",
-    "lichte handdoek",
-    "zaklamp of hoofdlamp"
+    "reservation confirmation",
+    "sheet liner (often required)",
+    "slippers or indoor shoes for inside",
+    "light pajamas or sleepwear",
+    "earplugs",
+    "water bottle",
+    "snacks for along the way",
+    "small toiletry bag",
+    "cash for payments",
+    "light towel",
+    "flashlight or headlamp"
   ],
   "sleeping in a tent": [
-    "tent (1- of 2-persoons, afhankelijk van reis)",
-    "grondzeil",
-    "slaapmat",
-    "slaapzak (passend bij temperatuur)",
-    "hoofdlamp of zaklamp",
-    "sneldrogende handdoek",
-    "kussen of opblaasbaar hoofdkussen",
-    "oorstopjes",
-    "toilettas",
-    "powerbank",
-    "waterfles",
-    "boek of e-reader",
-    "EHBO-set"
+    "tent (1‑ or 2‑person, depending on trip)",
+    "groundsheet",
+    "sleeping pad",
+    "sleeping bag (suitable for temperature)",
+    "headlamp or flashlight",
+    "quick‑dry towel",
+    "pillow or inflatable pillow",
+    "earplugs",
+    "toiletry bag",
+    "power bank",
+    "water bottle",
+    "book or e‑reader",
+    "first aid kit"
   ],
   "sleeping in a car": [
-    "slaapmat die past in auto",
-    "warme slaapzak",
-    "zonneschermen of verduistering",
-    "kussen",
-    "waterfles binnen handbereik",
-    "powerbank of 12V-lader",
-    "toilettas",
-    "snacks voor de nacht",
-    "thermische deken (voor koude nachten)"
+    "sleeping pad that fits in car",
+    "warm sleeping bag",
+    "sunshades or blackout covers",
+    "pillow",
+    "water bottle within reach",
+    "power bank or 12V charger",
+    "toiletry bag",
+    "snacks for the night",
+    "thermal blanket (for cold nights)"
   ],
   "own vehicle": [
-    "rijbewijs",
-    "autopapieren",
-    "EHBO-set",
-    "navigatie of smartphone",
-    "telefoonhouder"
+    "driver’s license",
+    "car documents",
+    "first aid kit",
+    "navigation or smartphone",
+    "phone holder"
   ],
   "no own vehicle": [
-    "rugzak",
-    "waterfles",
-    "lichte schoenen",
-    "openbaar vervoer app of ticket"
+    "backpack",
+    "water bottle",
+    "light shoes",
+    "public transport app or ticket"
   ],
   "off-grid / no electricity": [
-    "powerbank (minstens 10.000 mAh)",
-    "zonnepaneel of draagbaar laadsysteem",
-    "hoofdlamp + extra batterijen",
-    "oplaadbare batterijen en oplader",
-    "back-up verlichting (bijv. kleine zaklamp)",
-    "papieren kaart en kompas",
-    "notitieboekje + pen",
-    "noodcommunicatie (bijv. GPS beacon of satellietboodschapper)",
-    "opvouwbaar zonnepaneel (indien langere tochten)",
-    "navigatieapparaat met offline kaarten",
-    "extra opladerkabels",
-    "USB-hub (voor meerdere devices)",
-    "verpakking om elektronica droog te houden"
+    "power bank (at least 10,000 mAh)",
+    "solar panel or portable charging system",
+    "headlamp + extra batteries",
+    "rechargeable batteries and charger",
+    "backup lighting (e.g. small flashlight)",
+    "paper map and compass",
+    "notebook + pen",
+    "emergency communication (e.g. GPS beacon or satellite messenger)",
+    "foldable solar panel (if on longer trips)",
+    "navigation device with offline maps",
+    "extra charger cables",
+    "USB hub (for multiple devices)",
+    "packaging to keep electronics dry"
   ],
   "self-supported (bring your own cooking gear)": [
-    "lichtgewicht kooktoestel (gas, benzine of alcohol)",
-    "brandstof (voldoende voor aantal dagen)",
-    "pan of keteltje",
-    "spork of lepel",
-    "opvouwbaar snijplankje (optioneel)",
-    "aandrukbare kom of beker",
-    "aansteker + lucifers (waterdicht verpakt)",
-    "gedroogde of gevriesdroogde maaltijden",
-    "snacks / noodbars",
-    "afwasmiddel (biologisch afbreekbaar) + sponsje",
-    "opslagzakken voor afval",
-    "waterfilter of -pomp",
-    "chloordruppels of zuiveringstabletten",
-    "minstens 2 liter wateropslag per persoon",
-    "food bag of hangzak voor voedsel (wild-safe)"
+    "lightweight stove (gas, petrol or alcohol)",
+    "fuel (enough for several days)",
+    "pan or small pot",
+    "spork or spoon",
+    "foldable cutting board (optional)",
+    "press‑on bowl or mug",
+    "lighter + matches (waterproof packed)",
+    "dried or freeze‑dried meals",
+    "snacks / emergency bars",
+    "biodegradable soap + sponge",
+    "bags for waste",
+    "water filter or pump",
+    "chlorine drops or purification tablets",
+    "at least 2 liters of water storage per person",
+    "food bag or hanging bag (wild-safe)"
   ],
   "travel with children": [
-    "snacks en speelgoed",
-    "EHBO-set met pleisters",
-    "extra kleding",
-    "kinderdrager of buggy",
-    "zonbescherming",
-    "extra snacks voor kinderen",
-    "favoriet speelgoed of knuffel",
-    "reisstoel of draagzak",
-    "natte doekjes",
-    "luiers of potje (afhankelijk van leeftijd)",
-    "extra set kleding per dag",
-    "reisapotheek voor kinderen",
-    "kinderzonnebrand",
-    "activiteitenboekje of tablet met filmpjes",
-    "snelle snacks voor onderweg",
-    "kinder slaapzak of slaapmat",
-    "reisbedje of matrasje (voor jonge kinderen)",
-    "luiers of oefenbroekjes",
-    "flesjes en voeding (indien van toepassing)",
-    "babyfoon (voor verblijf op locatie)",
-    "speen of fopspeen",
-    "kinder EHBO-set (inclusief thermometer)",
-    "zonnehoedje of muts",
-    "regenhoes voor kinderwagen of draagzak",
-    "hydraterende cr\u00e8me (voor gevoelige huid)"
+    "snacks and toys",
+    "first aid kit with bandages",
+    "extra clothing",
+    "child carrier or stroller",
+    "sun protection",
+    "extra snacks for children",
+    "favorite toy or stuffed animal",
+    "travel chair or sling",
+    "wet wipes",
+    "diapers or potty (depending on age)",
+    "extra clothing set per day",
+    "children’s travel pharmacy",
+    "children’s sunscreen",
+    "activity book or tablet with films",
+    "quick snacks for en route",
+    "child sleeping bag or pad",
+    "travel crib or mattress (for young children)",
+    "diapers or training pants",
+    "bottles and food (if applicable)",
+    "baby monitor (for staying at location)",
+    "pacifier or dummy",
+    "kids first aid kit (including thermometer)",
+    "sun hat or cap",
+    "rain cover for stroller or carrier",
+    "hydrating cream (for sensitive skin)"
   ],
   "pet-friendly": [
-    "voer en waterbak",
-    "hondenriem of tuigje",
-    "poepzakjes",
-    "reismatje of deken",
-    "vaccinatieboekje"
+    "food and water bowl",
+    "dog leash or harness",
+    "poop bags",
+    "travel mat or blanket",
+    "vaccination booklet"
   ],
   "rainy climate": [
-    "regenjas of poncho",
-    "waterdichte rugzakhoes",
-    "sneldrogende kleding",
-    "rubberen schoenen"
+    "rain jacket or poncho",
+    "waterproof backpack cover",
+    "quick‑dry clothing",
+    "rubber shoes"
   ],
   "snow and ice": [
-    "warme laarzen",
-    "dikke handschoenen",
-    "sneeuwbril",
-    "thermokleding",
-    "gripzolen / spikes"
+    "warm boots",
+    "thick gloves",
+    "snow goggles",
+    "thermal clothing",
+    "traction soles / spikes"
   ],
   "high alpine terrain": [
-    "wandelstokken",
-    "kaart en kompas",
-    "wind- en waterdichte jas",
-    "extra voeding",
-    "EHBO-kit"
+    "trekking poles",
+    "map and compass",
+    "wind‑ and waterproof jacket",
+    "extra food",
+    "first aid kit"
   ],
   "snow, ice and avalanche-prone terrain": [
-    "lawinepieper",
-    "schep",
-    "sonde",
-    "airbag rugzak (indien beschikbaar)",
-    "kennis van lawineveiligheid / cursus",
-    "kaart en kompas / GPS",
-    "partner check voor vertrek"
-  ],
-  "no special conditions": [
-  ]
+    "avalanche beacon",
+    "shovel",
+    "probe",
+    "airbag backpack (if available)",
+    "knowledge of avalanche safety / course",
+    "map and compass / GPS",
+    "partner check before departure"
+  ],
+  "no special conditions": [],
   "1 day": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "2 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "3 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "4 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "5 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "6 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "7 days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "7+ days": [
-    "ondergoed per dag",
-    "sokken per dag",
-    "toilettas",
-    "extra kledinglaag",
-    "aantal maaltijden/snacks afgestemd op duur"
+    "underwear per day",
+    "socks per day",
+    "toiletry bag",
+    "extra clothing layer",
+    "number of meals/snacks matched to duration"
   ],
   "default_items": [
-    "telefoon + oplader",
-    "powerbank",
-    "identiteitsbewijs of paspoort",
-    "betaalmiddelen (pinpas / contant geld)",
-    "verzekeringspas / reisverzekering-info",
-    "toilettas (tandpasta, borstel, deodorant, zeep)",
-    "zonnebrandcr\u00e8me",
-    "lippenbalsem",
-    "ondergoed",
-    "sokken",
-    "regenjas of windjack",
-    "waterfles",
+    "phone + charger",
+    "power bank",
+    "identity document or passport",
+    "payment methods (debit card / cash)",
+    "insurance card / travel insurance info",
+    "toiletry bag (toothpaste, brush, deodorant, soap)",
+    "sunscreen",
+    "lip balm",
+    "underwear",
+    "socks",
+    "rain jacket or windbreaker",
+    "water bottle",
     "snacks",
-    "zonnebril",
-    "oorstopjes",
-    "slaapmasker",
-    "herbruikbare tas",
-    "zakdoekjes of toiletpapier"
+    "sunglasses",
+    "earplugs",
+    "sleep mask",
+    "reusable bag",
+    "tissues or toilet paper"
   ]
-}
+}

space/space/space/space/.ipynb_checkpoints/app-checkpoint.py

@@ -1,22 +1,48 @@
+# Prerequisites
 from transformers import pipeline
+import json
+import pandas as pd
 import gradio as gr
 
-# Load the model and create a pipeline for zero-shot classification
-classifier = pipeline("zero-shot-classification", model="facebook/bart-base")
+# get candidate labels
+with open("packing_label_structure.json", "r") as file:
+    candidate_labels = json.load(file)
+keys_list = list(candidate_labels.keys())
 
-# Load labels from a txt file
-with open("labels.txt", "r", encoding="utf-8") as f:
-    class_labels = [line.strip() for line in f if line.strip()]
+# Load test data (in list of dictionaries)
+with open("test_data.json", "r") as file:
+    packing_data = json.load(file)
 
-# Define the Gradio interface
-def classify(text):
-    return classifier(text, class_labels)
+# function and gradio app
+model_name = "facebook/bart-large-mnli"
+classifier = pipeline("zero-shot-classification", model=model_name)
+cut_off = 0.5  # used to choose which activities are relevant
+
+def classify(#model_name, 
+             trip_descr, cut_off):
+    
+    # Create an empty DataFrame with specified columns
+    df = pd.DataFrame(columns=['superclass', 'pred_class'])
+    for i, key in enumerate(keys_list):
+        if key == 'activities':
+            result = classifier(trip_descr, candidate_labels[key], multi_label=True)
+            indices = [i for i, score in enumerate(result['scores']) if score > cut_off]
+            classes = [result['labels'][i] for i in indices]
+        else:
+            result = classifier(trip_descr, candidate_labels[key])
+            classes = result["labels"][0]
+        df.loc[i] = [key, classes]
+    return df
 
 demo = gr.Interface(
     fn=classify,
-    inputs="text",
-    outputs="json",
-    title="Zero-Shot Classification",
+    inputs=[
+        #gr.Textbox(label="Model name", value = "facebook/bart-large-mnli"),
+        gr.Textbox(label="Trip description"),
+        gr.Number(label="Activity cut-off", value = 0.5),
+    ],
+    outputs="dataframe",
+    title="Trip classification",
     description="Enter a text describing your trip",
 )
 

space/space/space/space/app.py

@@ -1,22 +1,48 @@
+# Prerequisites
 from transformers import pipeline
+import json
+import pandas as pd
 import gradio as gr
 
-# Load the model and create a pipeline for zero-shot classification
-classifier = pipeline("zero-shot-classification", model="facebook/bart-base")
+# get candidate labels
+with open("packing_label_structure.json", "r") as file:
+    candidate_labels = json.load(file)
+keys_list = list(candidate_labels.keys())
 
-# Load labels from a txt file
-with open("labels.txt", "r", encoding="utf-8") as f:
-    class_labels = [line.strip() for line in f if line.strip()]
+# Load test data (in list of dictionaries)
+with open("test_data.json", "r") as file:
+    packing_data = json.load(file)
 
-# Define the Gradio interface
-def classify(text):
-    return classifier(text, class_labels)
+# function and gradio app
+model_name = "facebook/bart-large-mnli"
+classifier = pipeline("zero-shot-classification", model=model_name)
+cut_off = 0.5  # used to choose which activities are relevant
+
+def classify(#model_name, 
+             trip_descr, cut_off):
+    
+    # Create an empty DataFrame with specified columns
+    df = pd.DataFrame(columns=['superclass', 'pred_class'])
+    for i, key in enumerate(keys_list):
+        if key == 'activities':
+            result = classifier(trip_descr, candidate_labels[key], multi_label=True)
+            indices = [i for i, score in enumerate(result['scores']) if score > cut_off]
+            classes = [result['labels'][i] for i in indices]
+        else:
+            result = classifier(trip_descr, candidate_labels[key])
+            classes = result["labels"][0]
+        df.loc[i] = [key, classes]
+    return df
 
 demo = gr.Interface(
     fn=classify,
-    inputs="text",
-    outputs="json",
-    title="Zero-Shot Classification",
+    inputs=[
+        #gr.Textbox(label="Model name", value = "facebook/bart-large-mnli"),
+        gr.Textbox(label="Trip description"),
+        gr.Number(label="Activity cut-off", value = 0.5),
+    ],
+    outputs="dataframe",
+    title="Trip classification",
     description="Enter a text describing your trip",
 )
 

space/space/space/space/main_model.ipynb

@@ -0,0 +1,1115 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e25090fa-f990-4f1a-84f3-b12159eedae8",
+   "metadata": {},
+   "source": [
+    "# Implement and test prediciton model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bbee2e4-55c8-4b06-9929-72026edf7932",
+   "metadata": {},
+   "source": [
+    "## Prerequisites"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f8c28d2d-8458-49fd-8ebf-5e729d6e861f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prerequisites\n",
+    "from tabulate import tabulate\n",
+    "from transformers import pipeline\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pickle\n",
+    "import os\n",
+    "import time\n",
+    "import math\n",
+    "\n",
+    "# get candidate labels\n",
+    "with open(\"packing_label_structure.json\", \"r\") as file:\n",
+    "    candidate_labels = json.load(file)\n",
+    "keys_list = list(candidate_labels.keys())\n",
+    "\n",
+    "# Load test data (in list of dictionaries)\n",
+    "with open(\"test_data.json\", \"r\") as file:\n",
+    "    packing_data = json.load(file)\n",
+    "# Extract all trip descriptions and trip_types\n",
+    "trip_descriptions = [trip['description'] for trip in packing_data]\n",
+    "trip_types = [trip['trip_types'] for trip in packing_data]\n",
+    "\n",
+    "# Access the first trip description\n",
+    "first_trip = trip_descriptions[0]\n",
+    "# Get the packing list for the secondfirst trip\n",
+    "first_trip_type = trip_types[0]\n",
+    "\n",
+    "# print(f\"First trip: {first_trip} \\n\")\n",
+    "# print(f\"Trip type: {first_trip_type}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cf4f76f-0035-44e8-93af-52eafaec686e",
+   "metadata": {},
+   "source": [
+    "**All trip descriptions**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "89d42ca7-e871-4eda-b428-69e9bd965428",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 . I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands. \n",
+      "\n",
+      "beach vacation\n",
+      "['swimming', 'going to the beach', 'relaxing', 'hiking']\n",
+      "warm destination / summer\n",
+      "lightweight (but comfortable)\n",
+      "casual\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "no special conditions to consider\n",
+      "7+ days\n",
+      "\n",
+      "\n",
+      "1 . We are a couple in our thirties traveling to Vienna for a three-day city trip. We’ll be staying at a friend’s house and plan to explore the city by sightseeing, strolling through the streets, visiting markets, and trying out great restaurants and cafés. We also hope to attend a classical music concert. Our journey to Vienna will be by train. \n",
+      "\n",
+      "city trip\n",
+      "['sightseeing']\n",
+      "variable weather / spring / autumn\n",
+      "luxury (including evening wear)\n",
+      "casual\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "no special conditions to consider\n",
+      "3 days\n",
+      "\n",
+      "\n",
+      "2 . My partner and I are traveling to the Netherlands and Germany to spend Christmas with our family. We are in our late twenties and will start our journey with a two-hour flight to the Netherlands. From there, we will take a 5.5-hour train ride to northern Germany. \n",
+      "\n",
+      "city trip\n",
+      "['relaxing']\n",
+      "cold destination / winter\n",
+      "lightweight (but comfortable)\n",
+      "casual\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "no special conditions to consider\n",
+      "7+ days\n",
+      "\n",
+      "\n",
+      "3 . I’m in my twenties and will be traveling to Peru for three weeks. I’m going solo but will meet up with a friend to explore the Sacred Valley and take part in a Machu Picchu tour. We plan to hike, go rafting, and explore the remnants of the ancient Inca Empire. We’re also excited to try Peruvian cuisine and immerse ourselves in the local culture. Depending on our plans, we might also visit the rainforest region, such as Tarapoto. I’ll be flying to Peru on a long-haul flight and will be traveling in August. \n",
+      "\n",
+      "cultural exploration\n",
+      "['sightseeing', 'hiking', 'rafting']\n",
+      "variable weather / spring / autumn\n",
+      "lightweight (but comfortable)\n",
+      "casual\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "rainy climate\n",
+      "7+ days\n",
+      "\n",
+      "\n",
+      "4 . We’re planning a 10-day trip to Austria in the summer, combining hiking with relaxation by the lake. We love exploring scenic trails and enjoying the outdoors, but we also want to unwind and swim in the lake. It’s the perfect mix of adventure and relaxation. \n",
+      "\n",
+      "nature escape\n",
+      "['swimming', 'relaxing', 'hiking']\n",
+      "warm destination / summer\n",
+      "lightweight (but comfortable)\n",
+      "casual\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "no special conditions to consider\n",
+      "7+ days\n",
+      "\n",
+      "\n",
+      "5 . I am going on a multiple day hike and passing though mountains and the beach in Croatia. I like to pack light and will stay in refugios/huts with half board and travel to the start of the hike by car. It will be 6-7 days. \n",
+      "\n",
+      "long-distance hike / thru-hike\n",
+      "['going to the beach']\n",
+      "tropical / humid\n",
+      "minimalist\n",
+      "casual\n",
+      "huts with half board\n",
+      "own vehicle\n",
+      "off-grid / no electricity\n",
+      "6 days\n",
+      "\n",
+      "\n",
+      "6 . I will go with a friend on a beach holiday and we will do stand-up paddling, and surfing in the North of Spain. The destination is windy and can get cold, but is generally sunny. We will go by car and bring a tent to sleep in. It will be two weeks. \n",
+      "\n",
+      "beach vacation\n",
+      "['stand-up paddleboarding (SUP)', 'surfing']\n",
+      "cold destination / winter\n",
+      "ultralight\n",
+      "casual\n",
+      "sleeping in a tent\n",
+      "own vehicle\n",
+      "off-grid / no electricity\n",
+      "6 days\n",
+      "\n",
+      "\n",
+      "7 . We will go to Sweden in the winter, to go for a yoga and sauna/wellness retreat. I prefer lightweight packing and also want clothes to go for fancy dinners and maybe on a winter hike. We stay in hotels. \n",
+      "\n",
+      "yoga / wellness retreat\n",
+      "['hiking', 'yoga']\n",
+      "cold destination / winter\n",
+      "lightweight (but comfortable)\n",
+      "casual\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "snow and ice\n",
+      "7 days\n",
+      "\n",
+      "\n",
+      "8 . I go on a skitouring trip where we also make videos/photos and the destination is Japan. Mainly sports clothes and isolation are needed (it is winter). We stay in a guesthouse. It will be 10 days. \n",
+      "\n",
+      "ski tour / skitour\n",
+      "['ski touring', 'photography', 'swimming']\n",
+      "cold destination / winter\n",
+      "minimalist\n",
+      "conservative\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "avalanche-prone terrain\n",
+      "5 days\n",
+      "\n",
+      "\n",
+      "9 . We plan a wild camping trip with activities such as snorkeling, kayaking and canoeing. It is a warm place and we want to bring little stuff. We stay in tents and hammocks and travel with a car, it will be 3 days. \n",
+      "\n",
+      "camping trip (wild camping)\n",
+      "['scuba diving', 'kayaking / canoeing']\n",
+      "tropical / humid\n",
+      "lightweight (but comfortable)\n",
+      "conservative\n",
+      "sleeping in a tent\n",
+      "own vehicle\n",
+      "no special conditions to consider\n",
+      "3 days\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, item in enumerate(trip_descriptions):\n",
+    "    print(i, \".\", item, \"\\n\")\n",
+    "    for elem in trip_types[i]:\n",
+    "        print(elem)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f60c54b-affc-4d9a-acf1-da70f68c5578",
+   "metadata": {},
+   "source": [
+    "**Functions**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fac51224-9575-4b4b-8567-4ad4e759ecc9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function that returns pandas data frame with predictions and true values\n",
+    "\n",
+    "cut_off = 0.5  # used to choose which activities are relevant\n",
+    "\n",
+    "def pred_trip(model_name, trip_descr, trip_type, cut_off):\n",
+    "    classifier = pipeline(\"zero-shot-classification\", model=model_name)\n",
+    "    # Create an empty DataFrame with specified columns\n",
+    "    df = pd.DataFrame(columns=['superclass', 'pred_class'])\n",
+    "    for i, key in enumerate(keys_list):\n",
+    "        if key == 'activities':\n",
+    "            result = classifier(trip_descr, candidate_labels[key], multi_label=True)\n",
+    "            indices = [i for i, score in enumerate(result['scores']) if score > cut_off]\n",
+    "            classes = [result['labels'][i] for i in indices]\n",
+    "        else:\n",
+    "            result = classifier(trip_descr, candidate_labels[key])\n",
+    "            classes = result[\"labels\"][0]\n",
+    "        print(result)\n",
+    "        print(classes)\n",
+    "        print(i)\n",
+    "        df.loc[i] = [key, classes]\n",
+    "    df['true_class'] = trip_type\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b36ab806-2f35-4950-ac5a-7c192190cba7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function for accuracy, perc true classes identified and perc wrong pred classes\n",
+    "\n",
+    "def perf_measure(df):\n",
+    "    df['same_value'] = df['pred_class'] == df['true_class']\n",
+    "    correct = sum(df.loc[df.index != 1, 'same_value'])\n",
+    "    total = len(df['same_value'])\n",
+    "    accuracy = correct/total\n",
+    "    pred_class = df.loc[df.index == 1, 'pred_class'].iloc[0]\n",
+    "    true_class = df.loc[df.index == 1, 'true_class'].iloc[0]\n",
+    "    correct = [label for label in pred_class if label in true_class]\n",
+    "    num_correct = len(correct)\n",
+    "    correct_perc = num_correct/len(true_class)\n",
+    "    num_pred = len(pred_class)\n",
+    "    if num_pred == 0:\n",
+    "        wrong_perc = math.nan\n",
+    "    else:\n",
+    "        wrong_perc = (num_pred - num_correct)/num_pred\n",
+    "    df_perf = pd.DataFrame({\n",
+    "    'accuracy': [accuracy],\n",
+    "    'true_ident': [correct_perc],\n",
+    "    'false_pred': [wrong_perc]\n",
+    "    })\n",
+    "    return(df_perf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c10aa57d-d7ed-45c7-bdf5-29af193c7fd5",
+   "metadata": {},
+   "source": [
+    "## Make predictions for many models and trip descriptions\n",
+    "\n",
+    "Provide a list of candidate models and apply them to the test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dd7869a8-b436-40de-9ea0-28eb4b7d3248",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Using model: facebook/bart-large-mnli\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 25\u001b[0m\n\u001b[1;32m     23\u001b[0m current_trip \u001b[38;5;241m=\u001b[39m trip_descriptions[i]\n\u001b[1;32m     24\u001b[0m current_type \u001b[38;5;241m=\u001b[39m trip_types[i]\n\u001b[0;32m---> 25\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpred_trip\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurrent_trip\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurrent_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcut_off\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n\u001b[1;32m     27\u001b[0m \u001b[38;5;66;03m# accuracy, perc true classes identified and perc wrong pred classes\u001b[39;00m\n",
+      "Cell \u001b[0;32mIn[3], line 15\u001b[0m, in \u001b[0;36mpred_trip\u001b[0;34m(model_name, trip_descr, trip_type, cut_off)\u001b[0m\n\u001b[1;32m     13\u001b[0m     classes \u001b[38;5;241m=\u001b[39m [result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m][i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m indices]\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 15\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mclassifier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrip_descr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcandidate_labels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     16\u001b[0m     classes \u001b[38;5;241m=\u001b[39m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28mprint\u001b[39m(result)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/zero_shot_classification.py:206\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline.__call__\u001b[0;34m(self, sequences, *args, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnable to understand extra arguments \u001b[39m\u001b[38;5;132;01m{\u001b[39;00margs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msequences\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/base.py:1294\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterate(inputs, preprocess_params, forward_params, postprocess_params)\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ChunkPipeline):\n\u001b[0;32m-> 1294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1295\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1296\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_iterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1297\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreprocess_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpostprocess_params\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1302\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_single(inputs, preprocess_params, forward_params, postprocess_params)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/pt_utils.py:124\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_item()\n\u001b[1;32m    123\u001b[0m \u001b[38;5;66;03m# We're out of items within a batch\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(item, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;66;03m# We now have a batch of \"inferred things\".\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/pt_utils.py:269\u001b[0m, in \u001b[0;36mPipelinePackIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m accumulator\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_last:\n\u001b[0;32m--> 269\u001b[0m     processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed, torch\u001b[38;5;241m.\u001b[39mTensor):\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/base.py:1209\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1207\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m inference_context():\n\u001b[1;32m   1208\u001b[0m         model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m-> 1209\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1210\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m   1211\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/zero_shot_classification.py:229\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline._forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(model_forward)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    228\u001b[0m     model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    232\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcandidate_label\u001b[39m\u001b[38;5;124m\"\u001b[39m: candidate_label,\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msequence\u001b[39m\u001b[38;5;124m\"\u001b[39m: sequence,\n\u001b[1;32m    234\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m: inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moutputs,\n\u001b[1;32m    236\u001b[0m }\n\u001b[1;32m    237\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model_outputs\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/bart/modeling_bart.py:1763\u001b[0m, in \u001b[0;36mBartForSequenceClassification.forward\u001b[0;34m(self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, inputs_embeds, decoder_inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m input_ids \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m inputs_embeds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1759\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing input embeddings is currently not supported for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1761\u001b[0m     )\n\u001b[0;32m-> 1763\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1764\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1765\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1766\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecoder_input_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_input_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1767\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1768\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1769\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecoder_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1770\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcross_attn_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_attn_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1771\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoder_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1772\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1773\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecoder_inputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_inputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1774\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1775\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1776\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1777\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1778\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1779\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# last hidden state\u001b[39;00m\n\u001b[1;32m   1781\u001b[0m eos_mask \u001b[38;5;241m=\u001b[39m input_ids\u001b[38;5;241m.\u001b[39meq(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39meos_token_id)\u001b[38;5;241m.\u001b[39mto(hidden_states\u001b[38;5;241m.\u001b[39mdevice)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/bart/modeling_bart.py:1528\u001b[0m, in \u001b[0;36mBartModel.forward\u001b[0;34m(self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, inputs_embeds, decoder_inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1521\u001b[0m     encoder_outputs \u001b[38;5;241m=\u001b[39m BaseModelOutput(\n\u001b[1;32m   1522\u001b[0m         last_hidden_state\u001b[38;5;241m=\u001b[39mencoder_outputs[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m   1523\u001b[0m         hidden_states\u001b[38;5;241m=\u001b[39mencoder_outputs[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(encoder_outputs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1524\u001b[0m         attentions\u001b[38;5;241m=\u001b[39mencoder_outputs[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(encoder_outputs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1525\u001b[0m     )\n\u001b[1;32m   1527\u001b[0m \u001b[38;5;66;03m# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)\u001b[39;00m\n\u001b[0;32m-> 1528\u001b[0m decoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_input_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1530\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1531\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_outputs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1532\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1533\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1534\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcross_attn_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_attn_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1535\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1536\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_inputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1537\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1538\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1539\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1540\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1541\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_dict:\n\u001b[1;32m   1544\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m decoder_outputs \u001b[38;5;241m+\u001b[39m encoder_outputs\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/bart/modeling_bart.py:1380\u001b[0m, in \u001b[0;36mBartDecoder.forward\u001b[0;34m(self, input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask, head_mask, cross_attn_head_mask, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1367\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m   1368\u001b[0m         decoder_layer\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m   1369\u001b[0m         hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1377\u001b[0m         use_cache,\n\u001b[1;32m   1378\u001b[0m     )\n\u001b[1;32m   1379\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1380\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mdecoder_layer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1381\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1382\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1383\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1384\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1385\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1386\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcross_attn_layer_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1387\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcross_attn_head_mask\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcross_attn_head_mask\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\n\u001b[1;32m   1388\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1389\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1390\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1391\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1392\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1393\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1395\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache:\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/bart/modeling_bart.py:666\u001b[0m, in \u001b[0;36mBartDecoderLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, layer_head_mask, cross_attn_layer_head_mask, past_key_value, output_attentions, use_cache)\u001b[0m\n\u001b[1;32m    664\u001b[0m self_attn_past_key_value \u001b[38;5;241m=\u001b[39m past_key_value[:\u001b[38;5;241m2\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m past_key_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    665\u001b[0m \u001b[38;5;66;03m# add present self-attn cache to positions 1,2 of present_key_value tuple\u001b[39;00m\n\u001b[0;32m--> 666\u001b[0m hidden_states, self_attn_weights, present_key_value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself_attn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    667\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mself_attn_past_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    673\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mfunctional\u001b[38;5;241m.\u001b[39mdropout(hidden_states, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining)\n\u001b[1;32m    674\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m hidden_states\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/bart/modeling_bart.py:450\u001b[0m, in \u001b[0;36mBartSdpaAttention.forward\u001b[0;34m(self, hidden_states, key_value_states, past_key_value, attention_mask, layer_head_mask, output_attentions)\u001b[0m\n\u001b[1;32m    447\u001b[0m bsz, tgt_len, _ \u001b[38;5;241m=\u001b[39m hidden_states\u001b[38;5;241m.\u001b[39msize()\n\u001b[1;32m    449\u001b[0m \u001b[38;5;66;03m# get query proj\u001b[39;00m\n\u001b[0;32m--> 450\u001b[0m query_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mq_proj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    451\u001b[0m \u001b[38;5;66;03m# get key, value proj\u001b[39;00m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;66;03m# `past_key_value[0].shape[2] == key_value_states.shape[1]`\u001b[39;00m\n\u001b[1;32m    453\u001b[0m \u001b[38;5;66;03m# is checking that the `sequence_length` of the `past_key_value` is the same as\u001b[39;00m\n\u001b[1;32m    454\u001b[0m \u001b[38;5;66;03m# the provided `key_value_states` to support prefix tuning\u001b[39;00m\n\u001b[1;32m    455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    456\u001b[0m     is_cross_attention\n\u001b[1;32m    457\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m past_key_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    458\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m past_key_value[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m==\u001b[39m key_value_states\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    459\u001b[0m ):\n\u001b[1;32m    460\u001b[0m     \u001b[38;5;66;03m# reuse k,v, cross_attentions\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/linear.py:116\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# List of Hugging Face model names\n",
+    "model_names = [\n",
+    "    \"facebook/bart-large-mnli\",\n",
+    "    \"MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli\",\n",
+    "    ##\"cross-encoder/nli-deberta-v3-base\",\n",
+    "    \"cross-encoder/nli-deberta-v3-large\",\n",
+    "    \"MoritzLaurer/mDeBERTa-v3-base-mnli-xnli\",\n",
+    "    ##\"joeddav/bart-large-mnli-yahoo-answers\",\n",
+    "    \"MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli\",\n",
+    "    \"MoritzLaurer/deberta-v3-large-zeroshot-v2.0\",\n",
+    "    \"valhalla/distilbart-mnli-12-1\",\n",
+    "    #\"joeddav/xlm-roberta-large-xnli\" # keeps giving errors\n",
+    "]\n",
+    "\n",
+    "# Apply each model to the test data\n",
+    "for model_name in model_names:\n",
+    "    print(f\"\\nUsing model: {model_name}\")\n",
+    "    result_list = []\n",
+    "    performance = pd.DataFrame(columns=['accuracy', 'true_ident', 'false_pred'])\n",
+    "    \n",
+    "    start_time = time.time()\n",
+    "    for i in range(len(trip_descriptions)):\n",
+    "        current_trip = trip_descriptions[i]\n",
+    "        current_type = trip_types[i]\n",
+    "        df = pred_trip(model_name, current_trip, current_type, cut_off = 0.5)\n",
+    "        print(df)\n",
+    "        # accuracy, perc true classes identified and perc wrong pred classes\n",
+    "        performance = pd.concat([performance, perf_measure(df)])\n",
+    "        print(performance)\n",
+    "        \n",
+    "        result_list.append(df)\n",
+    "    end_time = time.time()\n",
+    "    elapsed_time = end_time - start_time\n",
+    "    # Extract \"same_value\" column from each DataFrame\n",
+    "    sv_columns = [df['same_value'] for df in result_list]  # 'same' needs to be changed\n",
+    "    sv_columns.insert(0, result_list[0]['superclass'])\n",
+    "    # Combine into a new DataFrame (columns side-by-side)\n",
+    "    sv_df = pd.concat(sv_columns, axis=1)\n",
+    "    print(sv_df)\n",
+    "    # Compute accuracy per superclass (row means of same_value matrix excluding the first column)\n",
+    "    row_means = sv_df.iloc[:, 1:].mean(axis=1)\n",
+    "    df_row_means = pd.DataFrame({\n",
+    "        'superclass': sv_df['superclass'],\n",
+    "        'accuracy': row_means\n",
+    "    })\n",
+    "    print(df_row_means)\n",
+    "    # Compute performance measures per trip (mean for each column of performance table)\n",
+    "    column_means = performance.mean()\n",
+    "    print(column_means)\n",
+    "    # save results\n",
+    "    model = model_name.replace(\"/\", \"-\")\n",
+    "    model_result = {\n",
+    "        'model': model,\n",
+    "        'predictions': result_list,\n",
+    "        'performance': performance,\n",
+    "        'perf_summary': column_means,\n",
+    "        'perf_superclass': df_row_means,\n",
+    "        'elapsed_time': elapsed_time\n",
+    "    }\n",
+    "    # File path with folder\n",
+    "    filename = os.path.join('results', f'{model}_results.pkl')\n",
+    "    # Save the object\n",
+    "    with open(filename, 'wb') as f:\n",
+    "        pickle.dump(model_result, f)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1cbb54e-abe6-49b6-957e-0683196f3199",
+   "metadata": {},
+   "source": [
+    "## Load and compare results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62ca82b0-6909-4e6c-9d2c-fed87971e5b6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: cross-encoder-nli-deberta-v3-base\n",
+      "Performance Summary:\n",
+      "accuracy      0.444444\n",
+      "true_ident    0.533333\n",
+      "false_pred    0.712500\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: joeddav-bart-large-mnli-yahoo-answers\n",
+      "Performance Summary:\n",
+      "accuracy      0.344444\n",
+      "true_ident    0.650000\n",
+      "false_pred    0.553792\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: cross-encoder-nli-deberta-v3-large\n",
+      "Performance Summary:\n",
+      "accuracy      0.466667\n",
+      "true_ident    0.566667\n",
+      "false_pred    0.541667\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: MoritzLaurer-DeBERTa-v3-large-mnli-fever-anli-ling-wanli\n",
+      "Performance Summary:\n",
+      "accuracy      0.566667\n",
+      "true_ident    0.841667\n",
+      "false_pred    0.546667\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: MoritzLaurer-mDeBERTa-v3-base-mnli-xnli\n",
+      "Performance Summary:\n",
+      "accuracy      0.466667\n",
+      "true_ident    0.408333\n",
+      "false_pred    0.481250\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: MoritzLaurer-deberta-v3-large-zeroshot-v2.0\n",
+      "Performance Summary:\n",
+      "accuracy      0.455556\n",
+      "true_ident    0.325000\n",
+      "false_pred    0.500000\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: facebook-bart-large-mnli\n",
+      "Performance Summary:\n",
+      "accuracy      0.466667\n",
+      "true_ident    0.708333\n",
+      "false_pred    0.400000\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: valhalla-distilbart-mnli-12-1\n",
+      "Performance Summary:\n",
+      "accuracy      0.522222\n",
+      "true_ident    0.300000\n",
+      "false_pred    0.533333\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\n",
+      "Performance Summary:\n",
+      "accuracy      0.522222\n",
+      "true_ident    0.841667\n",
+      "false_pred    0.572381\n",
+      "dtype: float64\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Folder where .pkl files are saved\n",
+    "results_dir = 'results/before'\n",
+    "\n",
+    "# Dictionary to store all loaded results\n",
+    "all_results = {}\n",
+    "\n",
+    "# Loop through all .pkl files in the folder\n",
+    "for filename in os.listdir(results_dir):\n",
+    "    if filename.endswith('.pkl'):\n",
+    "        model_name = filename.replace('_results.pkl', '')  # Extract model name\n",
+    "        file_path = os.path.join(results_dir, filename)\n",
+    "        \n",
+    "        # Load the result\n",
+    "        with open(file_path, 'rb') as f:\n",
+    "            result = pickle.load(f)\n",
+    "            all_results[model_name] = result\n",
+    "\n",
+    "# Compare performance across models\n",
+    "for model, data in all_results.items():\n",
+    "    print(f\"Model: {model}\")\n",
+    "    print(f\"Performance Summary:\\n{data['perf_summary']}\")\n",
+    "    print(\"-\" * 40)\n",
+    "\n",
+    "# Compare performance across models\n",
+    "#for model, data in all_results.items():\n",
+    "#    print(f\"Model: {model}\")\n",
+    "#    print(f\"Performance Summary:\\n{data['perf_superclass']}\")\n",
+    "#    print(\"-\" * 40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "37849e0b-864e-4377-b06c-0ac70c3861f9",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: cross-encoder-nli-deberta-v3-base\n",
+      "Performance Summary:\n",
+      "accuracy      0.444444\n",
+      "true_ident    0.533333\n",
+      "false_pred    0.712500\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: joeddav-bart-large-mnli-yahoo-answers\n",
+      "Performance Summary:\n",
+      "accuracy      0.355556\n",
+      "true_ident    0.650000\n",
+      "false_pred    0.553792\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: cross-encoder-nli-deberta-v3-large\n",
+      "Performance Summary:\n",
+      "accuracy      0.466667\n",
+      "true_ident    0.566667\n",
+      "false_pred    0.541667\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: MoritzLaurer-DeBERTa-v3-large-mnli-fever-anli-ling-wanli\n",
+      "Performance Summary:\n",
+      "accuracy      0.611111\n",
+      "true_ident    0.841667\n",
+      "false_pred    0.546667\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: MoritzLaurer-mDeBERTa-v3-base-mnli-xnli\n",
+      "Performance Summary:\n",
+      "accuracy      0.455556\n",
+      "true_ident    0.408333\n",
+      "false_pred    0.481250\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: MoritzLaurer-deberta-v3-large-zeroshot-v2.0\n",
+      "Performance Summary:\n",
+      "accuracy      0.500\n",
+      "true_ident    0.325\n",
+      "false_pred    0.500\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: facebook-bart-large-mnli\n",
+      "Performance Summary:\n",
+      "accuracy      0.466667\n",
+      "true_ident    0.708333\n",
+      "false_pred    0.400000\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: valhalla-distilbart-mnli-12-1\n",
+      "Performance Summary:\n",
+      "accuracy      0.500000\n",
+      "true_ident    0.300000\n",
+      "false_pred    0.533333\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\n",
+      "Performance Summary:\n",
+      "accuracy      0.522222\n",
+      "true_ident    0.841667\n",
+      "false_pred    0.572381\n",
+      "dtype: float64\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Folder where .pkl files are saved\n",
+    "results_dir = 'results'\n",
+    "\n",
+    "# Dictionary to store all loaded results\n",
+    "all_results = {}\n",
+    "\n",
+    "# Loop through all .pkl files in the folder\n",
+    "for filename in os.listdir(results_dir):\n",
+    "    if filename.endswith('.pkl'):\n",
+    "        model_name = filename.replace('_results.pkl', '')  # Extract model name\n",
+    "        file_path = os.path.join(results_dir, filename)\n",
+    "        \n",
+    "        # Load the result\n",
+    "        with open(file_path, 'rb') as f:\n",
+    "            result = pickle.load(f)\n",
+    "            all_results[model_name] = result\n",
+    "\n",
+    "# Compare performance across models\n",
+    "for model, data in all_results.items():\n",
+    "    print(f\"Model: {model}\")\n",
+    "    print(f\"Performance Summary:\\n{data['perf_summary']}\")\n",
+    "    print(\"-\" * 40)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f65e5b1-bc32-42c2-bbe9-9e3a6ffc72c1",
+   "metadata": {},
+   "source": [
+    "**Identify trips that are difficult to predict**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "040055c9-5df4-49b0-921a-5bf98ff01a69",
+   "metadata": {},
+   "source": [
+    "Per model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "57fd150d-1cda-4be5-806b-ef380469243a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cross-encoder-nli-deberta-v3-base: Index([0, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')\n",
+      "\n",
+      "joeddav-bart-large-mnli-yahoo-answers: RangeIndex(start=0, stop=10, step=1)\n",
+      "\n",
+      "cross-encoder-nli-deberta-v3-large: Index([0, 1, 2, 3, 4, 6, 7, 8, 9], dtype='int64')\n",
+      "\n",
+      "MoritzLaurer-DeBERTa-v3-large-mnli-fever-anli-ling-wanli: Index([2, 3, 5, 6, 7, 8, 9], dtype='int64')\n",
+      "\n",
+      "MoritzLaurer-mDeBERTa-v3-base-mnli-xnli: RangeIndex(start=0, stop=10, step=1)\n",
+      "\n",
+      "MoritzLaurer-deberta-v3-large-zeroshot-v2.0: Index([1, 2, 3, 5, 6, 7, 9], dtype='int64')\n",
+      "\n",
+      "facebook-bart-large-mnli: RangeIndex(start=0, stop=10, step=1)\n",
+      "\n",
+      "valhalla-distilbart-mnli-12-1: Index([0, 1, 2, 3, 4, 7, 9], dtype='int64')\n",
+      "\n",
+      "MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli: Index([0, 2, 3, 4, 6, 7], dtype='int64')\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_difficult_trips(model_result, cut_off = 0.6):\n",
+    "    # model_result is a dict with dict_keys(['model', 'predictions', \n",
+    "    # 'performance', 'perf_summary', 'perf_superclass', 'elapsed_time'])\n",
+    "    # get performance dataframe and repair index\n",
+    "    df = model_result['performance'].reset_index(drop=True)\n",
+    "    # find index of trips whose accuracy is below cut_off\n",
+    "    index_result = df[df['accuracy'] < cut_off].index\n",
+    "    return(index_result)\n",
+    "\n",
+    "# dictionary of trips that have accuracy below cut_off default\n",
+    "difficult_trips_dict = {}\n",
+    "for model, data in all_results.items():\n",
+    "    difficult_trips_dict[data[\"model\"]] = get_difficult_trips(data)\n",
+    "\n",
+    "for key, value in difficult_trips_dict.items():\n",
+    "    print(f\"{key}: {value}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d91fb932-c5aa-472a-9b8d-a0cfc83a87f8",
+   "metadata": {},
+   "source": [
+    "For all models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a2754cb7-59b9-4f1d-ab74-1bf711b3eba2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 . My partner and I are traveling to the Netherlands and Germany to spend Christmas with our family. We are in our late twenties and will start our journey with a two-hour flight to the Netherlands. From there, we will take a 5.5-hour train ride to northern Germany. \n",
+      "\n",
+      "city trip\n",
+      "['relaxing']\n",
+      "cold destination / winter\n",
+      "lightweight (but comfortable)\n",
+      "casual\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "no special conditions to consider\n",
+      "7+ days\n",
+      "\n",
+      "\n",
+      "3 . I’m in my twenties and will be traveling to Peru for three weeks. I’m going solo but will meet up with a friend to explore the Sacred Valley and take part in a Machu Picchu tour. We plan to hike, go rafting, and explore the remnants of the ancient Inca Empire. We’re also excited to try Peruvian cuisine and immerse ourselves in the local culture. Depending on our plans, we might also visit the rainforest region, such as Tarapoto. I’ll be flying to Peru on a long-haul flight and will be traveling in August. \n",
+      "\n",
+      "cultural exploration\n",
+      "['sightseeing', 'hiking', 'rafting']\n",
+      "variable weather / spring / autumn\n",
+      "lightweight (but comfortable)\n",
+      "casual\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "rainy climate\n",
+      "7+ days\n",
+      "\n",
+      "\n",
+      "7 . We will go to Sweden in the winter, to go for a yoga and sauna/wellness retreat. I prefer lightweight packing and also want clothes to go for fancy dinners and maybe on a winter hike. We stay in hotels. \n",
+      "\n",
+      "yoga / wellness retreat\n",
+      "['hiking', 'yoga']\n",
+      "cold destination / winter\n",
+      "lightweight (but comfortable)\n",
+      "casual\n",
+      "indoor\n",
+      "no own vehicle\n",
+      "snow and ice\n",
+      "7 days\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Which trips are difficult for all models\n",
+    "common = set.intersection(*(set(v) for v in difficult_trips_dict.values()))\n",
+    "for index in common:\n",
+    "    print(index, \".\", trip_descriptions[index], \"\\n\")\n",
+    "    for item in trip_types[index]:\n",
+    "        print(item)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be58d66f-a491-4f47-98df-2c0aa4af38e7",
+   "metadata": {},
+   "source": [
+    "**Identify superclasses that are difficult to predict**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e833c2d-9356-4d40-9b20-0a1eb6628a30",
+   "metadata": {},
+   "source": [
+    "Per model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "adb491b1-3ac3-4c32-934f-5eb6171f2ec9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cross-encoder-nli-deberta-v3-base: ['activities', 'climate_or_season', 'style_or_comfort', 'special_conditions']\n",
+      "\n",
+      "joeddav-bart-large-mnli-yahoo-answers: ['activities', 'climate_or_season', 'style_or_comfort', 'dress_code', 'accommodation', 'transportation', 'special_conditions']\n",
+      "\n",
+      "cross-encoder-nli-deberta-v3-large: ['activities', 'climate_or_season', 'style_or_comfort', 'transportation', 'special_conditions']\n",
+      "\n",
+      "MoritzLaurer-DeBERTa-v3-large-mnli-fever-anli-ling-wanli: ['activities', 'style_or_comfort']\n",
+      "\n",
+      "MoritzLaurer-mDeBERTa-v3-base-mnli-xnli: ['activities', 'style_or_comfort', 'accommodation', 'special_conditions', 'trip_length_days']\n",
+      "\n",
+      "MoritzLaurer-deberta-v3-large-zeroshot-v2.0: ['activities', 'climate_or_season', 'style_or_comfort', 'accommodation', 'special_conditions']\n",
+      "\n",
+      "facebook-bart-large-mnli: ['activities', 'style_or_comfort', 'accommodation', 'special_conditions']\n",
+      "\n",
+      "valhalla-distilbart-mnli-12-1: ['activities', 'climate_or_season', 'style_or_comfort', 'accommodation', 'special_conditions']\n",
+      "\n",
+      "MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli: ['activities', 'climate_or_season', 'style_or_comfort', 'special_conditions']\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_difficult_superclasses(model_result, cut_off = 0.6):\n",
+    "    # model_result is a dict with dict_keys(['model', 'predictions', \n",
+    "    # 'performance', 'perf_summary', 'perf_superclass', 'elapsed_time'])\n",
+    "    df = model_result[\"perf_superclass\"]\n",
+    "    # find superclass whose accuracy is below cut_off\n",
+    "    diff_spc = list(df[df['accuracy'] < cut_off][\"superclass\"])\n",
+    "    return(diff_spc)\n",
+    "\n",
+    "# make dictionary of superclasses that have accuracy below cut_off default\n",
+    "difficult_superclass_dict = {}\n",
+    "for model, data in all_results.items():\n",
+    "    difficult_superclass_dict[data[\"model\"]] = get_difficult_superclasses(data)\n",
+    "\n",
+    "for key, value in difficult_superclass_dict.items():\n",
+    "    print(f\"{key}: {value}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbcebdf8-0975-45cb-96f5-15b4645aa7f6",
+   "metadata": {},
+   "source": [
+    "For all models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4e51c11b-9a0a-4f9d-b20c-a6feda2d5a3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'style_or_comfort', 'activities'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Which trips are difficult for all models\n",
+    "common = set.intersection(*(set(v) for v in difficult_superclass_dict.values()))\n",
+    "print(common)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f0e31e2c-e87d-4776-b781-991919492430",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Look at particular predicitons in detail\n",
+    "# print(all_results[\"joeddav-bart-large-mnli-yahoo-answers\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01e24355-4aac-4ad6-b50c-96f75585ce45",
+   "metadata": {},
+   "source": [
+    "**Comparing models**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b020f584-1468-4c84-9dac-7ca7fac6e8ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n",
+      "<class 'list'>\n",
+      "   accuracy true_ident false_pred  \\\n",
+      "0  0.444444   0.533333     0.7125   \n",
+      "1  0.355556       0.65   0.553792   \n",
+      "2  0.466667   0.566667   0.541667   \n",
+      "3  0.611111   0.841667   0.546667   \n",
+      "4  0.455556   0.408333    0.48125   \n",
+      "5       0.5      0.325        0.5   \n",
+      "6  0.466667   0.708333        0.4   \n",
+      "7       0.5        0.3   0.533333   \n",
+      "8  0.522222   0.841667   0.572381   \n",
+      "\n",
+      "                                               model  \n",
+      "0                  cross-encoder-nli-deberta-v3-base  \n",
+      "1              joeddav-bart-large-mnli-yahoo-answers  \n",
+      "2                 cross-encoder-nli-deberta-v3-large  \n",
+      "3  MoritzLaurer-DeBERTa-v3-large-mnli-fever-anli-...  \n",
+      "4            MoritzLaurer-mDeBERTa-v3-base-mnli-xnli  \n",
+      "5        MoritzLaurer-deberta-v3-large-zeroshot-v2.0  \n",
+      "6                           facebook-bart-large-mnli  \n",
+      "7                      valhalla-distilbart-mnli-12-1  \n",
+      "8       MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make table of 'perf_summary' for all models inlcude time elapsed\n",
+    "#print(type(all_results))\n",
+    "#print(type(all_results[\"MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\"]))\n",
+    "#print(all_results[\"MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\"].keys())\n",
+    "#print(type(all_results[\"MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\"][\"perf_summary\"]))\n",
+    "#print(all_results[\"MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\"][\"perf_summary\"])\n",
+    "#print(all_results[\"MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\"][\"perf_summary\"][\"accuracy\"])\n",
+    "# make empty data frame\n",
+    "perf_table = []\n",
+    "print(perf_table)\n",
+    "\n",
+    "# fill in for loop with perf_summary per model\n",
+    "for model, result in all_results.items():\n",
+    "    row = pd.DataFrame(result[\"perf_summary\"]).T\n",
+    "    #print(row.shape)\n",
+    "    row[\"model\"] = model\n",
+    "    perf_table.append(row)\n",
+    "# Concatenate all into one table\n",
+    "df_all = pd.concat(perf_table, ignore_index=True)\n",
+    "\n",
+    "print(df_all)\n",
+    "#print(type(df_all))\n",
+    "    \n",
+    "\n",
+    "# Make ranking from that table for each category\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0f7376bd-a50b-47cc-8055-48a6de5dfee6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'dict'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(all_results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17483df4-55c4-41cd-b8a9-61f7a5c7e8a3",
+   "metadata": {},
+   "source": [
+    "# Use gradio for user input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5bf23e10-0a93-4b2f-9508-34bb0974d24c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prerequisites\n",
+    "from transformers import pipeline\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "import gradio as gr\n",
+    "\n",
+    "# get candidate labels\n",
+    "with open(\"packing_label_structure.json\", \"r\") as file:\n",
+    "    candidate_labels = json.load(file)\n",
+    "keys_list = list(candidate_labels.keys())\n",
+    "\n",
+    "# Load test data (in list of dictionaries)\n",
+    "with open(\"test_data.json\", \"r\") as file:\n",
+    "    packing_data = json.load(file)\n",
+    "# Extract all trip descriptions and trip_types\n",
+    "# trip_descriptions = [trip['description'] for trip in packing_data]\n",
+    "# trip_types = [trip['trip_types'] for trip in packing_data]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "61ebbe99-2563-4c99-ba65-d2312c9d5844",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "Running on public URL: https://91493051ab28db8a5a.gradio.live\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://91493051ab28db8a5a.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# function and gradio app\n",
+    "cut_off = 0.5  # used to choose which activities are relevant\n",
+    "\n",
+    "def classify(model_name, trip_descr, cut_off):\n",
+    "    classifier = pipeline(\"zero-shot-classification\", model=model_name)\n",
+    "    # Create an empty DataFrame with specified columns\n",
+    "    df = pd.DataFrame(columns=['superclass', 'pred_class'])\n",
+    "    for i, key in enumerate(keys_list):\n",
+    "        if key == 'activities':\n",
+    "            result = classifier(trip_descr, candidate_labels[key], multi_label=True)\n",
+    "            indices = [i for i, score in enumerate(result['scores']) if score > cut_off]\n",
+    "            classes = [result['labels'][i] for i in indices]\n",
+    "        else:\n",
+    "            result = classifier(trip_descr, candidate_labels[key])\n",
+    "            classes = result[\"labels\"][0]\n",
+    "        df.loc[i] = [key, classes]\n",
+    "    return df\n",
+    "\n",
+    "demo = gr.Interface(\n",
+    "    fn=classify,\n",
+    "    inputs=[\n",
+    "        gr.Textbox(label=\"Model name\", value = \"facebook/bart-large-mnli\"),\n",
+    "        gr.Textbox(label=\"Trip description\"),\n",
+    "        gr.Number(label=\"Activity cut-off\", value = 0.5),\n",
+    "    ],\n",
+    "    outputs=\"dataframe\",\n",
+    "    title=\"Trip classification\",\n",
+    "    description=\"Enter a text describing your trip\",\n",
+    ")\n",
+    "\n",
+    "# Launch the Gradio app\n",
+    "if __name__ == \"__main__\":\n",
+    "    demo.launch(share=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (huggingface_env)",
+   "language": "python",
+   "name": "huggingface_env"
+  },
+  "language_info": {
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+    "name": "ipython",
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    "id": "3bbee2e4-55c8-4b06-9929-72026edf7932",
    "metadata": {},
    "source": [
-    "**Load and try the model**"
+    "**Load prerequisites**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 57,
    "id": "f8c28d2d-8458-49fd-8ebf-5e729d6e861f",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
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-      ]
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-     "output_type": "display_data"
-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
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-     "output_type": "display_data"
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-     "data": {
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-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -152,10 +54,14 @@
     "import matplotlib.pyplot as plt\n",
     "import pickle\n",
     "import os\n",
+    "import time\n",
     "\n",
     "# Load the model and create a pipeline for zero-shot classification (1min loading + classifying with 89 labels)\n",
-    "classifier = pipeline(\"zero-shot-classification\", model=\"MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli\")\n",
+    "classifier = pipeline(\"zero-shot-classification\", model=\"cross-encoder/nli-deberta-v3-base\")\n",
+    "model_name = 'model_cross-encoder-nli-deberta-v3-base'\n",
     "# tried:\n",
+    "# cross-encoder/nli-deberta-v3-large  gave error\n",
+    "# MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli\n",
     "# facebook/bart-large-mnli\n",
     "# sileod/deberta-v3-base-tasksource-nli\n",
     "\n",
@@ -182,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 58,
    "id": "3a762755-872d-43a6-b666-874d6133488c",
    "metadata": {},
    "outputs": [],
@@ -212,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 59,
    "id": "3b4f3193-3bdd-453c-8664-df84f955600c",
    "metadata": {},
    "outputs": [],
@@ -249,87 +155,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 60,
    "id": "4dd01755-be8d-4904-8494-ac28aba2fee7",
    "metadata": {
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['beach vacation', 'micro-adventure / weekend trip', 'cultural exploration', 'nature escape', 'digital nomad trip', 'camping trip (campground)', 'camping trip (wild camping)', 'long-distance hike / thru-hike', 'ski tour / skitour', 'hut trek (summer)', 'city trip', 'hut trek (winter)', 'road trip (car/camper)', 'festival trip', 'yoga / wellness retreat', 'snowboard / splitboard trip'], 'scores': [0.37198853492736816, 0.31496119499206543, 0.10890532284975052, 0.09102731198072433, 0.0735681876540184, 0.012933704070746899, 0.009422042407095432, 0.0051276967860758305, 0.004056071396917105, 0.0017408831045031548, 0.001503779087215662, 0.0014244643971323967, 0.0013752576196566224, 0.0009292717440985143, 0.0006881792796775699, 0.0003480584127828479]}\n",
-      "beach vacation\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['micro-adventure / weekend trip', 'digital nomad trip', 'beach vacation', 'festival trip', 'city trip', 'cultural exploration', 'road trip (car/camper)', 'camping trip (wild camping)', 'long-distance hike / thru-hike', 'hut trek (winter)', 'ski tour / skitour', 'snowboard / splitboard trip', 'nature escape', 'yoga / wellness retreat', 'hut trek (summer)', 'camping trip (campground)'], 'scores': [0.9722680449485779, 0.007802918087691069, 0.0075571718625724316, 0.0022959215566515923, 0.0021305829286575317, 0.001222927705384791, 0.0009879637509584427, 0.000805296644102782, 0.0007946204277686775, 0.0007107199053280056, 0.0007009899127297103, 0.0006353880744427443, 0.0005838185315951705, 0.0005424902774393559, 0.0004807499353773892, 0.0004804217896889895]}\n",
+      "micro-adventure / weekend trip\n",
       "0\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['relaxing', 'hiking', 'going to the beach', 'photography', 'sightseeing', 'hut-to-hut hiking', 'snorkeling', 'snowshoe hiking', 'yoga', 'stand-up paddleboarding (SUP)', 'kayaking / canoeing', 'horseback riding', 'swimming', 'paragliding', 'rafting', 'biking', 'rock climbing', 'surfing', 'running', 'ice climbing', 'cross-country skiing', 'fishing', 'ski touring', 'skiing', 'scuba diving'], 'scores': [0.9943736791610718, 0.9631249308586121, 0.9454535841941833, 0.7538902759552002, 0.4525446593761444, 0.1696157604455948, 0.05957728251814842, 0.04234873503446579, 0.01991761103272438, 0.016971556469798088, 0.006959819234907627, 0.00411367928609252, 0.0030609173700213432, 0.00186573073733598, 0.0017515394138172269, 0.00142807571683079, 0.0005748369731009007, 0.00037779140984639525, 0.0003097739245276898, 0.00030914091621525586, 0.0002725012309383601, 0.00027050732751376927, 0.00024376016517635435, 0.00017392759036738425, 0.00014787293912377208]}\n",
-      "['relaxing', 'hiking', 'going to the beach', 'photography']\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['going to the beach', 'sightseeing', 'relaxing', 'hiking', 'hut-to-hut hiking', 'stand-up paddleboarding (SUP)', 'photography', 'biking', 'running', 'ski touring', 'snowshoe hiking', 'yoga', 'kayaking / canoeing', 'horseback riding', 'rafting', 'paragliding', 'cross-country skiing', 'surfing', 'skiing', 'ice climbing', 'fishing', 'snorkeling', 'swimming', 'rock climbing', 'scuba diving'], 'scores': [0.4660525321960449, 0.007281942293047905, 0.003730606520548463, 0.0001860307966126129, 0.00014064949937164783, 0.00011034693307010457, 5.2949126256862655e-05, 3.828677654382773e-05, 3.396756437723525e-05, 1.5346524378401227e-05, 9.348185812996235e-06, 8.182429155567661e-06, 6.5973340497293975e-06, 6.271920938161202e-06, 5.544673058466287e-06, 5.299102667777333e-06, 4.855380211665761e-06, 4.506250661506783e-06, 3.949530764657538e-06, 3.730233856913401e-06, 3.297281637060223e-06, 3.0508665531669976e-06, 2.933618134193239e-06, 2.6379277642263332e-06, 2.2992651338427095e-06]}\n",
+      "[]\n",
       "1\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['tropical / humid', 'warm destination / summer', 'variable weather / spring / autumn', 'cold destination / winter', 'dry / desert-like'], 'scores': [0.4895477890968323, 0.25917261838912964, 0.24829530715942383, 0.0017174285603687167, 0.0012668712297454476]}\n",
-      "tropical / humid\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['variable weather / spring / autumn', 'warm destination / summer', 'cold destination / winter', 'dry / desert-like', 'tropical / humid'], 'scores': [0.5934922695159912, 0.17430798709392548, 0.10943299531936646, 0.07068652659654617, 0.05208020657300949]}\n",
+      "variable weather / spring / autumn\n",
       "2\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['minimalist', 'luxury (including evening wear)', 'lightweight (but comfortable)', 'ultralight'], 'scores': [0.7574900984764099, 0.09964746236801147, 0.07804173231124878, 0.06482075154781342]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['minimalist', 'ultralight', 'luxury (including evening wear)', 'lightweight (but comfortable)'], 'scores': [0.6965053081512451, 0.11270010471343994, 0.10676420480012894, 0.08403033763170242]}\n",
       "minimalist\n",
       "3\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['casual', 'conservative', 'formal (business trip)'], 'scores': [0.8163393139839172, 0.11898067593574524, 0.06467998772859573]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['casual', 'formal (business trip)', 'conservative'], 'scores': [0.6362482309341431, 0.22082458436489105, 0.14292724430561066]}\n",
       "casual\n",
       "4\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['indoor', 'huts with half board', 'sleeping in a car', 'sleeping in a tent'], 'scores': [0.6389047503471375, 0.18624886870384216, 0.13902997970581055, 0.03581654652953148]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['indoor', 'sleeping in a tent', 'huts with half board', 'sleeping in a car'], 'scores': [0.435793399810791, 0.20242486894130707, 0.19281964004039764, 0.16896207630634308]}\n",
       "indoor\n",
       "5\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['no own vehicle', 'own vehicle'], 'scores': [0.9990958571434021, 0.0009041387238539755]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['no own vehicle', 'own vehicle'], 'scores': [0.9987181425094604, 0.0012818538816645741]}\n",
       "no own vehicle\n",
-      "6\n"
+      "6\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['self-supported (bring your own food/cooking)', 'no special conditions', 'off-grid / no electricity', 'rainy climate', 'child-friendly', 'snow and ice', 'pet-friendly', 'high alpine terrain', 'avalanche-prone terrain'], 'scores': [0.1984991431236267, 0.1695038080215454, 0.16221018135547638, 0.13200421631336212, 0.12101645022630692, 0.10550825297832489, 0.042406272143125534, 0.03797775134444237, 0.030873913317918777]}\n",
+      "self-supported (bring your own food/cooking)\n",
+      "7\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['7+ days', '2 days', '1 day', '7 days', '5 days', '3 days', '6 days', '4 days'], 'scores': [0.4730822443962097, 0.1168912723660469, 0.10058756172657013, 0.0991850346326828, 0.05424537882208824, 0.053677864372730255, 0.051554784178733826, 0.050775907933712006]}\n",
+      "7+ days\n",
+      "8\n",
+      "           superclass                                    pred_class  \\\n",
+      "0       activity_type                micro-adventure / weekend trip   \n",
+      "1          activities                                            []   \n",
+      "2   climate_or_season            variable weather / spring / autumn   \n",
+      "3    style_or_comfort                                    minimalist   \n",
+      "4          dress_code                                        casual   \n",
+      "5       accommodation                                        indoor   \n",
+      "6      transportation                                no own vehicle   \n",
+      "7  special_conditions  self-supported (bring your own food/cooking)   \n",
+      "8    trip_length_days                                       7+ days   \n",
+      "\n",
+      "                                         true_class  \n",
+      "0                                    beach vacation  \n",
+      "1  [swimming, going to the beach, relaxing, hiking]  \n",
+      "2                         warm destination / summer  \n",
+      "3                     lightweight (but comfortable)  \n",
+      "4                                            casual  \n",
+      "5                                            indoor  \n",
+      "6                                    no own vehicle  \n",
+      "7                             no special conditions  \n",
+      "8                                           7+ days  \n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[39], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m current_trip \u001b[38;5;241m=\u001b[39m trip_descriptions[i]\n\u001b[1;32m      6\u001b[0m current_type \u001b[38;5;241m=\u001b[39m trip_types[i]\n\u001b[0;32m----> 7\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpred_trip\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_trip\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurrent_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcut_off\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# accuracy, perc true classes identified and perc wrong pred classes\u001b[39;00m\n",
-      "Cell \u001b[0;32mIn[37], line 14\u001b[0m, in \u001b[0;36mpred_trip\u001b[0;34m(trip_descr, trip_type, cut_off)\u001b[0m\n\u001b[1;32m     12\u001b[0m     classes \u001b[38;5;241m=\u001b[39m [result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m][i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m indices]\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 14\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mclassifier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrip_descr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcandidate_labels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m     classes \u001b[38;5;241m=\u001b[39m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28mprint\u001b[39m(result)\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/zero_shot_classification.py:206\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline.__call__\u001b[0;34m(self, sequences, *args, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnable to understand extra arguments \u001b[39m\u001b[38;5;132;01m{\u001b[39;00margs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msequences\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/base.py:1294\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterate(inputs, preprocess_params, forward_params, postprocess_params)\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ChunkPipeline):\n\u001b[0;32m-> 1294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1295\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1296\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_iterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1297\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreprocess_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpostprocess_params\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1302\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_single(inputs, preprocess_params, forward_params, postprocess_params)\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/pt_utils.py:124\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_item()\n\u001b[1;32m    123\u001b[0m \u001b[38;5;66;03m# We're out of items within a batch\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(item, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;66;03m# We now have a batch of \"inferred things\".\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/pt_utils.py:269\u001b[0m, in \u001b[0;36mPipelinePackIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m accumulator\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_last:\n\u001b[0;32m--> 269\u001b[0m     processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed, torch\u001b[38;5;241m.\u001b[39mTensor):\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/base.py:1209\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1207\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m inference_context():\n\u001b[1;32m   1208\u001b[0m         model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m-> 1209\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1210\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m   1211\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/zero_shot_classification.py:229\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline._forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(model_forward)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    228\u001b[0m     model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    232\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcandidate_label\u001b[39m\u001b[38;5;124m\"\u001b[39m: candidate_label,\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msequence\u001b[39m\u001b[38;5;124m\"\u001b[39m: sequence,\n\u001b[1;32m    234\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m: inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moutputs,\n\u001b[1;32m    236\u001b[0m }\n\u001b[1;32m    237\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model_outputs\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:1297\u001b[0m, in \u001b[0;36mDebertaV2ForSequenceClassification.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1289\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1290\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\u001b[39;00m\n\u001b[1;32m   1291\u001b[0m \u001b[38;5;124;03m    Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,\u001b[39;00m\n\u001b[1;32m   1292\u001b[0m \u001b[38;5;124;03m    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\u001b[39;00m\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;124;03m    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\u001b[39;00m\n\u001b[1;32m   1294\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1295\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1297\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeberta\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1302\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1303\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1304\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1305\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1306\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1308\u001b[0m encoder_layer \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1309\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(encoder_layer)\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:1063\u001b[0m, in \u001b[0;36mDebertaV2Model.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1053\u001b[0m     token_type_ids \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros(input_shape, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong, device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m   1055\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(\n\u001b[1;32m   1056\u001b[0m     input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m   1057\u001b[0m     token_type_ids\u001b[38;5;241m=\u001b[39mtoken_type_ids,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1060\u001b[0m     inputs_embeds\u001b[38;5;241m=\u001b[39minputs_embeds,\n\u001b[1;32m   1061\u001b[0m )\n\u001b[0;32m-> 1063\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1064\u001b[0m \u001b[43m    \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1065\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1066\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1067\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1068\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1069\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1070\u001b[0m encoded_layers \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m   1072\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_steps \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:507\u001b[0m, in \u001b[0;36mDebertaV2Encoder.forward\u001b[0;34m(self, hidden_states, attention_mask, output_hidden_states, output_attentions, query_states, relative_pos, return_dict)\u001b[0m\n\u001b[1;32m    497\u001b[0m     output_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m    498\u001b[0m         layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m    499\u001b[0m         next_kv,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    504\u001b[0m         output_attentions,\n\u001b[1;32m    505\u001b[0m     )\n\u001b[1;32m    506\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 507\u001b[0m     output_states \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    508\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnext_kv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    509\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    510\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    511\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelative_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelative_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    512\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrel_embeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    513\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    514\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    517\u001b[0m     output_states, att_m \u001b[38;5;241m=\u001b[39m output_states\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:366\u001b[0m, in \u001b[0;36mDebertaV2Layer.forward\u001b[0;34m(self, hidden_states, attention_mask, query_states, relative_pos, rel_embeddings, output_attentions)\u001b[0m\n\u001b[1;32m    364\u001b[0m     attention_output, att_matrix \u001b[38;5;241m=\u001b[39m attention_output\n\u001b[1;32m    365\u001b[0m intermediate_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintermediate(attention_output)\n\u001b[0;32m--> 366\u001b[0m layer_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput\u001b[49m\u001b[43m(\u001b[49m\u001b[43mintermediate_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattention_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    368\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (layer_output, att_matrix)\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:332\u001b[0m, in \u001b[0;36mDebertaV2Output.forward\u001b[0;34m(self, hidden_states, input_tensor)\u001b[0m\n\u001b[1;32m    331\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, hidden_states, input_tensor):\n\u001b[0;32m--> 332\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdense\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    333\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(hidden_states)\n\u001b[1;32m    334\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mLayerNorm(hidden_states \u001b[38;5;241m+\u001b[39m input_tensor)\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/linear.py:116\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[60], line 13\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# accuracy, perc true classes identified and perc wrong pred classes\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m performance \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([performance, \u001b[43mperf_measure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m])\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28mprint\u001b[39m(performance)\n\u001b[1;32m     16\u001b[0m result_list\u001b[38;5;241m.\u001b[39mappend(df)\n",
+      "Cell \u001b[0;32mIn[59], line 14\u001b[0m, in \u001b[0;36mperf_measure\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m     12\u001b[0m correct_perc \u001b[38;5;241m=\u001b[39m num_correct\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(true_class)\n\u001b[1;32m     13\u001b[0m num_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(pred_class)\n\u001b[0;32m---> 14\u001b[0m wrong_perc \u001b[38;5;241m=\u001b[39m \u001b[43m(\u001b[49m\u001b[43mnum_pred\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnum_correct\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mnum_pred\u001b[49m\n\u001b[1;32m     15\u001b[0m df_perf \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\n\u001b[1;32m     16\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m: [accuracy],\n\u001b[1;32m     17\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrue_ident\u001b[39m\u001b[38;5;124m'\u001b[39m: [correct_perc],\n\u001b[1;32m     18\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfalse_pred\u001b[39m\u001b[38;5;124m'\u001b[39m: [wrong_perc]\n\u001b[1;32m     19\u001b[0m })\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m(df_perf)\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
    ],
    "source": [
     "result_list = []\n",
     "performance = pd.DataFrame(columns=['accuracy', 'true_ident', 'false_pred'])\n",
-    " \n",
+    "\n",
+    "start_time = time.time()\n",
+    "\n",
     "for i in range(len(trip_descriptions)):\n",
     "    current_trip = trip_descriptions[i]\n",
     "    current_type = trip_types[i]\n",
@@ -340,7 +244,11 @@
     "    performance = pd.concat([performance, perf_measure(df)])\n",
     "    print(performance)\n",
     "    \n",
-    "    result_list.append(df)"
+    "    result_list.append(df)\n",
+    "\n",
+    "end_time = time.time()\n",
+    "\n",
+    "elapsed_time = end_time - start_time"
    ]
   },
   {
@@ -353,10 +261,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "id": "eb33fd31-94e6-40b5-9c36-a32effe77c01",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[61], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Extract \"same_value\" column from each DataFrame\u001b[39;00m\n\u001b[1;32m      2\u001b[0m sv_columns \u001b[38;5;241m=\u001b[39m [df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msame_value\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m df \u001b[38;5;129;01min\u001b[39;00m result_list]  \u001b[38;5;66;03m# 'same' needs to be changed\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m sv_columns\u001b[38;5;241m.\u001b[39minsert(\u001b[38;5;241m0\u001b[39m, \u001b[43mresult_list\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msuperclass\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Combine into a new DataFrame (columns side-by-side)\u001b[39;00m\n\u001b[1;32m      6\u001b[0m sv_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(sv_columns, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
    "source": [
     "# Extract \"same_value\" column from each DataFrame\n",
     "sv_columns = [df['same_value'] for df in result_list]  # 'same' needs to be changed\n",
@@ -370,10 +290,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "bf7546cb-79ce-49ad-8cee-54d02239220c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           superclass  accuracy\n",
+      "0       activity_type       0.8\n",
+      "1          activities       0.0\n",
+      "2   climate_or_season       0.5\n",
+      "3    style_or_comfort       0.3\n",
+      "4          dress_code       0.8\n",
+      "5       accommodation       0.8\n",
+      "6      transportation       0.7\n",
+      "7  special_conditions       0.2\n",
+      "8    trip_length_days       0.6\n"
+     ]
+    }
+   ],
    "source": [
     "# Compute accuracy per superclass (row means of same_value matrix excluding the first column)\n",
     "row_means = sv_df.iloc[:, 1:].mean(axis=1)\n",
@@ -413,15 +350,14 @@
    "outputs": [],
    "source": [
     "# save results\n",
-    "# Example data for one model\n",
-    "model_name = 'model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli'\n",
     "# Structure to save\n",
     "model_result = {\n",
     "    'model': model_name,\n",
     "    'predictions': result_list,\n",
     "    'performance': performance,\n",
     "    'perf_summary': column_means,\n",
-    "    'perf_superclass': df_row_means\n",
+    "    'perf_superclass': df_row_means,\n",
+    "    'elapsed_time': elapsed_time\n",
     "}\n",
     "\n",
     "# File path with folder\n",
@@ -432,6 +368,16 @@
     "    pickle.dump(model_result, f)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f38d0924-30b6-43cd-9bfc-fe5b0dc80411",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(elapsed_time/60)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e1cbb54e-abe6-49b6-957e-0683196f3199",
@@ -442,14 +388,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 54,
    "id": "62ca82b0-6909-4e6c-9d2c-fed87971e5b6",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Model: model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\n",
+      "Performance Summary:\n",
+      "accuracy      0.522222\n",
+      "true_ident    0.841667\n",
+      "false_pred    0.572381\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
       "Model: model_a_facebook-bart-large-mnli\n",
       "Performance Summary:\n",
       "accuracy      0.454545\n",
@@ -464,6 +419,19 @@
       "false_pred    0.551667\n",
       "dtype: float64\n",
       "----------------------------------------\n",
+      "Model: model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\n",
+      "Performance Summary:\n",
+      "           superclass  accuracy\n",
+      "0       activity_type       0.8\n",
+      "1          activities       0.0\n",
+      "2   climate_or_season       0.5\n",
+      "3    style_or_comfort       0.3\n",
+      "4          dress_code       0.8\n",
+      "5       accommodation       0.8\n",
+      "6      transportation       0.7\n",
+      "7  special_conditions       0.2\n",
+      "8    trip_length_days       0.6\n",
+      "----------------------------------------\n",
       "Model: model_a_facebook-bart-large-mnli\n",
       "Performance Summary:\n",
       "           superclass  accuracy\n",
@@ -511,20 +479,57 @@
     "            result = pickle.load(f)\n",
     "            all_results[model_name] = result\n",
     "\n",
-    "# Now you can compare performance across models\n",
+    "# Compare performance across models\n",
     "for model, data in all_results.items():\n",
     "    print(f\"Model: {model}\")\n",
     "    print(f\"Performance Summary:\\n{data['perf_summary']}\")\n",
     "    print(\"-\" * 40)\n",
     "\n",
     "\n",
-    "# Now you can compare performance across models\n",
+    "# Compare performance across models\n",
     "for model, data in all_results.items():\n",
     "    print(f\"Model: {model}\")\n",
     "    print(f\"Performance Summary:\\n{data['perf_superclass']}\")\n",
     "    print(\"-\" * 40)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "57fd150d-1cda-4be5-806b-ef380469243a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\n",
+      "Time in minutes for 10 trips:\n",
+      "83.45150986512502\n",
+      "----------------------------------------\n",
+      "Model: model_a_facebook-bart-large-mnli\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'elapsed_time'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[69], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m model, data \u001b[38;5;129;01min\u001b[39;00m all_results\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime in minutes for 10 trips:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mdata[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124melapsed_time\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m60\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m40\u001b[39m)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'elapsed_time'"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare across models\n",
+    "for model, data in all_results.items():\n",
+    "    print(f\"Model: {model}\")\n",
+    "    print(f\"Time in minutes for 10 trips:\\n{data['elapsed_time']/60}\")\n",
+    "    print(\"-\" * 40)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "17483df4-55c4-41cd-b8a9-61f7a5c7e8a3",

space/space/space/space/space/gradio_tryout.ipynb

@@ -19,12 +19,12 @@
    "id": "3bbee2e4-55c8-4b06-9929-72026edf7932",
    "metadata": {},
    "source": [
-    "**Load and try the model**"
+    "**Load prerequisites**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 57,
    "id": "f8c28d2d-8458-49fd-8ebf-5e729d6e861f",
    "metadata": {},
    "outputs": [
@@ -57,8 +57,11 @@
     "import time\n",
     "\n",
     "# Load the model and create a pipeline for zero-shot classification (1min loading + classifying with 89 labels)\n",
-    "classifier = pipeline(\"zero-shot-classification\", model=\"MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli\")\n",
+    "classifier = pipeline(\"zero-shot-classification\", model=\"cross-encoder/nli-deberta-v3-base\")\n",
+    "model_name = 'model_cross-encoder-nli-deberta-v3-base'\n",
     "# tried:\n",
+    "# cross-encoder/nli-deberta-v3-large  gave error\n",
+    "# MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli\n",
     "# facebook/bart-large-mnli\n",
     "# sileod/deberta-v3-base-tasksource-nli\n",
     "\n",
@@ -85,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 58,
    "id": "3a762755-872d-43a6-b666-874d6133488c",
    "metadata": {},
    "outputs": [],
@@ -115,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 59,
    "id": "3b4f3193-3bdd-453c-8664-df84f955600c",
    "metadata": {},
    "outputs": [],
@@ -152,60 +155,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 60,
    "id": "4dd01755-be8d-4904-8494-ac28aba2fee7",
    "metadata": {
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['beach vacation', 'micro-adventure / weekend trip', 'cultural exploration', 'nature escape', 'digital nomad trip', 'camping trip (campground)', 'camping trip (wild camping)', 'long-distance hike / thru-hike', 'ski tour / skitour', 'hut trek (summer)', 'city trip', 'hut trek (winter)', 'road trip (car/camper)', 'festival trip', 'yoga / wellness retreat', 'snowboard / splitboard trip'], 'scores': [0.37198853492736816, 0.31496119499206543, 0.10890532284975052, 0.09102731198072433, 0.0735681876540184, 0.012933704070746899, 0.009422042407095432, 0.0051276967860758305, 0.004056071396917105, 0.0017408831045031548, 0.001503779087215662, 0.0014244643971323967, 0.0013752576196566224, 0.0009292717440985143, 0.0006881792796775699, 0.0003480584127828479]}\n",
-      "beach vacation\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['micro-adventure / weekend trip', 'digital nomad trip', 'beach vacation', 'festival trip', 'city trip', 'cultural exploration', 'road trip (car/camper)', 'camping trip (wild camping)', 'long-distance hike / thru-hike', 'hut trek (winter)', 'ski tour / skitour', 'snowboard / splitboard trip', 'nature escape', 'yoga / wellness retreat', 'hut trek (summer)', 'camping trip (campground)'], 'scores': [0.9722680449485779, 0.007802918087691069, 0.0075571718625724316, 0.0022959215566515923, 0.0021305829286575317, 0.001222927705384791, 0.0009879637509584427, 0.000805296644102782, 0.0007946204277686775, 0.0007107199053280056, 0.0007009899127297103, 0.0006353880744427443, 0.0005838185315951705, 0.0005424902774393559, 0.0004807499353773892, 0.0004804217896889895]}\n",
+      "micro-adventure / weekend trip\n",
       "0\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['relaxing', 'hiking', 'going to the beach', 'photography', 'sightseeing', 'hut-to-hut hiking', 'snorkeling', 'snowshoe hiking', 'yoga', 'stand-up paddleboarding (SUP)', 'kayaking / canoeing', 'horseback riding', 'swimming', 'paragliding', 'rafting', 'biking', 'rock climbing', 'surfing', 'running', 'ice climbing', 'cross-country skiing', 'fishing', 'ski touring', 'skiing', 'scuba diving'], 'scores': [0.9943736791610718, 0.9631249308586121, 0.9454535841941833, 0.7538902759552002, 0.4525446593761444, 0.1696157604455948, 0.05957728251814842, 0.04234873503446579, 0.01991761103272438, 0.016971556469798088, 0.006959819234907627, 0.00411367928609252, 0.0030609173700213432, 0.00186573073733598, 0.0017515394138172269, 0.00142807571683079, 0.0005748369731009007, 0.00037779140984639525, 0.0003097739245276898, 0.00030914091621525586, 0.0002725012309383601, 0.00027050732751376927, 0.00024376016517635435, 0.00017392759036738425, 0.00014787293912377208]}\n",
-      "['relaxing', 'hiking', 'going to the beach', 'photography']\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['going to the beach', 'sightseeing', 'relaxing', 'hiking', 'hut-to-hut hiking', 'stand-up paddleboarding (SUP)', 'photography', 'biking', 'running', 'ski touring', 'snowshoe hiking', 'yoga', 'kayaking / canoeing', 'horseback riding', 'rafting', 'paragliding', 'cross-country skiing', 'surfing', 'skiing', 'ice climbing', 'fishing', 'snorkeling', 'swimming', 'rock climbing', 'scuba diving'], 'scores': [0.4660525321960449, 0.007281942293047905, 0.003730606520548463, 0.0001860307966126129, 0.00014064949937164783, 0.00011034693307010457, 5.2949126256862655e-05, 3.828677654382773e-05, 3.396756437723525e-05, 1.5346524378401227e-05, 9.348185812996235e-06, 8.182429155567661e-06, 6.5973340497293975e-06, 6.271920938161202e-06, 5.544673058466287e-06, 5.299102667777333e-06, 4.855380211665761e-06, 4.506250661506783e-06, 3.949530764657538e-06, 3.730233856913401e-06, 3.297281637060223e-06, 3.0508665531669976e-06, 2.933618134193239e-06, 2.6379277642263332e-06, 2.2992651338427095e-06]}\n",
+      "[]\n",
       "1\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['tropical / humid', 'warm destination / summer', 'variable weather / spring / autumn', 'cold destination / winter', 'dry / desert-like'], 'scores': [0.4895477890968323, 0.25917261838912964, 0.24829530715942383, 0.0017174285603687167, 0.0012668712297454476]}\n",
-      "tropical / humid\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['variable weather / spring / autumn', 'warm destination / summer', 'cold destination / winter', 'dry / desert-like', 'tropical / humid'], 'scores': [0.5934922695159912, 0.17430798709392548, 0.10943299531936646, 0.07068652659654617, 0.05208020657300949]}\n",
+      "variable weather / spring / autumn\n",
       "2\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['minimalist', 'luxury (including evening wear)', 'lightweight (but comfortable)', 'ultralight'], 'scores': [0.7574900984764099, 0.09964746236801147, 0.07804173231124878, 0.06482075154781342]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['minimalist', 'ultralight', 'luxury (including evening wear)', 'lightweight (but comfortable)'], 'scores': [0.6965053081512451, 0.11270010471343994, 0.10676420480012894, 0.08403033763170242]}\n",
       "minimalist\n",
       "3\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['casual', 'conservative', 'formal (business trip)'], 'scores': [0.8163393139839172, 0.11898067593574524, 0.06467998772859573]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['casual', 'formal (business trip)', 'conservative'], 'scores': [0.6362482309341431, 0.22082458436489105, 0.14292724430561066]}\n",
       "casual\n",
       "4\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['indoor', 'huts with half board', 'sleeping in a car', 'sleeping in a tent'], 'scores': [0.6389047503471375, 0.18624886870384216, 0.13902997970581055, 0.03581654652953148]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['indoor', 'sleeping in a tent', 'huts with half board', 'sleeping in a car'], 'scores': [0.435793399810791, 0.20242486894130707, 0.19281964004039764, 0.16896207630634308]}\n",
       "indoor\n",
       "5\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['no own vehicle', 'own vehicle'], 'scores': [0.9990958571434021, 0.0009041387238539755]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['no own vehicle', 'own vehicle'], 'scores': [0.9987181425094604, 0.0012818538816645741]}\n",
       "no own vehicle\n",
       "6\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['off-grid / no electricity', 'self-supported (bring your own food/cooking)', 'child-friendly', 'no special conditions', 'pet-friendly', 'rainy climate', 'avalanche-prone terrain', 'high alpine terrain', 'snow and ice'], 'scores': [0.7414510250091553, 0.07683143764734268, 0.055722303688526154, 0.054133761674165726, 0.04852374270558357, 0.006977608893066645, 0.005693929269909859, 0.005599685944616795, 0.005066512618213892]}\n",
-      "off-grid / no electricity\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['self-supported (bring your own food/cooking)', 'no special conditions', 'off-grid / no electricity', 'rainy climate', 'child-friendly', 'snow and ice', 'pet-friendly', 'high alpine terrain', 'avalanche-prone terrain'], 'scores': [0.1984991431236267, 0.1695038080215454, 0.16221018135547638, 0.13200421631336212, 0.12101645022630692, 0.10550825297832489, 0.042406272143125534, 0.03797775134444237, 0.030873913317918777]}\n",
+      "self-supported (bring your own food/cooking)\n",
       "7\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['7+ days', '2 days', '7 days', '3 days', '4 days', '6 days', '5 days', '1 day'], 'scores': [0.29225289821624756, 0.20232954621315002, 0.1837582290172577, 0.13940994441509247, 0.06562349200248718, 0.04916509613394737, 0.040249694138765335, 0.0272111464291811]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['7+ days', '2 days', '1 day', '7 days', '5 days', '3 days', '6 days', '4 days'], 'scores': [0.4730822443962097, 0.1168912723660469, 0.10058756172657013, 0.0991850346326828, 0.05424537882208824, 0.053677864372730255, 0.051554784178733826, 0.050775907933712006]}\n",
       "7+ days\n",
       "8\n",
-      "           superclass                                         pred_class  \\\n",
-      "0       activity_type                                     beach vacation   \n",
-      "1          activities  [relaxing, hiking, going to the beach, photogr...   \n",
-      "2   climate_or_season                                   tropical / humid   \n",
-      "3    style_or_comfort                                         minimalist   \n",
-      "4          dress_code                                             casual   \n",
-      "5       accommodation                                             indoor   \n",
-      "6      transportation                                     no own vehicle   \n",
-      "7  special_conditions                          off-grid / no electricity   \n",
-      "8    trip_length_days                                            7+ days   \n",
+      "           superclass                                    pred_class  \\\n",
+      "0       activity_type                micro-adventure / weekend trip   \n",
+      "1          activities                                            []   \n",
+      "2   climate_or_season            variable weather / spring / autumn   \n",
+      "3    style_or_comfort                                    minimalist   \n",
+      "4          dress_code                                        casual   \n",
+      "5       accommodation                                        indoor   \n",
+      "6      transportation                                no own vehicle   \n",
+      "7  special_conditions  self-supported (bring your own food/cooking)   \n",
+      "8    trip_length_days                                       7+ days   \n",
       "\n",
       "                                         true_class  \n",
       "0                                    beach vacation  \n",
@@ -216,51 +212,19 @@
       "5                                            indoor  \n",
       "6                                    no own vehicle  \n",
       "7                             no special conditions  \n",
-      "8                                           7+ days  \n",
-      "   accuracy  true_ident  false_pred\n",
-      "0  0.555556        0.75        0.25\n",
-      "{'sequence': 'We are a couple in our thirties traveling to Vienna for a three-day city trip. We’ll be staying at a friend’s house and plan to explore the city by sightseeing, strolling through the streets, visiting markets, and trying out great restaurants and cafés. We also hope to attend a classical music concert. Our journey to Vienna will be by train.', 'labels': ['city trip', 'cultural exploration', 'micro-adventure / weekend trip', 'ski tour / skitour', 'festival trip', 'digital nomad trip', 'hut trek (winter)', 'camping trip (campground)', 'long-distance hike / thru-hike', 'hut trek (summer)', 'nature escape', 'camping trip (wild camping)', 'yoga / wellness retreat', 'road trip (car/camper)', 'beach vacation', 'snowboard / splitboard trip'], 'scores': [0.517789363861084, 0.297355592250824, 0.1621870994567871, 0.006185388192534447, 0.005294559057801962, 0.002764208009466529, 0.001503965351730585, 0.0014866390265524387, 0.0012240204960107803, 0.0012071850942447782, 0.000757778063416481, 0.0006650012801401317, 0.0005547589971683919, 0.00043604226084426045, 0.00031738984398543835, 0.0002710542466957122]}\n",
-      "city trip\n",
-      "0\n"
+      "8                                           7+ days  \n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[43], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m current_trip \u001b[38;5;241m=\u001b[39m trip_descriptions[i]\n\u001b[1;32m      6\u001b[0m current_type \u001b[38;5;241m=\u001b[39m trip_types[i]\n\u001b[0;32m----> 7\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpred_trip\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_trip\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurrent_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcut_off\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# accuracy, perc true classes identified and perc wrong pred classes\u001b[39;00m\n",
-      "Cell \u001b[0;32mIn[41], line 10\u001b[0m, in \u001b[0;36mpred_trip\u001b[0;34m(trip_descr, trip_type, cut_off)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(keys_list):\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mactivities\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 10\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mclassifier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrip_descr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcandidate_labels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m         indices \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i, score \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscores\u001b[39m\u001b[38;5;124m'\u001b[39m]) \u001b[38;5;28;01mif\u001b[39;00m score \u001b[38;5;241m>\u001b[39m cut_off]\n\u001b[1;32m     12\u001b[0m         classes \u001b[38;5;241m=\u001b[39m [result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m][i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m indices]\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/zero_shot_classification.py:206\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline.__call__\u001b[0;34m(self, sequences, *args, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnable to understand extra arguments \u001b[39m\u001b[38;5;132;01m{\u001b[39;00margs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msequences\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/base.py:1294\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterate(inputs, preprocess_params, forward_params, postprocess_params)\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ChunkPipeline):\n\u001b[0;32m-> 1294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1295\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1296\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_iterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1297\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreprocess_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpostprocess_params\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1302\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_single(inputs, preprocess_params, forward_params, postprocess_params)\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/pt_utils.py:124\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_item()\n\u001b[1;32m    123\u001b[0m \u001b[38;5;66;03m# We're out of items within a batch\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(item, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;66;03m# We now have a batch of \"inferred things\".\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/pt_utils.py:269\u001b[0m, in \u001b[0;36mPipelinePackIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m accumulator\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_last:\n\u001b[0;32m--> 269\u001b[0m     processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed, torch\u001b[38;5;241m.\u001b[39mTensor):\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/base.py:1209\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1207\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m inference_context():\n\u001b[1;32m   1208\u001b[0m         model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m-> 1209\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1210\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m   1211\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/zero_shot_classification.py:229\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline._forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(model_forward)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    228\u001b[0m     model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    232\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcandidate_label\u001b[39m\u001b[38;5;124m\"\u001b[39m: candidate_label,\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msequence\u001b[39m\u001b[38;5;124m\"\u001b[39m: sequence,\n\u001b[1;32m    234\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m: inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moutputs,\n\u001b[1;32m    236\u001b[0m }\n\u001b[1;32m    237\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model_outputs\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:1297\u001b[0m, in \u001b[0;36mDebertaV2ForSequenceClassification.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1289\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1290\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\u001b[39;00m\n\u001b[1;32m   1291\u001b[0m \u001b[38;5;124;03m    Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,\u001b[39;00m\n\u001b[1;32m   1292\u001b[0m \u001b[38;5;124;03m    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\u001b[39;00m\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;124;03m    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\u001b[39;00m\n\u001b[1;32m   1294\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1295\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1297\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeberta\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1302\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1303\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1304\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1305\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1306\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1308\u001b[0m encoder_layer \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1309\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(encoder_layer)\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:1063\u001b[0m, in \u001b[0;36mDebertaV2Model.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1053\u001b[0m     token_type_ids \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros(input_shape, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong, device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m   1055\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(\n\u001b[1;32m   1056\u001b[0m     input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m   1057\u001b[0m     token_type_ids\u001b[38;5;241m=\u001b[39mtoken_type_ids,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1060\u001b[0m     inputs_embeds\u001b[38;5;241m=\u001b[39minputs_embeds,\n\u001b[1;32m   1061\u001b[0m )\n\u001b[0;32m-> 1063\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1064\u001b[0m \u001b[43m    \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1065\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1066\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1067\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1068\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1069\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1070\u001b[0m encoded_layers \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m   1072\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_steps \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:507\u001b[0m, in \u001b[0;36mDebertaV2Encoder.forward\u001b[0;34m(self, hidden_states, attention_mask, output_hidden_states, output_attentions, query_states, relative_pos, return_dict)\u001b[0m\n\u001b[1;32m    497\u001b[0m     output_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m    498\u001b[0m         layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m    499\u001b[0m         next_kv,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    504\u001b[0m         output_attentions,\n\u001b[1;32m    505\u001b[0m     )\n\u001b[1;32m    506\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 507\u001b[0m     output_states \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    508\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnext_kv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    509\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    510\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    511\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelative_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelative_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    512\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrel_embeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    513\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    514\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    517\u001b[0m     output_states, att_m \u001b[38;5;241m=\u001b[39m output_states\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:355\u001b[0m, in \u001b[0;36mDebertaV2Layer.forward\u001b[0;34m(self, hidden_states, attention_mask, query_states, relative_pos, rel_embeddings, output_attentions)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    348\u001b[0m     hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    353\u001b[0m     output_attentions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    354\u001b[0m ):\n\u001b[0;32m--> 355\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    356\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    357\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    358\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    359\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    360\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelative_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelative_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    361\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrel_embeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    362\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    363\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    364\u001b[0m         attention_output, att_matrix \u001b[38;5;241m=\u001b[39m attention_output\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:286\u001b[0m, in \u001b[0;36mDebertaV2Attention.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions, query_states, relative_pos, rel_embeddings)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    278\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    279\u001b[0m     hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    284\u001b[0m     rel_embeddings\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    285\u001b[0m ):\n\u001b[0;32m--> 286\u001b[0m     self_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    287\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    289\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    290\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    291\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelative_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelative_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    292\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrel_embeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    293\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    294\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    295\u001b[0m         self_output, att_matrix \u001b[38;5;241m=\u001b[39m self_output\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:700\u001b[0m, in \u001b[0;36mDisentangledSelfAttention.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions, query_states, relative_pos, rel_embeddings)\u001b[0m\n\u001b[1;32m    698\u001b[0m     query_states \u001b[38;5;241m=\u001b[39m hidden_states\n\u001b[1;32m    699\u001b[0m query_layer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtranspose_for_scores(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquery_proj(query_states), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_attention_heads)\n\u001b[0;32m--> 700\u001b[0m key_layer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtranspose_for_scores(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey_proj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_attention_heads)\n\u001b[1;32m    701\u001b[0m value_layer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtranspose_for_scores(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue_proj(hidden_states), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_attention_heads)\n\u001b[1;32m    703\u001b[0m rel_att \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/linear.py:116\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[60], line 13\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# accuracy, perc true classes identified and perc wrong pred classes\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m performance \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([performance, \u001b[43mperf_measure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m])\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28mprint\u001b[39m(performance)\n\u001b[1;32m     16\u001b[0m result_list\u001b[38;5;241m.\u001b[39mappend(df)\n",
+      "Cell \u001b[0;32mIn[59], line 14\u001b[0m, in \u001b[0;36mperf_measure\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m     12\u001b[0m correct_perc \u001b[38;5;241m=\u001b[39m num_correct\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(true_class)\n\u001b[1;32m     13\u001b[0m num_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(pred_class)\n\u001b[0;32m---> 14\u001b[0m wrong_perc \u001b[38;5;241m=\u001b[39m \u001b[43m(\u001b[49m\u001b[43mnum_pred\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnum_correct\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mnum_pred\u001b[49m\n\u001b[1;32m     15\u001b[0m df_perf \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\n\u001b[1;32m     16\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m: [accuracy],\n\u001b[1;32m     17\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrue_ident\u001b[39m\u001b[38;5;124m'\u001b[39m: [correct_perc],\n\u001b[1;32m     18\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfalse_pred\u001b[39m\u001b[38;5;124m'\u001b[39m: [wrong_perc]\n\u001b[1;32m     19\u001b[0m })\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m(df_perf)\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
    ],
@@ -269,6 +233,7 @@
     "performance = pd.DataFrame(columns=['accuracy', 'true_ident', 'false_pred'])\n",
     "\n",
     "start_time = time.time()\n",
+    "\n",
     "for i in range(len(trip_descriptions)):\n",
     "    current_trip = trip_descriptions[i]\n",
     "    current_type = trip_types[i]\n",
@@ -279,7 +244,11 @@
     "    performance = pd.concat([performance, perf_measure(df)])\n",
     "    print(performance)\n",
     "    \n",
-    "    result_list.append(df)"
+    "    result_list.append(df)\n",
+    "\n",
+    "end_time = time.time()\n",
+    "\n",
+    "elapsed_time = end_time - start_time"
    ]
   },
   {
@@ -292,10 +261,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "id": "eb33fd31-94e6-40b5-9c36-a32effe77c01",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[61], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Extract \"same_value\" column from each DataFrame\u001b[39;00m\n\u001b[1;32m      2\u001b[0m sv_columns \u001b[38;5;241m=\u001b[39m [df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msame_value\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m df \u001b[38;5;129;01min\u001b[39;00m result_list]  \u001b[38;5;66;03m# 'same' needs to be changed\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m sv_columns\u001b[38;5;241m.\u001b[39minsert(\u001b[38;5;241m0\u001b[39m, \u001b[43mresult_list\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msuperclass\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Combine into a new DataFrame (columns side-by-side)\u001b[39;00m\n\u001b[1;32m      6\u001b[0m sv_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(sv_columns, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
    "source": [
     "# Extract \"same_value\" column from each DataFrame\n",
     "sv_columns = [df['same_value'] for df in result_list]  # 'same' needs to be changed\n",
@@ -309,10 +290,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "bf7546cb-79ce-49ad-8cee-54d02239220c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           superclass  accuracy\n",
+      "0       activity_type       0.8\n",
+      "1          activities       0.0\n",
+      "2   climate_or_season       0.5\n",
+      "3    style_or_comfort       0.3\n",
+      "4          dress_code       0.8\n",
+      "5       accommodation       0.8\n",
+      "6      transportation       0.7\n",
+      "7  special_conditions       0.2\n",
+      "8    trip_length_days       0.6\n"
+     ]
+    }
+   ],
    "source": [
     "# Compute accuracy per superclass (row means of same_value matrix excluding the first column)\n",
     "row_means = sv_df.iloc[:, 1:].mean(axis=1)\n",
@@ -352,15 +350,14 @@
    "outputs": [],
    "source": [
     "# save results\n",
-    "# Example data for one model\n",
-    "model_name = 'model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli'\n",
     "# Structure to save\n",
     "model_result = {\n",
     "    'model': model_name,\n",
     "    'predictions': result_list,\n",
     "    'performance': performance,\n",
     "    'perf_summary': column_means,\n",
-    "    'perf_superclass': df_row_means\n",
+    "    'perf_superclass': df_row_means,\n",
+    "    'elapsed_time': elapsed_time\n",
     "}\n",
     "\n",
     "# File path with folder\n",
@@ -371,6 +368,16 @@
     "    pickle.dump(model_result, f)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f38d0924-30b6-43cd-9bfc-fe5b0dc80411",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(elapsed_time/60)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e1cbb54e-abe6-49b6-957e-0683196f3199",
@@ -381,14 +388,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 54,
    "id": "62ca82b0-6909-4e6c-9d2c-fed87971e5b6",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Model: model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\n",
+      "Performance Summary:\n",
+      "accuracy      0.522222\n",
+      "true_ident    0.841667\n",
+      "false_pred    0.572381\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
       "Model: model_a_facebook-bart-large-mnli\n",
       "Performance Summary:\n",
       "accuracy      0.454545\n",
@@ -403,6 +419,19 @@
       "false_pred    0.551667\n",
       "dtype: float64\n",
       "----------------------------------------\n",
+      "Model: model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\n",
+      "Performance Summary:\n",
+      "           superclass  accuracy\n",
+      "0       activity_type       0.8\n",
+      "1          activities       0.0\n",
+      "2   climate_or_season       0.5\n",
+      "3    style_or_comfort       0.3\n",
+      "4          dress_code       0.8\n",
+      "5       accommodation       0.8\n",
+      "6      transportation       0.7\n",
+      "7  special_conditions       0.2\n",
+      "8    trip_length_days       0.6\n",
+      "----------------------------------------\n",
       "Model: model_a_facebook-bart-large-mnli\n",
       "Performance Summary:\n",
       "           superclass  accuracy\n",
@@ -450,20 +479,57 @@
     "            result = pickle.load(f)\n",
     "            all_results[model_name] = result\n",
     "\n",
-    "# Now you can compare performance across models\n",
+    "# Compare performance across models\n",
     "for model, data in all_results.items():\n",
     "    print(f\"Model: {model}\")\n",
     "    print(f\"Performance Summary:\\n{data['perf_summary']}\")\n",
     "    print(\"-\" * 40)\n",
     "\n",
     "\n",
-    "# Now you can compare performance across models\n",
+    "# Compare performance across models\n",
     "for model, data in all_results.items():\n",
     "    print(f\"Model: {model}\")\n",
     "    print(f\"Performance Summary:\\n{data['perf_superclass']}\")\n",
     "    print(\"-\" * 40)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "57fd150d-1cda-4be5-806b-ef380469243a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli\n",
+      "Time in minutes for 10 trips:\n",
+      "83.45150986512502\n",
+      "----------------------------------------\n",
+      "Model: model_a_facebook-bart-large-mnli\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'elapsed_time'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[69], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m model, data \u001b[38;5;129;01min\u001b[39;00m all_results\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime in minutes for 10 trips:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mdata[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124melapsed_time\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m60\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m40\u001b[39m)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'elapsed_time'"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare across models\n",
+    "for model, data in all_results.items():\n",
+    "    print(f\"Model: {model}\")\n",
+    "    print(f\"Time in minutes for 10 trips:\\n{data['elapsed_time']/60}\")\n",
+    "    print(\"-\" * 40)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "17483df4-55c4-41cd-b8a9-61f7a5c7e8a3",

space/space/space/space/space/packing_label_structure.json

@@ -49,7 +49,8 @@
     "warm destination / summer",
     "variable weather / spring / autumn",
     "tropical / humid",
-    "dry / desert-like"
+    "dry / desert-like",
+    "rainy climate"
   ],
   "style_or_comfort": [
     "ultralight",
@@ -74,14 +75,13 @@
   ],
   "special_conditions": [
     "off-grid / no electricity",
-    "self-supported (bring your own food/cooking)",
-    "child-friendly",
+    "self-supported (bring your own cooking gear)",
+    "travel with children",
     "pet-friendly",
-    "rainy climate",
     "snow and ice",
     "high alpine terrain",
-    "avalanche-prone terrain",
-    "no special conditions"  
+    "snow, ice and avalanche-prone terrain",
+    "no special conditions to consider"  
   ],
   "trip_length_days": [
     "1 day",

space/space/space/space/space/packing_templates_self_supported_offgrid_expanded.json

@@ -535,7 +535,7 @@
     "USB-hub (voor meerdere devices)",
     "verpakking om elektronica droog te houden"
   ],
-  "self-supported (bring your own food/cooking)": [
+  "self-supported (bring your own cooking gear)": [
     "lichtgewicht kooktoestel (gas, benzine of alcohol)",
     "brandstof (voldoende voor aantal dagen)",
     "pan of keteltje",
@@ -552,7 +552,7 @@
     "minstens 2 liter wateropslag per persoon",
     "food bag of hangzak voor voedsel (wild-safe)"
   ],
-  "child-friendly": [
+  "travel with children": [
     "snacks en speelgoed",
     "EHBO-set met pleisters",
     "extra kleding",
@@ -606,7 +606,7 @@
     "extra voeding",
     "EHBO-kit"
   ],
-  "avalanche-prone terrain": [
+  "snow, ice and avalanche-prone terrain": [
     "lawinepieper",
     "schep",
     "sonde",

space/space/space/space/space/results/model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli_results.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd1d8f5ed1d8b9a0878aa0e99fa144341c27d48bcf71d77390e28951e4936149
+size 9539

space/space/space/space/space/space/results/model_b_sileod-deberta-v3-base-tasksource-nli_results.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98f1217c7add9665e814d4ef90a8e1a60e33d53ea81c6c98472bc652aa11ee56
+size 9461

space/space/space/space/space/space/space/packing_label_hierarchical_mapping.json

@@ -247,11 +247,11 @@
     "category": "accommodation",
     "superclass": "type"
   },
-  "transportation: vehicle": {
+  "transportation: own vehicle": {
     "category": "transportation",
     "superclass": "mode"
   },
-  "transportation: no vehicle": {
+  "transportation: no own vehicle": {
     "category": "transportation",
     "superclass": "mode"
   },

space/space/space/space/space/space/space/packing_label_structure.json

@@ -69,8 +69,8 @@
     "sleeping in a car"
   ],
   "transportation": [
-    "vehicle",
-    "no vehicle"
+    "own vehicle",
+    "no own vehicle"
   ],
   "special_conditions": [
     "off-grid / no electricity",

space/space/space/space/space/space/space/packing_templates_self_supported_offgrid_expanded.json

@@ -507,14 +507,14 @@
     "snacks voor de nacht",
     "thermische deken (voor koude nachten)"
   ],
-  "vehicle": [
+  "own vehicle": [
     "rijbewijs",
     "autopapieren",
     "EHBO-set",
     "navigatie of smartphone",
     "telefoonhouder"
   ],
-  "no vehicle": [
+  "no own vehicle": [
     "rugzak",
     "waterfles",
     "lichte schoenen",

space/space/space/space/space/space/space/results/model_a_facebook-bart-large-mnli_results.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f5c3b28d1fb60f40fa22ebbe9a32e10f6a81b61beaf376c620eabc6912a72e7
+size 9445

space/space/space/space/space/space/space/space/gradio_tryout.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 40,
    "id": "f8c28d2d-8458-49fd-8ebf-5e729d6e861f",
    "metadata": {},
    "outputs": [
@@ -39,9 +39,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "First trip: I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands. \n",
+      "First trip: We are a couple in our thirties traveling to Vienna for a three-day city trip. We’ll be staying at a friend’s house and plan to explore the city by sightseeing, strolling through the streets, visiting markets, and trying out great restaurants and cafés. We also hope to attend a classical music concert. Our journey to Vienna will be by train. \n",
       "\n",
-      "Trip type: ['beach vacation', ['swimming', 'going to the beach', 'relaxing', 'hiking'], 'warm destination / summer', 'lightweight (but comfortable)', 'casual', 'indoor', 'no vehicle', 'no special conditions', '7 days']\n"
+      "Trip type: ['city trip', ['sightseeing'], 'variable weather / spring / autumn', 'luxury (including evening wear)', 'casual', 'indoor', 'no own vehicle', 'no special condition', '3 days']\n"
      ]
     }
    ],
@@ -51,9 +51,16 @@
     "from transformers import pipeline\n",
     "import json\n",
     "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pickle\n",
+    "import os\n",
+    "import time\n",
     "\n",
     "# Load the model and create a pipeline for zero-shot classification (1min loading + classifying with 89 labels)\n",
-    "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")\n",
+    "classifier = pipeline(\"zero-shot-classification\", model=\"MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli\")\n",
+    "# tried:\n",
+    "# facebook/bart-large-mnli\n",
+    "# sileod/deberta-v3-base-tasksource-nli\n",
     "\n",
     "# get candidate labels\n",
     "with open(\"packing_label_structure.json\", \"r\") as file:\n",
@@ -68,9 +75,9 @@
     "trip_types = [trip['trip_types'] for trip in packing_data]\n",
     "\n",
     "# Access the first trip description\n",
-    "first_trip = trip_descriptions[0]\n",
+    "first_trip = trip_descriptions[1]\n",
     "# Get the packing list for the secondfirst trip\n",
-    "first_trip_type = trip_types[0]\n",
+    "first_trip_type = trip_types[1]\n",
     "\n",
     "print(f\"First trip: {first_trip} \\n\")\n",
     "print(f\"Trip type: {first_trip_type}\")"
@@ -78,196 +85,385 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "fed1f8bc-5baf-46e7-8763-9d56fb9c536b",
+   "execution_count": 41,
+   "id": "3a762755-872d-43a6-b666-874d6133488c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function that returns pandas data frame with predictions\n",
+    "\n",
+    "cut_off = 0.5  # used to choose which activities are relevant\n",
+    "\n",
+    "def pred_trip(trip_descr, trip_type, cut_off):\n",
+    "    # Create an empty DataFrame with specified columns\n",
+    "    df = pd.DataFrame(columns=['superclass', 'pred_class'])\n",
+    "    for i, key in enumerate(keys_list):\n",
+    "        if key == 'activities':\n",
+    "            result = classifier(trip_descr, candidate_labels[key], multi_label=True)\n",
+    "            indices = [i for i, score in enumerate(result['scores']) if score > cut_off]\n",
+    "            classes = [result['labels'][i] for i in indices]\n",
+    "        else:\n",
+    "            result = classifier(trip_descr, candidate_labels[key])\n",
+    "            classes = result[\"labels\"][0]\n",
+    "        print(result)\n",
+    "        print(classes)\n",
+    "        print(i)\n",
+    "        df.loc[i] = [key, classes]\n",
+    "    df['true_class'] = trip_type\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "3b4f3193-3bdd-453c-8664-df84f955600c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function for accuracy, perc true classes identified and perc wrong pred classes\n",
+    "\n",
+    "def perf_measure(df):\n",
+    "    df['same_value'] = df['pred_class'] == df['true_class']\n",
+    "    correct = sum(df.loc[df.index != 1, 'same_value'])\n",
+    "    total = len(df['same_value'])\n",
+    "    accuracy = correct/total\n",
+    "    pred_class = df.loc[df.index == 1, 'pred_class'].iloc[0]\n",
+    "    true_class = df.loc[df.index == 1, 'true_class'].iloc[0]\n",
+    "    correct = [label for label in pred_class if label in true_class]\n",
+    "    num_correct = len(correct)\n",
+    "    correct_perc = num_correct/len(true_class)\n",
+    "    num_pred = len(pred_class)\n",
+    "    wrong_perc = (num_pred - num_correct)/num_pred\n",
+    "    df_perf = pd.DataFrame({\n",
+    "    'accuracy': [accuracy],\n",
+    "    'true_ident': [correct_perc],\n",
+    "    'false_pred': [wrong_perc]\n",
+    "    })\n",
+    "    return(df_perf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62c5c18c-58f4-465c-a188-c57cfa7ffa90",
+   "metadata": {},
+   "source": [
+    "**Now do the same for all trips**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "4dd01755-be8d-4904-8494-ac28aba2fee7",
    "metadata": {
     "scrolled": true
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['beach vacation', 'micro-adventure / weekend trip', 'nature escape', 'digital nomad trip', 'cultural exploration', 'yoga / wellness retreat', 'festival trip', 'long-distance hike / thru-hike', 'hut trek (summer)', 'city trip', 'road trip (car/camper)', 'ski tour / skitour', 'camping trip (campground)', 'snowboard / splitboard trip', 'camping trip (wild camping)', 'hut trek (winter)'], 'scores': [0.37631064653396606, 0.35016775131225586, 0.13397355377674103, 0.031636204570531845, 0.031270742416381836, 0.012846449390053749, 0.012699575163424015, 0.009526746347546577, 0.008148356340825558, 0.007793044205754995, 0.006512156222015619, 0.005669699050486088, 0.0044484627433121204, 0.004113250877708197, 0.002713854657486081, 0.002169555053114891]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['beach vacation', 'micro-adventure / weekend trip', 'cultural exploration', 'nature escape', 'digital nomad trip', 'camping trip (campground)', 'camping trip (wild camping)', 'long-distance hike / thru-hike', 'ski tour / skitour', 'hut trek (summer)', 'city trip', 'hut trek (winter)', 'road trip (car/camper)', 'festival trip', 'yoga / wellness retreat', 'snowboard / splitboard trip'], 'scores': [0.37198853492736816, 0.31496119499206543, 0.10890532284975052, 0.09102731198072433, 0.0735681876540184, 0.012933704070746899, 0.009422042407095432, 0.0051276967860758305, 0.004056071396917105, 0.0017408831045031548, 0.001503779087215662, 0.0014244643971323967, 0.0013752576196566224, 0.0009292717440985143, 0.0006881792796775699, 0.0003480584127828479]}\n",
       "beach vacation\n",
       "0\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['going to the beach', 'relaxing', 'hiking', 'swimming', 'sightseeing', 'running', 'hut-to-hut hiking', 'biking', 'photography', 'surfing', 'stand-up paddleboarding (SUP)', 'snorkeling', 'yoga', 'kayaking / canoeing', 'rock climbing', 'fishing', 'paragliding', 'rafting', 'horseback riding', 'snowshoe hiking', 'cross-country skiing', 'ice climbing', 'skiing', 'scuba diving', 'ski touring'], 'scores': [0.9914858341217041, 0.9771362543106079, 0.9426282048225403, 0.21901991963386536, 0.17586199939250946, 0.09854521602392197, 0.08370419591665268, 0.03679152950644493, 0.03668990358710289, 0.03099300153553486, 0.025300050154328346, 0.021451234817504883, 0.011070131324231625, 0.0075112744234502316, 0.006306737195700407, 0.0034973458386957645, 0.002655829070135951, 0.00197031581774354, 0.0015599008183926344, 0.001527810120023787, 0.0015017405385151505, 0.0014336870517581701, 0.0011686616344377398, 0.000789369223639369, 0.0004912536824122071]}\n",
-      "['going to the beach', 'relaxing', 'hiking']\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['relaxing', 'hiking', 'going to the beach', 'photography', 'sightseeing', 'hut-to-hut hiking', 'snorkeling', 'snowshoe hiking', 'yoga', 'stand-up paddleboarding (SUP)', 'kayaking / canoeing', 'horseback riding', 'swimming', 'paragliding', 'rafting', 'biking', 'rock climbing', 'surfing', 'running', 'ice climbing', 'cross-country skiing', 'fishing', 'ski touring', 'skiing', 'scuba diving'], 'scores': [0.9943736791610718, 0.9631249308586121, 0.9454535841941833, 0.7538902759552002, 0.4525446593761444, 0.1696157604455948, 0.05957728251814842, 0.04234873503446579, 0.01991761103272438, 0.016971556469798088, 0.006959819234907627, 0.00411367928609252, 0.0030609173700213432, 0.00186573073733598, 0.0017515394138172269, 0.00142807571683079, 0.0005748369731009007, 0.00037779140984639525, 0.0003097739245276898, 0.00030914091621525586, 0.0002725012309383601, 0.00027050732751376927, 0.00024376016517635435, 0.00017392759036738425, 0.00014787293912377208]}\n",
+      "['relaxing', 'hiking', 'going to the beach', 'photography']\n",
       "1\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['warm destination / summer', 'tropical / humid', 'variable weather / spring / autumn', 'dry / desert-like', 'cold destination / winter'], 'scores': [0.6468702554702759, 0.19999535381793976, 0.09394325315952301, 0.05279730260372162, 0.0063938056118786335]}\n",
-      "warm destination / summer\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['tropical / humid', 'warm destination / summer', 'variable weather / spring / autumn', 'cold destination / winter', 'dry / desert-like'], 'scores': [0.4895477890968323, 0.25917261838912964, 0.24829530715942383, 0.0017174285603687167, 0.0012668712297454476]}\n",
+      "tropical / humid\n",
       "2\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['minimalist', 'ultralight', 'lightweight (but comfortable)', 'luxury (including evening wear)'], 'scores': [0.4286234974861145, 0.2564568817615509, 0.2147122174501419, 0.10020739585161209]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['minimalist', 'luxury (including evening wear)', 'lightweight (but comfortable)', 'ultralight'], 'scores': [0.7574900984764099, 0.09964746236801147, 0.07804173231124878, 0.06482075154781342]}\n",
       "minimalist\n",
       "3\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['casual', 'conservative', 'formal (business trip)'], 'scores': [0.6567223072052002, 0.3034382164478302, 0.039839524775743484]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['casual', 'conservative', 'formal (business trip)'], 'scores': [0.8163393139839172, 0.11898067593574524, 0.06467998772859573]}\n",
       "casual\n",
       "4\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['huts with half board', 'indoor', 'sleeping in a car', 'sleeping in a tent'], 'scores': [0.5007699728012085, 0.34074831008911133, 0.10416240990161896, 0.05431929975748062]}\n",
-      "huts with half board\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['indoor', 'huts with half board', 'sleeping in a car', 'sleeping in a tent'], 'scores': [0.6389047503471375, 0.18624886870384216, 0.13902997970581055, 0.03581654652953148]}\n",
+      "indoor\n",
       "5\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['vehicle', 'no vehicle'], 'scores': [0.7521055936813354, 0.24789436161518097]}\n",
-      "vehicle\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['no own vehicle', 'own vehicle'], 'scores': [0.9990958571434021, 0.0009041387238539755]}\n",
+      "no own vehicle\n",
       "6\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['off-grid / no electricity', 'self-supported (bring your own food/cooking)', 'no special conditions', 'pet-friendly', 'rainy climate', 'child-friendly', 'snow and ice', 'high alpine terrain', 'avalanche-prone terrain'], 'scores': [0.46220096945762634, 0.12957870960235596, 0.10651793330907822, 0.09777138382196426, 0.06722460687160492, 0.0632496327161789, 0.04952802509069443, 0.015049820765852928, 0.008878983557224274]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['off-grid / no electricity', 'self-supported (bring your own food/cooking)', 'child-friendly', 'no special conditions', 'pet-friendly', 'rainy climate', 'avalanche-prone terrain', 'high alpine terrain', 'snow and ice'], 'scores': [0.7414510250091553, 0.07683143764734268, 0.055722303688526154, 0.054133761674165726, 0.04852374270558357, 0.006977608893066645, 0.005693929269909859, 0.005599685944616795, 0.005066512618213892]}\n",
       "off-grid / no electricity\n",
       "7\n",
-      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['7+ days', '2 days', '1 day', '6 days', '3 days', '4 days', '7 days', '5 days'], 'scores': [0.21139054000377655, 0.18512114882469177, 0.14520084857940674, 0.0976138487458229, 0.094282366335392, 0.09376301616430283, 0.09161651134490967, 0.08101171255111694]}\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['7+ days', '2 days', '7 days', '3 days', '4 days', '6 days', '5 days', '1 day'], 'scores': [0.29225289821624756, 0.20232954621315002, 0.1837582290172577, 0.13940994441509247, 0.06562349200248718, 0.04916509613394737, 0.040249694138765335, 0.0272111464291811]}\n",
       "7+ days\n",
-      "8\n"
+      "8\n",
+      "           superclass                                         pred_class  \\\n",
+      "0       activity_type                                     beach vacation   \n",
+      "1          activities  [relaxing, hiking, going to the beach, photogr...   \n",
+      "2   climate_or_season                                   tropical / humid   \n",
+      "3    style_or_comfort                                         minimalist   \n",
+      "4          dress_code                                             casual   \n",
+      "5       accommodation                                             indoor   \n",
+      "6      transportation                                     no own vehicle   \n",
+      "7  special_conditions                          off-grid / no electricity   \n",
+      "8    trip_length_days                                            7+ days   \n",
+      "\n",
+      "                                         true_class  \n",
+      "0                                    beach vacation  \n",
+      "1  [swimming, going to the beach, relaxing, hiking]  \n",
+      "2                         warm destination / summer  \n",
+      "3                     lightweight (but comfortable)  \n",
+      "4                                            casual  \n",
+      "5                                            indoor  \n",
+      "6                                    no own vehicle  \n",
+      "7                             no special conditions  \n",
+      "8                                           7+ days  \n",
+      "   accuracy  true_ident  false_pred\n",
+      "0  0.555556        0.75        0.25\n",
+      "{'sequence': 'We are a couple in our thirties traveling to Vienna for a three-day city trip. We’ll be staying at a friend’s house and plan to explore the city by sightseeing, strolling through the streets, visiting markets, and trying out great restaurants and cafés. We also hope to attend a classical music concert. Our journey to Vienna will be by train.', 'labels': ['city trip', 'cultural exploration', 'micro-adventure / weekend trip', 'ski tour / skitour', 'festival trip', 'digital nomad trip', 'hut trek (winter)', 'camping trip (campground)', 'long-distance hike / thru-hike', 'hut trek (summer)', 'nature escape', 'camping trip (wild camping)', 'yoga / wellness retreat', 'road trip (car/camper)', 'beach vacation', 'snowboard / splitboard trip'], 'scores': [0.517789363861084, 0.297355592250824, 0.1621870994567871, 0.006185388192534447, 0.005294559057801962, 0.002764208009466529, 0.001503965351730585, 0.0014866390265524387, 0.0012240204960107803, 0.0012071850942447782, 0.000757778063416481, 0.0006650012801401317, 0.0005547589971683919, 0.00043604226084426045, 0.00031738984398543835, 0.0002710542466957122]}\n",
+      "city trip\n",
+      "0\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[43], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m current_trip \u001b[38;5;241m=\u001b[39m trip_descriptions[i]\n\u001b[1;32m      6\u001b[0m current_type \u001b[38;5;241m=\u001b[39m trip_types[i]\n\u001b[0;32m----> 7\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpred_trip\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_trip\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurrent_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcut_off\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# accuracy, perc true classes identified and perc wrong pred classes\u001b[39;00m\n",
+      "Cell \u001b[0;32mIn[41], line 10\u001b[0m, in \u001b[0;36mpred_trip\u001b[0;34m(trip_descr, trip_type, cut_off)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(keys_list):\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mactivities\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 10\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mclassifier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrip_descr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcandidate_labels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m         indices \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i, score \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscores\u001b[39m\u001b[38;5;124m'\u001b[39m]) \u001b[38;5;28;01mif\u001b[39;00m score \u001b[38;5;241m>\u001b[39m cut_off]\n\u001b[1;32m     12\u001b[0m         classes \u001b[38;5;241m=\u001b[39m [result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m][i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m indices]\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/zero_shot_classification.py:206\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline.__call__\u001b[0;34m(self, sequences, *args, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnable to understand extra arguments \u001b[39m\u001b[38;5;132;01m{\u001b[39;00margs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msequences\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/base.py:1294\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterate(inputs, preprocess_params, forward_params, postprocess_params)\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ChunkPipeline):\n\u001b[0;32m-> 1294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1295\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1296\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_iterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1297\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreprocess_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpostprocess_params\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1302\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_single(inputs, preprocess_params, forward_params, postprocess_params)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/pt_utils.py:124\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_item()\n\u001b[1;32m    123\u001b[0m \u001b[38;5;66;03m# We're out of items within a batch\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(item, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;66;03m# We now have a batch of \"inferred things\".\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/pt_utils.py:269\u001b[0m, in \u001b[0;36mPipelinePackIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m accumulator\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_last:\n\u001b[0;32m--> 269\u001b[0m     processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed, torch\u001b[38;5;241m.\u001b[39mTensor):\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/base.py:1209\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1207\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m inference_context():\n\u001b[1;32m   1208\u001b[0m         model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m-> 1209\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1210\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m   1211\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/pipelines/zero_shot_classification.py:229\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline._forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(model_forward)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    228\u001b[0m     model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    232\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcandidate_label\u001b[39m\u001b[38;5;124m\"\u001b[39m: candidate_label,\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msequence\u001b[39m\u001b[38;5;124m\"\u001b[39m: sequence,\n\u001b[1;32m    234\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m: inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moutputs,\n\u001b[1;32m    236\u001b[0m }\n\u001b[1;32m    237\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model_outputs\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:1297\u001b[0m, in \u001b[0;36mDebertaV2ForSequenceClassification.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1289\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1290\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\u001b[39;00m\n\u001b[1;32m   1291\u001b[0m \u001b[38;5;124;03m    Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,\u001b[39;00m\n\u001b[1;32m   1292\u001b[0m \u001b[38;5;124;03m    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\u001b[39;00m\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;124;03m    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\u001b[39;00m\n\u001b[1;32m   1294\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1295\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1297\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeberta\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1302\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1303\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1304\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1305\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1306\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1308\u001b[0m encoder_layer \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1309\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(encoder_layer)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:1063\u001b[0m, in \u001b[0;36mDebertaV2Model.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1053\u001b[0m     token_type_ids \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros(input_shape, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong, device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m   1055\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(\n\u001b[1;32m   1056\u001b[0m     input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m   1057\u001b[0m     token_type_ids\u001b[38;5;241m=\u001b[39mtoken_type_ids,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1060\u001b[0m     inputs_embeds\u001b[38;5;241m=\u001b[39minputs_embeds,\n\u001b[1;32m   1061\u001b[0m )\n\u001b[0;32m-> 1063\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1064\u001b[0m \u001b[43m    \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1065\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1066\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1067\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1068\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1069\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1070\u001b[0m encoded_layers \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m   1072\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_steps \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:507\u001b[0m, in \u001b[0;36mDebertaV2Encoder.forward\u001b[0;34m(self, hidden_states, attention_mask, output_hidden_states, output_attentions, query_states, relative_pos, return_dict)\u001b[0m\n\u001b[1;32m    497\u001b[0m     output_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m    498\u001b[0m         layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m    499\u001b[0m         next_kv,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    504\u001b[0m         output_attentions,\n\u001b[1;32m    505\u001b[0m     )\n\u001b[1;32m    506\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 507\u001b[0m     output_states \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    508\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnext_kv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    509\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    510\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    511\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelative_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelative_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    512\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrel_embeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    513\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    514\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    517\u001b[0m     output_states, att_m \u001b[38;5;241m=\u001b[39m output_states\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:355\u001b[0m, in \u001b[0;36mDebertaV2Layer.forward\u001b[0;34m(self, hidden_states, attention_mask, query_states, relative_pos, rel_embeddings, output_attentions)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    348\u001b[0m     hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    353\u001b[0m     output_attentions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    354\u001b[0m ):\n\u001b[0;32m--> 355\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    356\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    357\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    358\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    359\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    360\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelative_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelative_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    361\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrel_embeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    362\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    363\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    364\u001b[0m         attention_output, att_matrix \u001b[38;5;241m=\u001b[39m attention_output\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:286\u001b[0m, in \u001b[0;36mDebertaV2Attention.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions, query_states, relative_pos, rel_embeddings)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    278\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    279\u001b[0m     hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    284\u001b[0m     rel_embeddings\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    285\u001b[0m ):\n\u001b[0;32m--> 286\u001b[0m     self_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    287\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    289\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    290\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    291\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelative_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelative_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    292\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrel_embeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    293\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    294\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    295\u001b[0m         self_output, att_matrix \u001b[38;5;241m=\u001b[39m self_output\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:700\u001b[0m, in \u001b[0;36mDisentangledSelfAttention.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions, query_states, relative_pos, rel_embeddings)\u001b[0m\n\u001b[1;32m    698\u001b[0m     query_states \u001b[38;5;241m=\u001b[39m hidden_states\n\u001b[1;32m    699\u001b[0m query_layer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtranspose_for_scores(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquery_proj(query_states), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_attention_heads)\n\u001b[0;32m--> 700\u001b[0m key_layer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtranspose_for_scores(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey_proj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_attention_heads)\n\u001b[1;32m    701\u001b[0m value_layer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtranspose_for_scores(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue_proj(hidden_states), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_attention_heads)\n\u001b[1;32m    703\u001b[0m rel_att \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/huggingface_env/lib/python3.8/site-packages/torch/nn/modules/linear.py:116\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
    "source": [
-    "# Create an empty DataFrame with specified columns\n",
-    "df = pd.DataFrame(columns=['superclass', 'pred_class'])\n",
-    "cutoff = 0.5  # used to choose which activities are relevant\n",
+    "result_list = []\n",
+    "performance = pd.DataFrame(columns=['accuracy', 'true_ident', 'false_pred'])\n",
+    "\n",
+    "start_time = time.time()\n",
+    "for i in range(len(trip_descriptions)):\n",
+    "    current_trip = trip_descriptions[i]\n",
+    "    current_type = trip_types[i]\n",
+    "    df = pred_trip(current_trip, current_type, cut_off = 0.5)\n",
+    "    print(df)\n",
+    "    \n",
+    "    # accuracy, perc true classes identified and perc wrong pred classes\n",
+    "    performance = pd.concat([performance, perf_measure(df)])\n",
+    "    print(performance)\n",
+    "    \n",
+    "    result_list.append(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5c08703-7166-4d03-9d6b-ee2c12608134",
+   "metadata": {},
+   "source": [
+    "**Compute average performance measures**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb33fd31-94e6-40b5-9c36-a32effe77c01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract \"same_value\" column from each DataFrame\n",
+    "sv_columns = [df['same_value'] for df in result_list]  # 'same' needs to be changed\n",
+    "sv_columns.insert(0, result_list[0]['superclass'])\n",
     "\n",
-    "# fill DataFrame\n",
-    "for i, key in enumerate(keys_list):\n",
-    "    # Run the classification (ca 30 seconds classifying)\n",
-    "    if key == 'activities':\n",
-    "        result = classifier(first_trip, candidate_labels[key], multi_label=True)\n",
-    "        indices = [i for i, score in enumerate(result['scores']) if score > cutoff]\n",
-    "        classes = [result['labels'][i] for i in indices]\n",
-    "    else:\n",
-    "        result = classifier(first_trip, candidate_labels[key])\n",
-    "        classes = result[\"labels\"][0]\n",
-    "    print(result)\n",
-    "    print(classes)\n",
-    "    print(i)\n",
-    "    df.loc[i] = [key, classes]\n",
+    "# Combine into a new DataFrame (columns side-by-side)\n",
+    "sv_df = pd.concat(sv_columns, axis=1)\n",
     "\n",
-    "df['true_class'] = first_trip_type"
+    "print(sv_df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "b3b51280-76a1-4229-a9de-070b925d3463",
+   "execution_count": null,
+   "id": "bf7546cb-79ce-49ad-8cee-54d02239220c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute accuracy per superclass (row means of same_value matrix excluding the first column)\n",
+    "row_means = sv_df.iloc[:, 1:].mean(axis=1)\n",
+    "\n",
+    "df_row_means = pd.DataFrame({\n",
+    "    'superclass': sv_df['superclass'],\n",
+    "    'accuracy': row_means\n",
+    "})\n",
+    "\n",
+    "print(df_row_means)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd232953-59e8-4f28-9ce8-11515a2c310b",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "           Superclass                              pred_class                                        true_class\n",
-      "0       activity_type                          beach vacation                                    beach vacation\n",
-      "1          activities  [going to the beach, relaxing, hiking]  [swimming, going to the beach, relaxing, hiking]\n",
-      "2   climate_or_season               warm destination / summer                         warm destination / summer\n",
-      "3    style_or_comfort                              minimalist                     lightweight (but comfortable)\n",
-      "4          dress_code                                  casual                                            casual\n",
-      "5       accommodation                    huts with half board                                            indoor\n",
-      "6      transportation                                 vehicle                                        no vehicle\n",
-      "7  special_conditions               off-grid / no electricity                             no special conditions\n",
-      "8    trip_length_days                                 7+ days                                            7 days\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "pd.set_option('display.width', 1000) \n",
-    "pd.set_option('display.max_columns', None)\n",
-    "print(df)"
+    "# Compute performance measures per trip (mean for each column of performance table)\n",
+    "column_means = performance.mean()\n",
+    "print(column_means)\n",
+    "\n",
+    "# Plot histograms for all numeric columns\n",
+    "performance.hist(bins=10, figsize=(10, 6))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "2ec09e8f-75f5-45b1-b4c0-4fafd685d36b",
+   "execution_count": null,
+   "id": "bd682c84-3eb1-4a8d-9621-b741e98e4537",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "           Superclass                              pred_class                                        true_class   same\n",
-      "0       activity_type                          beach vacation                                    beach vacation   True\n",
-      "1          activities  [going to the beach, relaxing, hiking]  [swimming, going to the beach, relaxing, hiking]  False\n",
-      "2   climate_or_season               warm destination / summer                         warm destination / summer   True\n",
-      "3    style_or_comfort                              minimalist                     lightweight (but comfortable)  False\n",
-      "4          dress_code                                  casual                                            casual   True\n",
-      "5       accommodation                    huts with half board                                            indoor  False\n",
-      "6      transportation                                 vehicle                                        no vehicle  False\n",
-      "7  special_conditions               off-grid / no electricity                             no special conditions  False\n",
-      "8    trip_length_days                                 7+ days                                            7 days  False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df['same'] = df['pred_class'] == df['true_class']\n",
-    "print(df)"
+    "# save results\n",
+    "# Example data for one model\n",
+    "model_name = 'model_MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli'\n",
+    "# Structure to save\n",
+    "model_result = {\n",
+    "    'model': model_name,\n",
+    "    'predictions': result_list,\n",
+    "    'performance': performance,\n",
+    "    'perf_summary': column_means,\n",
+    "    'perf_superclass': df_row_means\n",
+    "}\n",
+    "\n",
+    "# File path with folder\n",
+    "filename = os.path.join('results', f'{model_name}_results.pkl')\n",
+    "\n",
+    "# Save the object\n",
+    "with open(filename, 'wb') as f:\n",
+    "    pickle.dump(model_result, f)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "82ae19c8-8bb7-4f7f-841b-1cb6501a17a7",
+   "cell_type": "markdown",
+   "id": "e1cbb54e-abe6-49b6-957e-0683196f3199",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy (excluding activities): 0.3333333333333333\n"
-     ]
-    }
-   ],
    "source": [
-    "# accuracy excluding activities\n",
-    "correct = sum(df.loc[df.index != 1, 'same'])\n",
-    "total = len(df['same'])\n",
-    "accuracy = correct/total\n",
-    "print(\"Accuracy (excluding activities):\", accuracy)"
+    "**Load and compare results**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "id": "16c0a3ae-34ac-49a4-b59f-411a6f0ce947",
+   "execution_count": 35,
+   "id": "62ca82b0-6909-4e6c-9d2c-fed87971e5b6",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Percentage of true classes that were identified: 0.75\n",
-      "Percentage of predicted classes that were wrong: 0.0\n"
+      "Model: model_a_facebook-bart-large-mnli\n",
+      "Performance Summary:\n",
+      "accuracy      0.454545\n",
+      "true_ident    0.689394\n",
+      "false_pred    0.409091\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: model_b_sileod-deberta-v3-base-tasksource-nli\n",
+      "Performance Summary:\n",
+      "accuracy      0.500000\n",
+      "true_ident    0.666667\n",
+      "false_pred    0.551667\n",
+      "dtype: float64\n",
+      "----------------------------------------\n",
+      "Model: model_a_facebook-bart-large-mnli\n",
+      "Performance Summary:\n",
+      "           superclass  accuracy\n",
+      "0       activity_type       0.8\n",
+      "1          activities       0.0\n",
+      "2   climate_or_season       0.6\n",
+      "3    style_or_comfort       0.4\n",
+      "4          dress_code       0.7\n",
+      "5       accommodation       0.3\n",
+      "6      transportation       0.8\n",
+      "7  special_conditions       0.0\n",
+      "8    trip_length_days       0.5\n",
+      "----------------------------------------\n",
+      "Model: model_b_sileod-deberta-v3-base-tasksource-nli\n",
+      "Performance Summary:\n",
+      "           superclass  accuracy\n",
+      "0       activity_type       0.7\n",
+      "1          activities       0.1\n",
+      "2   climate_or_season       0.6\n",
+      "3    style_or_comfort       0.4\n",
+      "4          dress_code       0.6\n",
+      "5       accommodation       0.9\n",
+      "6      transportation       0.7\n",
+      "7  special_conditions       0.1\n",
+      "8    trip_length_days       0.5\n",
+      "----------------------------------------\n"
      ]
     }
    ],
    "source": [
-    "pred_class = df.loc[df.index == 1, 'pred_class'].iloc[0]\n",
-    "true_class = df.loc[df.index == 1, 'true_class'].iloc[0]\n",
-    "correct = [label for label in pred_class if label in true_class]\n",
+    "# Folder where your .pkl files are saved\n",
+    "results_dir = 'results'\n",
     "\n",
-    "num_correct = len(correct)\n",
-    "correct_perc = num_correct/len(true_class)\n",
+    "# Dictionary to store all loaded results\n",
+    "all_results = {}\n",
     "\n",
-    "num_pred = len(pred_class)\n",
-    "wrong_perc = (num_pred - num_correct)/num_pred\n",
+    "# Loop through all .pkl files in the folder\n",
+    "for filename in os.listdir(results_dir):\n",
+    "    if filename.endswith('.pkl'):\n",
+    "        model_name = filename.replace('_results.pkl', '')  # Extract model name\n",
+    "        file_path = os.path.join(results_dir, filename)\n",
+    "        \n",
+    "        # Load the result\n",
+    "        with open(file_path, 'rb') as f:\n",
+    "            result = pickle.load(f)\n",
+    "            all_results[model_name] = result\n",
     "\n",
-    "print(\"Percentage of true classes that were identified:\", correct_perc)\n",
-    "print(\"Percentage of predicted classes that were wrong:\", wrong_perc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "62c5c18c-58f4-465c-a188-c57cfa7ffa90",
-   "metadata": {},
-   "source": [
-    "Now do the same for all trips"
+    "# Now you can compare performance across models\n",
+    "for model, data in all_results.items():\n",
+    "    print(f\"Model: {model}\")\n",
+    "    print(f\"Performance Summary:\\n{data['perf_summary']}\")\n",
+    "    print(\"-\" * 40)\n",
+    "\n",
+    "\n",
+    "# Now you can compare performance across models\n",
+    "for model, data in all_results.items():\n",
+    "    print(f\"Model: {model}\")\n",
+    "    print(f\"Performance Summary:\\n{data['perf_superclass']}\")\n",
+    "    print(\"-\" * 40)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e4192b42-f1bc-4fcb-a238-dbdb3df7d699",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "17483df4-55c4-41cd-b8a9-61f7a5c7e8a3",

space/space/space/space/space/space/space/space/space/space/space/gradio_tryout.ipynb

@@ -8,59 +8,359 @@
     "# Try out gradio"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "afd23321-1870-44af-82ed-bb241d055dfa",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "3bbee2e4-55c8-4b06-9929-72026edf7932",
    "metadata": {},
    "source": [
-    "Try model"
+    "**Load and try the model**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f8c28d2d-8458-49fd-8ebf-5e729d6e861f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First trip: I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands. \n",
+      "\n",
+      "Trip type: ['beach vacation', ['swimming', 'going to the beach', 'relaxing', 'hiking'], 'warm destination / summer', 'lightweight (but comfortable)', 'casual', 'indoor', 'no vehicle', 'no special conditions', '7 days']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prerequisites\n",
+    "from tabulate import tabulate\n",
+    "from transformers import pipeline\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Load the model and create a pipeline for zero-shot classification (1min loading + classifying with 89 labels)\n",
+    "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")\n",
+    "\n",
+    "# get candidate labels\n",
+    "with open(\"packing_label_structure.json\", \"r\") as file:\n",
+    "    candidate_labels = json.load(file)\n",
+    "keys_list = list(candidate_labels.keys())\n",
+    "\n",
+    "# Load test data (in list of dictionaries)\n",
+    "with open(\"test_data.json\", \"r\") as file:\n",
+    "    packing_data = json.load(file)\n",
+    "# Extract all trip descriptions and trip_types\n",
+    "trip_descriptions = [trip['description'] for trip in packing_data]\n",
+    "trip_types = [trip['trip_types'] for trip in packing_data]\n",
+    "\n",
+    "# Access the first trip description\n",
+    "first_trip = trip_descriptions[0]\n",
+    "# Get the packing list for the secondfirst trip\n",
+    "first_trip_type = trip_types[0]\n",
+    "\n",
+    "print(f\"First trip: {first_trip} \\n\")\n",
+    "print(f\"Trip type: {first_trip_type}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "fa0d8126-e346-4412-9197-7d51baf868da",
+   "execution_count": 37,
+   "id": "fed1f8bc-5baf-46e7-8763-9d56fb9c536b",
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Some weights of BartForSequenceClassification were not initialized from the model checkpoint at facebook/bart-base and are newly initialized: ['classification_head.dense.bias', 'classification_head.dense.weight', 'classification_head.out_proj.bias', 'classification_head.out_proj.weight']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
-      "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n",
-      "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to -1. Define a descriptive label2id mapping in the model config to ensure correct outputs.\n",
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['beach vacation', 'micro-adventure / weekend trip', 'nature escape', 'digital nomad trip', 'cultural exploration', 'yoga / wellness retreat', 'festival trip', 'long-distance hike / thru-hike', 'hut trek (summer)', 'city trip', 'road trip (car/camper)', 'ski tour / skitour', 'camping trip (campground)', 'snowboard / splitboard trip', 'camping trip (wild camping)', 'hut trek (winter)'], 'scores': [0.37631064653396606, 0.35016775131225586, 0.13397355377674103, 0.031636204570531845, 0.031270742416381836, 0.012846449390053749, 0.012699575163424015, 0.009526746347546577, 0.008148356340825558, 0.007793044205754995, 0.006512156222015619, 0.005669699050486088, 0.0044484627433121204, 0.004113250877708197, 0.002713854657486081, 0.002169555053114891]}\n",
+      "beach vacation\n",
+      "0\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['going to the beach', 'relaxing', 'hiking', 'swimming', 'sightseeing', 'running', 'hut-to-hut hiking', 'biking', 'photography', 'surfing', 'stand-up paddleboarding (SUP)', 'snorkeling', 'yoga', 'kayaking / canoeing', 'rock climbing', 'fishing', 'paragliding', 'rafting', 'horseback riding', 'snowshoe hiking', 'cross-country skiing', 'ice climbing', 'skiing', 'scuba diving', 'ski touring'], 'scores': [0.9914858341217041, 0.9771362543106079, 0.9426282048225403, 0.21901991963386536, 0.17586199939250946, 0.09854521602392197, 0.08370419591665268, 0.03679152950644493, 0.03668990358710289, 0.03099300153553486, 0.025300050154328346, 0.021451234817504883, 0.011070131324231625, 0.0075112744234502316, 0.006306737195700407, 0.0034973458386957645, 0.002655829070135951, 0.00197031581774354, 0.0015599008183926344, 0.001527810120023787, 0.0015017405385151505, 0.0014336870517581701, 0.0011686616344377398, 0.000789369223639369, 0.0004912536824122071]}\n",
+      "['going to the beach', 'relaxing', 'hiking']\n",
+      "1\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['warm destination / summer', 'tropical / humid', 'variable weather / spring / autumn', 'dry / desert-like', 'cold destination / winter'], 'scores': [0.6468702554702759, 0.19999535381793976, 0.09394325315952301, 0.05279730260372162, 0.0063938056118786335]}\n",
+      "warm destination / summer\n",
+      "2\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['minimalist', 'ultralight', 'lightweight (but comfortable)', 'luxury (including evening wear)'], 'scores': [0.4286234974861145, 0.2564568817615509, 0.2147122174501419, 0.10020739585161209]}\n",
+      "minimalist\n",
+      "3\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['casual', 'conservative', 'formal (business trip)'], 'scores': [0.6567223072052002, 0.3034382164478302, 0.039839524775743484]}\n",
+      "casual\n",
+      "4\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['huts with half board', 'indoor', 'sleeping in a car', 'sleeping in a tent'], 'scores': [0.5007699728012085, 0.34074831008911133, 0.10416240990161896, 0.05431929975748062]}\n",
+      "huts with half board\n",
+      "5\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['vehicle', 'no vehicle'], 'scores': [0.7521055936813354, 0.24789436161518097]}\n",
+      "vehicle\n",
+      "6\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['off-grid / no electricity', 'self-supported (bring your own food/cooking)', 'no special conditions', 'pet-friendly', 'rainy climate', 'child-friendly', 'snow and ice', 'high alpine terrain', 'avalanche-prone terrain'], 'scores': [0.46220096945762634, 0.12957870960235596, 0.10651793330907822, 0.09777138382196426, 0.06722460687160492, 0.0632496327161789, 0.04952802509069443, 0.015049820765852928, 0.008878983557224274]}\n",
+      "off-grid / no electricity\n",
+      "7\n",
+      "{'sequence': 'I am planning a trip to Greece with my boyfriend, where we will visit two islands. We have booked an apartment on each island for a few days and plan to spend most of our time relaxing. Our main goals are to enjoy the beach, try delicious local food, and possibly go on a hike—if it’s not too hot. We will be relying solely on public transport. We’re in our late 20s and traveling from the Netherlands.', 'labels': ['7+ days', '2 days', '1 day', '6 days', '3 days', '4 days', '7 days', '5 days'], 'scores': [0.21139054000377655, 0.18512114882469177, 0.14520084857940674, 0.0976138487458229, 0.094282366335392, 0.09376301616430283, 0.09161651134490967, 0.08101171255111694]}\n",
+      "7+ days\n",
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create an empty DataFrame with specified columns\n",
+    "df = pd.DataFrame(columns=['superclass', 'pred_class'])\n",
+    "cutoff = 0.5  # used to choose which activities are relevant\n",
+    "\n",
+    "# fill DataFrame\n",
+    "for i, key in enumerate(keys_list):\n",
+    "    # Run the classification (ca 30 seconds classifying)\n",
+    "    if key == 'activities':\n",
+    "        result = classifier(first_trip, candidate_labels[key], multi_label=True)\n",
+    "        indices = [i for i, score in enumerate(result['scores']) if score > cutoff]\n",
+    "        classes = [result['labels'][i] for i in indices]\n",
+    "    else:\n",
+    "        result = classifier(first_trip, candidate_labels[key])\n",
+    "        classes = result[\"labels\"][0]\n",
+    "    print(result)\n",
+    "    print(classes)\n",
+    "    print(i)\n",
+    "    df.loc[i] = [key, classes]\n",
+    "\n",
+    "df['true_class'] = first_trip_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b3b51280-76a1-4229-a9de-070b925d3463",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Superclass                              pred_class                                        true_class\n",
+      "0       activity_type                          beach vacation                                    beach vacation\n",
+      "1          activities  [going to the beach, relaxing, hiking]  [swimming, going to the beach, relaxing, hiking]\n",
+      "2   climate_or_season               warm destination / summer                         warm destination / summer\n",
+      "3    style_or_comfort                              minimalist                     lightweight (but comfortable)\n",
+      "4          dress_code                                  casual                                            casual\n",
+      "5       accommodation                    huts with half board                                            indoor\n",
+      "6      transportation                                 vehicle                                        no vehicle\n",
+      "7  special_conditions               off-grid / no electricity                             no special conditions\n",
+      "8    trip_length_days                                 7+ days                                            7 days\n"
+     ]
+    }
+   ],
+   "source": [
+    "pd.set_option('display.width', 1000) \n",
+    "pd.set_option('display.max_columns', None)\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "2ec09e8f-75f5-45b1-b4c0-4fafd685d36b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Superclass                              pred_class                                        true_class   same\n",
+      "0       activity_type                          beach vacation                                    beach vacation   True\n",
+      "1          activities  [going to the beach, relaxing, hiking]  [swimming, going to the beach, relaxing, hiking]  False\n",
+      "2   climate_or_season               warm destination / summer                         warm destination / summer   True\n",
+      "3    style_or_comfort                              minimalist                     lightweight (but comfortable)  False\n",
+      "4          dress_code                                  casual                                            casual   True\n",
+      "5       accommodation                    huts with half board                                            indoor  False\n",
+      "6      transportation                                 vehicle                                        no vehicle  False\n",
+      "7  special_conditions               off-grid / no electricity                             no special conditions  False\n",
+      "8    trip_length_days                                 7+ days                                            7 days  False\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['same'] = df['pred_class'] == df['true_class']\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "82ae19c8-8bb7-4f7f-841b-1cb6501a17a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy (excluding activities): 0.3333333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "# accuracy excluding activities\n",
+    "correct = sum(df.loc[df.index != 1, 'same'])\n",
+    "total = len(df['same'])\n",
+    "accuracy = correct/total\n",
+    "print(\"Accuracy (excluding activities):\", accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "16c0a3ae-34ac-49a4-b59f-411a6f0ce947",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percentage of true classes that were identified: 0.75\n",
+      "Percentage of predicted classes that were wrong: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred_class = df.loc[df.index == 1, 'pred_class'].iloc[0]\n",
+    "true_class = df.loc[df.index == 1, 'true_class'].iloc[0]\n",
+    "correct = [label for label in pred_class if label in true_class]\n",
+    "\n",
+    "num_correct = len(correct)\n",
+    "correct_perc = num_correct/len(true_class)\n",
+    "\n",
+    "num_pred = len(pred_class)\n",
+    "wrong_perc = (num_pred - num_correct)/num_pred\n",
+    "\n",
+    "print(\"Percentage of true classes that were identified:\", correct_perc)\n",
+    "print(\"Percentage of predicted classes that were wrong:\", wrong_perc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62c5c18c-58f4-465c-a188-c57cfa7ffa90",
+   "metadata": {},
+   "source": [
+    "Now do the same for all trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4192b42-f1bc-4fcb-a238-dbdb3df7d699",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17483df4-55c4-41cd-b8a9-61f7a5c7e8a3",
+   "metadata": {},
+   "source": [
+    "**Use gradio for user input**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "cb7fd425-d0d6-458d-97ca-2150dc55f206",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "Running on public URL: https://aa06d5d85ffadaa92b.gradio.live\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://aa06d5d85ffadaa92b.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'sequence': 'I like to cycle and I burn easily. I also love culture and like to post on social media about my food. I will go on a trip to italy in july.', 'labels': ['Map', 'Compass', 'Laptop', 'Car charger', 'Toys', 'Travel crib', 'Hat', 'Playlist', 'Stroller', 'Currency', 'Travel adapter', 'Hostel lock', 'Pen', 'Charging cables', 'Flip-flops', 'Pacifier', 'Camping stove', 'Multi-tool', 'Passport', 'Poncho', 'Hiking boots', 'Portable charger', 'Power bank', 'Trekking poles', 'Snowboard', 'Base layers', 'Bandana', 'Aloe vera gel', 'Gloves', 'Baby blanket', 'Tent', 'Tent', 'Snorkel gear', 'Water filter', 'Diapers', 'Presentation materials', 'Nursing cover', 'Headphones', 'Sunscreen', 'Beach towel', 'Snacks', 'Ski jacket', 'Earplugs', 'Ski goggles', 'Flashlight', 'Neck wallet', 'Swimsuit', 'Notebook', 'Thermal clothing', 'Blanket', 'Snow boots', 'Sleeping bag', 'Lightweight backpack', 'Refillable water bottle', 'Quick-dry towel', 'Comfortable shoes', 'Reusable shopping bags', 'Travel journal', 'Travel pillow', 'Beach bag', 'Reusable coffee mug', 'Reusable water bottle', 'Festival tickets', 'Waterproof phone case', 'Business attire', 'Sunglasses', 'Sunglasses', 'Cooler', 'Baby clothes', 'Fanny pack', 'Beanie', 'First aid kit', 'Emergency roadside kit', 'Dry bag', 'SIM card', 'Energy bars', 'Baby food', 'Work ID badge', 'Packable rain jacket', 'Hand warmers', 'Visa documents', 'Glow sticks', 'Bug spray', 'Travel-sized toiletries', 'Dress shoes', 'Language phrasebook', 'Baby wipes', 'Lip balm', 'Travel insurance documents'], 'scores': [0.013028442859649658, 0.012909057550132275, 0.0124660674482584, 0.012431488372385502, 0.012379261665046215, 0.012377972714602947, 0.012329353019595146, 0.012096051126718521, 0.012086767703294754, 0.011947661638259888, 0.011939236894249916, 0.011935302056372166, 0.011887168511748314, 0.011814153753221035, 0.011788924224674702, 0.011783207766711712, 0.01177265401929617, 0.011771135963499546, 0.011747810058295727, 0.011738969013094902, 0.01169698778539896, 0.01166312862187624, 0.011658026836812496, 0.011596457101404667, 0.01158847101032734, 0.011561167426407337, 0.011526867747306824, 0.01149983424693346, 0.011472185142338276, 0.011455104686319828, 0.011445573531091213, 0.011445573531091213, 0.011444379575550556, 0.011416648514568806, 0.01136692427098751, 0.011363024823367596, 0.011361461132764816, 0.011328471824526787, 0.011299548670649529, 0.011291779577732086, 0.011282541789114475, 0.01127372495830059, 0.011270811781287193, 0.011263585649430752, 0.011179029010236263, 0.011149592697620392, 0.01113132108002901, 0.011122703552246094, 0.011105425655841827, 0.011101326905190945, 0.011090466752648354, 0.011066330596804619, 0.011058374308049679, 0.011055233888328075, 0.01103114802390337, 0.011022195219993591, 0.011012199334800243, 0.01100123766809702, 0.010985593311488628, 0.010961917228996754, 0.010958753526210785, 0.010938071645796299, 0.010903625749051571, 0.010879918932914734, 0.010863620787858963, 0.010824359022080898, 0.010824359022080898, 0.010805793106555939, 0.010763236321508884, 0.010710005648434162, 0.010690474882721901, 0.010647830553352833, 0.010583569295704365, 0.010571518912911415, 0.010570857673883438, 0.010552200488746166, 0.0105352271348238, 0.010523369535803795, 0.010514546185731888, 0.010479346849024296, 0.010450395755469799, 0.010436479933559895, 0.01043587177991867, 0.010400519706308842, 0.010214710608124733, 0.010052643716335297, 0.010041419416666031, 0.010003888048231602, 0.009946384467184544]}\n"
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n"
      ]
     }
    ],
    "source": [
+    "# use model with gradio\n",
     "from transformers import pipeline\n",
     "import gradio as gr\n",
     "\n",
-    "# Load the model and create a pipeline for zero-shot classification\n",
-    "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-base\")\n",
-    "\n",
-    "# Load labels from a txt file\n",
-    "with open(\"labels.txt\", \"r\", encoding=\"utf-8\") as f:\n",
-    "    class_labels = [line.strip() for line in f if line.strip()]\n",
+    "# make a function for what I am doing\n",
+    "def classify(text):\n",
+    "    df = pd.DataFrame(columns=['Superclass', 'class'])\n",
+    "    for i, key in enumerate(keys_list):\n",
+    "        # Run the classification (ca 30 seconds classifying)\n",
+    "        if key == 'activities':\n",
+    "            result = classifier(text, candidate_labels[key], multi_label=True)\n",
+    "            classes = [result['labels'][i] for i in indices]\n",
+    "        else:\n",
+    "            result = classifier(text, candidate_labels[key])\n",
+    "            classes = result[\"labels\"][0]\n",
+    "        print(i)\n",
+    "        df.loc[i] = [key, classes]\n",
     "\n",
-    "# Example text to classify\n",
-    "input_text = \"I like to cycle and I burn easily. I also love culture and like to post on social media about my food. I will go on a trip to italy in july.\"\n",
+    "    return df\n",
     "\n",
-    "# Perform classification\n",
-    "result = classifier(input_text, class_labels)\n",
+    "demo = gr.Interface(\n",
+    "    fn=classify,\n",
+    "    inputs=\"text\",\n",
+    "    outputs=\"dataframe\",\n",
+    "    title=\"Zero-Shot Classification\",\n",
+    "    description=\"Enter a text describing your trip\",\n",
+    ")\n",
     "\n",
-    "print(result)"
+    "# Launch the Gradio app\n",
+    "if __name__ == \"__main__\":\n",
+    "    demo.launch(share=True)"
    ]
   },
   {
@@ -73,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "521d9118-b59d-4cc6-b637-20202eaf8f33",
    "metadata": {
     "scrolled": true
@@ -83,15 +383,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7860\n",
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "Running on public URL: https://0f70ba5369d721cf8f.gradio.live\n",
       "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+       "<div><iframe src=\"https://0f70ba5369d721cf8f.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -116,50 +417,16 @@
     "\n",
     "# Launch the Gradio app\n",
     "if __name__ == \"__main__\":\n",
-    "    demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6526d18-6ba6-4a66-8310-21337b832d84",
-   "metadata": {},
-   "source": [
-    "Simple app"
+    "    demo.launch(share=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5496ded9-7294-4da4-af05-00e5846cdd04",
+   "id": "c8da1c90-d3a3-4b08-801c-b3afa17b2633",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "from transformers import pipeline\n",
-    "\n",
-    "# Initialize the zero-shot classification pipeline\n",
-    "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-base\")\n",
-    "\n",
-    "# Define the classification function\n",
-    "def classify_text(text, labels):\n",
-    "    labels = labels.split(\",\")  # Convert the comma-separated string into a list\n",
-    "    result = classifier(text, candidate_labels=labels)\n",
-    "    return result\n",
-    "\n",
-    "# Set up the Gradio interface\n",
-    "with gr.Blocks() as demo:\n",
-    "    gr.Markdown(\"# Zero-Shot Classification\")\n",
-    "    text_input = gr.Textbox(label=\"Input Text\")\n",
-    "    label_input = gr.Textbox(label=\"Comma-separated Labels\")\n",
-    "    output = gr.JSON(label=\"Result\")\n",
-    "    classify_button = gr.Button(\"Classify\")\n",
-    "\n",
-    "    # Link the button to the classification function\n",
-    "    classify_button.click(classify_text, inputs=[text_input, label_input], outputs=output)\n",
-    "\n",
-    "# Launch the Gradio interface\n",
-    "demo.launch()"
-   ]
+   "source": []
   }
  ],
  "metadata": {

space/space/space/space/space/space/space/space/space/space/space/packing_label_hierarchical_mapping.json

@@ -0,0 +1,290 @@
+{
+  "ski touring": {
+    "category": "activities",
+    "superclass": "winter sport"
+  },
+  "cross-country skiing": {
+    "category": "activities",
+    "superclass": "winter sport"
+  },
+  "skiing": {
+    "category": "activities",
+    "superclass": "winter sport"
+  },
+  "snowboard / splitboard trip": {
+    "category": "activity_type",
+    "superclass": "winter sport"
+  },
+  "snowshoe hiking": {
+    "category": "activities",
+    "superclass": "winter sport"
+  },
+  "ice climbing": {
+    "category": "activities",
+    "superclass": "winter sport"
+  },
+  "hiking": {
+    "category": "activities",
+    "superclass": "outdoor land"
+  },
+  "hut-to-hut hiking": {
+    "category": "activities",
+    "superclass": "outdoor land"
+  },
+  "huttentocht (zomer)": {
+    "category": "activity_type",
+    "superclass": "outdoor land"
+  },
+  "huttentocht (winter)": {
+    "category": "activity_type",
+    "superclass": "winter sport"
+  },
+  "rock climbing": {
+    "category": "activities",
+    "superclass": "outdoor land"
+  },
+  "biking": {
+    "category": "activities",
+    "superclass": "outdoor land"
+  },
+  "running": {
+    "category": "activities",
+    "superclass": "fitness"
+  },
+  "swimming": {
+    "category": "activities",
+    "superclass": "water sport"
+  },
+  "kayaking / canoeing": {
+    "category": "activities",
+    "superclass": "water sport"
+  },
+  "stand-up paddleboarding (SUP)": {
+    "category": "activities",
+    "superclass": "water sport"
+  },
+  "snorkeling": {
+    "category": "activities",
+    "superclass": "water sport"
+  },
+  "scuba diving": {
+    "category": "activities",
+    "superclass": "water sport"
+  },
+  "surfing": {
+    "category": "activities",
+    "superclass": "water sport"
+  },
+  "fishing": {
+    "category": "activities",
+    "superclass": "water sport"
+  },
+  "rafting": {
+    "category": "activities",
+    "superclass": "water sport"
+  },
+  "paragliding": {
+    "category": "activities",
+    "superclass": "adventure sport"
+  },
+  "horseback riding": {
+    "category": "activities",
+    "superclass": "outdoor land"
+  },
+  "photography": {
+    "category": "activities",
+    "superclass": "leisure"
+  },
+  "relaxing": {
+    "category": "activities",
+    "superclass": "leisure"
+  },
+  "sightseeing": {
+    "category": "activities",
+    "superclass": "cultural"
+  },
+  "yoga": {
+    "category": "activities",
+    "superclass": "wellness"
+  },
+  "festivaltrip": {
+    "category": "activity_type",
+    "superclass": "urban / event"
+  },
+  "stedentrip": {
+    "category": "activity_type",
+    "superclass": "urban / cultural"
+  },
+  "roadtrip (auto/camper)": {
+    "category": "activity_type",
+    "superclass": "travel mode"
+  },
+  "digitale nomade reis": {
+    "category": "activity_type",
+    "superclass": "remote work"
+  },
+  "kampeerreis (wildkamperen)": {
+    "category": "activity_type",
+    "superclass": "outdoor overnight"
+  },
+  "kampeerreis (camping)": {
+    "category": "activity_type",
+    "superclass": "outdoor overnight"
+  },
+  "langeafstandswandeling / thru-hike": {
+    "category": "activity_type",
+    "superclass": "backpacking"
+  },
+  "microavontuur / weekendtrip": {
+    "category": "activity_type",
+    "superclass": "short trip"
+  },
+  "yoga / wellness retreat": {
+    "category": "activity_type",
+    "superclass": "wellness"
+  },
+  "strandvakantie": {
+    "category": "activity_type",
+    "superclass": "beach / leisure"
+  },
+  "warme bestemming / zomer": {
+    "category": "climate_or_season",
+    "superclass": "warm"
+  },
+  "koude bestemming / winter": {
+    "category": "climate_or_season",
+    "superclass": "cold"
+  },
+  "wisselvallig / lente / herfst": {
+    "category": "climate_or_season",
+    "superclass": "mild"
+  },
+  "tropisch / vochtig": {
+    "category": "climate_or_season",
+    "superclass": "humid"
+  },
+  "droog / woestijnachtig": {
+    "category": "climate_or_season",
+    "superclass": "dry"
+  },
+  "ultralight": {
+    "category": "style_or_comfort",
+    "superclass": "packing style"
+  },
+  "lichtgewicht (maar comfortabel)": {
+    "category": "style_or_comfort",
+    "superclass": "packing style"
+  },
+  "luxe (inclusief avondkleding)": {
+    "category": "style_or_comfort",
+    "superclass": "comfort / luxury"
+  },
+  "casual": {
+    "category": "dress_code",
+    "superclass": "informal"
+  },
+  "formeel (zakelijke reis)": {
+    "category": "dress_code",
+    "superclass": "formal"
+  },
+  "conservative": {
+    "category": "dress_code",
+    "superclass": "cultural"
+  },
+  "minimalistisch": {
+    "category": "style_or_comfort",
+    "superclass": "packing style"
+  },
+  "off-grid / geen stroom": {
+    "category": "special_conditions",
+    "superclass": "independent"
+  },
+  "self-supported (eten/koken zelf meenemen)": {
+    "category": "special_conditions",
+    "superclass": "independent"
+  },
+  "regenachtig klimaat": {
+    "category": "special_conditions",
+    "superclass": "weather"
+  },
+  "sneeuw en ijs": {
+    "category": "special_conditions",
+    "superclass": "weather"
+  },
+  "hoog alpine terrein": {
+    "category": "special_conditions",
+    "superclass": "terrain"
+  },
+  "lawinegevoelig terrein": {
+    "category": "special_conditions",
+    "superclass": "terrain"
+  },
+  "hutten met halfpension": {
+    "category": "accommodation",
+    "superclass": "type"
+  },
+  "slapen in tent": {
+    "category": "accommodation",
+    "superclass": "type"
+  },
+  "slapen in auto": {
+    "category": "accommodation",
+    "superclass": "type"
+  },
+  "kindvriendelijk": {
+    "category": "special_conditions",
+    "superclass": "target group"
+  },
+  "huisdiervriendelijk": {
+    "category": "special_conditions",
+    "superclass": "target group"
+  },
+  "accommodation: outdoor": {
+    "category": "accommodation",
+    "superclass": "type"
+  },
+  "accommodation: indoor": {
+    "category": "accommodation",
+    "superclass": "type"
+  },
+  "transportation: vehicle": {
+    "category": "transportation",
+    "superclass": "mode"
+  },
+  "transportation: no vehicle": {
+    "category": "transportation",
+    "superclass": "mode"
+  },
+  "1 day": {
+    "category": "trip_length_days",
+    "superclass": "duration"
+  },
+  "2 days": {
+    "category": "trip_length_days",
+    "superclass": "duration"
+  },
+  "3 days": {
+    "category": "trip_length_days",
+    "superclass": "duration"
+  },
+  "4 days": {
+    "category": "trip_length_days",
+    "superclass": "duration"
+  },
+  "5 days": {
+    "category": "trip_length_days",
+    "superclass": "duration"
+  },
+  "6 days": {
+    "category": "trip_length_days",
+    "superclass": "duration"
+  },
+  "7 days": {
+    "category": "trip_length_days",
+    "superclass": "duration"
+  },
+  "7+ days": {
+    "category": "trip_length_days",
+    "superclass": "duration"
+  }
+}