Auto-update from GitHub

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",

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",

results/MoritzLaurer-DeBERTa-v3-base-mnli-fever-anli_results.pkl

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results/before/facebook-bart-large-mnli_results.pkl

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results/facebook-bart-large-mnli_results.pkl

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space/.ipynb_checkpoints/gradio_tryout-checkpoint.ipynb

@@ -19,113 +19,15 @@
    "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": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ec45d8fb4e247e4b1188972547ebb7f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/1.09k [00:00<?, ?B/s]"
-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "991968ef7a0448b39ca84316e6d06902",
-       "version_major": 2,
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-       "model.safetensors:   0%|          | 0.00/369M [00:00<?, ?B/s]"
-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "561c0cdcc88f4cd6b89dd19bcc0599cb",
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-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c5e517b2512246018e5cb1fdc8d3e1e1",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "03b470dec7174fd29b9b0e4afab759a0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/8.66M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "16d24496b31b4c0fb7744c8ee1222d40",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "added_tokens.json:   0%|          | 0.00/23.0 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ef2aefc365734c1884bbbb04d79edf5b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "special_tokens_map.json:   0%|          | 0.00/286 [00:00<?, ?B/s]"
-      ]
-     },
-     "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/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/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/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/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/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/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/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/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/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/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/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/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"
+  }
+}

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

@@ -0,0 +1,96 @@
+{
+  "activity_type": [
+    "hut trek (summer)",
+    "hut trek (winter)",
+    "camping trip (wild camping)",
+    "camping trip (campground)",
+    "ski tour / skitour",
+    "snowboard / splitboard trip",
+    "long-distance hike / thru-hike",
+    "digital nomad trip",
+    "city trip",
+    "road trip (car/camper)",
+    "festival trip",
+    "yoga / wellness retreat",
+    "micro-adventure / weekend trip",
+    "beach vacation",
+    "cultural exploration",
+    "nature escape"
+  ],
+  "activities": [
+    "swimming",
+    "going to the beach",
+    "relaxing",
+    "sightseeing",
+    "biking",
+    "running",
+    "skiing",
+    "cross-country skiing",
+    "ski touring",
+    "hiking",
+    "hut-to-hut hiking",
+    "rock climbing",
+    "ice climbing",
+    "snowshoe hiking",
+    "kayaking / canoeing",
+    "stand-up paddleboarding (SUP)",
+    "snorkeling",
+    "scuba diving",
+    "surfing",
+    "paragliding",
+    "horseback riding",
+    "photography",
+    "fishing",
+    "rafting",
+    "yoga"
+  ],
+  "climate_or_season": [
+    "cold destination / winter",
+    "warm destination / summer",
+    "variable weather / spring / autumn",
+    "tropical / humid",
+    "dry / desert-like"
+  ],
+  "style_or_comfort": [
+    "ultralight",
+    "lightweight (but comfortable)",
+    "luxury (including evening wear)",
+    "minimalist"
+  ],
+  "dress_code": [
+    "casual",
+    "formal (business trip)",
+    "conservative"
+  ],
+  "accommodation": [
+    "indoor",
+    "huts with half board",
+    "sleeping in a tent",
+    "sleeping in a car"
+  ],
+  "transportation": [
+    "vehicle",
+    "no vehicle"
+  ],
+  "special_conditions": [
+    "off-grid / no electricity",
+    "self-supported (bring your own food/cooking)",
+    "child-friendly",
+    "pet-friendly",
+    "rainy climate",
+    "snow and ice",
+    "high alpine terrain",
+    "avalanche-prone terrain",
+    "no special conditions"  
+  ],
+  "trip_length_days": [
+    "1 day",
+    "2 days",
+    "3 days",
+    "4 days",
+    "5 days",
+    "6 days",
+    "7 days",
+    "7+ days"
+  ]
+}

space/space/space/space/space/space/space/packing_list_api.ipynb

@@ -59,61 +59,6 @@
     "print(output)\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "fb7e69c7-b590-4b40-8478-76d055583f2a",
-   "metadata": {},
-   "source": [
-    "**Try packing list labels**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c5f75916-aaf2-4ca7-8d1a-070579940952",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'error': ['Error in `parameters.candidate_labels`: ensure this value has at most 10 items']}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Input 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",
-    "\n",
-    "# Candidate labels\n",
-    "candidate_labels = [\n",
-    "    \"Swimsuit\", \"Sunscreen\", \"Flip-flops\", \"Beach towel\", \"Sunglasses\", \n",
-    "    \"Waterproof phone case\", \"Hat\", \"Beach bag\", \"Snorkel gear\", \"Aloe vera gel\",\n",
-    "    \"Tent\", \"Sleeping bag\", \"Camping stove\", \"Flashlight\", \"Hiking boots\",\n",
-    "    \"Water filter\", \"Compass\", \"First aid kit\", \"Bug spray\", \"Multi-tool\",\n",
-    "    \"Thermal clothing\", \"Ski jacket\", \"Ski goggles\", \"Snow boots\", \"Gloves\",\n",
-    "    \"Hand warmers\", \"Beanie\", \"Lip balm\", \"Snowboard\", \"Base layers\",\n",
-    "    \"Passport\", \"Visa documents\", \"Travel adapter\", \"Currency\", \"Language phrasebook\",\n",
-    "    \"SIM card\", \"Travel pillow\", \"Neck wallet\", \"Travel insurance documents\", \"Power bank\",\n",
-    "    \"Laptop\", \"Notebook\", \"Business attire\", \"Dress shoes\", \"Charging cables\",\n",
-    "    \"Presentation materials\", \"Work ID badge\", \"Pen\", \"Headphones\", \n",
-    "    \"Lightweight backpack\", \"Travel-sized toiletries\", \"Packable rain jacket\",\n",
-    "    \"Reusable water bottle\", \"Dry bag\", \"Trekking poles\", \"Hostel lock\", \"Quick-dry towel\",\n",
-    "    \"Travel journal\", \"Energy bars\", \"Car charger\", \"Snacks\", \"Map\",\n",
-    "    \"Sunglasses\", \"Cooler\", \"Blanket\", \"Emergency roadside kit\", \"Reusable coffee mug\",\n",
-    "    \"Playlist\", \"Reusable shopping bags\", \"Earplugs\", \"Fanny pack\", \"Portable charger\",\n",
-    "    \"Poncho\", \"Bandana\", \"Comfortable shoes\", \"Tent\", \"Refillable water bottle\",\n",
-    "    \"Glow sticks\", \"Festival tickets\", \"Diapers\", \"Baby wipes\", \"Baby food\",\n",
-    "    \"Stroller\", \"Pacifier\", \"Baby clothes\", \"Baby blanket\", \"Travel crib\",\n",
-    "    \"Toys\", \"Nursing cover\"\n",
-    "]\n",
-    "\n",
-    "\n",
-    "# Get the prediction\n",
-    "output = query({\"inputs\": input_text, \"parameters\": {\"candidate_labels\": candidate_labels}})\n",
-    "print(output)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "edf44387-d166-4e0f-a8ad-621230aee115",
@@ -158,7 +103,7 @@
     "trips = list(packing_data.keys())\n",
     "# Access the first trip description\n",
     "first_trip = trips[0]\n",
-    "# Get the packing list for the second trip\n",
+    "# Get the packing list for the secondfirst trip\n",
     "first_trip_items = packing_data[first_trip]\n",
     "\n",
     "print(f\"First trip: {first_trip} \\n\")\n",
@@ -243,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "116c7ee3-2b59-4623-a416-162c487aab70",
    "metadata": {},
    "outputs": [],
@@ -254,10 +199,204 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "8591425b-ce55-4a36-a4b6-70974e8d4e59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| bart_base                                                            | bart_large_mnli                                                      |\n",
+      "+======================================================================+======================================================================+\n",
+      "| bandana                                                              | travel adapter                                                       |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| lip balm                                                             | travel journal                                                       |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| hand warmers                                                         | light jacket                                                         |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| sim card                                                             | sim card                                                             |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| neck wallet                                                          | bathing suit                                                         |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| tent                                                                 | multi-tool                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| poncho                                                               | dry bag                                                              |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| gloves                                                               | travel pillow                                                        |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| painkiller                                                           | base layers                                                          |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| pen                                                                  | day pack                                                             |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| thin scarf                                                           | entertainment for downtime (e.g. book/ebook, games, laptop, journal) |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| wallet                                                               | passport                                                             |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| sleeping bag                                                         | thin scarf                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| dry bag                                                              | comfortable shoes                                                    |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| gifts                                                                | lightweight backpack                                                 |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| fanny pack                                                           | beach bag                                                            |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| beach towel                                                          | swimsuit                                                             |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| underwear                                                            | short pants/skirts                                                   |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| swimsuit                                                             | quick-dry towel                                                      |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| blanket                                                              | sunhat                                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| laptop                                                               | local currency                                                       |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| quick-dry towel                                                      | tickets                                                              |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| ski jacket                                                           | wallet                                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| emergency roadside kit                                               | cardigan/sweater                                                     |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| passport                                                             | refillable water bottle                                              |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| necessary medication                                                 | fanny pack                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| snacks for the journey                                               | poncho                                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| snow boots                                                           | thermal clothing                                                     |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| sunglasses                                                           | laptop                                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| flip-flops                                                           | pen                                                                  |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| phone and charger                                                    | big backpack/suitcase                                                |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| socks                                                                | beach towel                                                          |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| local currency                                                       | currency                                                             |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| t-shirts/tops                                                        | blanket                                                              |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| bathing suit                                                         | compass                                                              |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| currency                                                             | beanie                                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| cardigan/sweater                                                     | sunscreen                                                            |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| snowboard                                                            | phone and charger                                                    |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| visa documents                                                       | reusable coffee mug                                                  |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| shirts                                                               | power bank                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| headphones                                                           | personal toiletries (e.g. toothbrush, toothpaste)                    |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| pants                                                                | packable rain jacket                                                 |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| refillable water bottle                                              | bandana                                                              |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| beach bag                                                            | short pants                                                          |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| big backpack/suitcase                                                | business attire                                                      |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| multi-tool                                                           | sleeping bag                                                         |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| sandals                                                              | flashlight                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| tickets                                                              | t-shirts/tops                                                        |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| thermal clothing                                                     | waterproof phone case                                                |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| short pants/skirts                                                   | long pants                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| light jacket                                                         | travel-sized toiletries                                              |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| base layers                                                          | visa documents                                                       |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| snacks                                                               | sandals                                                              |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| comfortable shoes                                                    | hand warmers                                                         |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| lightweight backpack                                                 | hostel lock                                                          |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| winter shoes                                                         | headphones                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| dress shoes                                                          | emergency roadside kit                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| power bank                                                           | ski jacket                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| packable rain jacket                                                 | shirts                                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| first aid kit                                                        | first aid kit                                                        |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| water filter                                                         | reusable shopping bags                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| short pants                                                          | flip-flops                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| ski goggles                                                          | camping stove                                                        |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| waterproof phone case                                                | water filter                                                         |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| sunhat                                                               | gloves                                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| flashlight                                                           | dress shoes                                                          |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| earplugs                                                             | tent                                                                 |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| beanie                                                               | sunglasses                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| hostel lock                                                          | pants                                                                |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| personal toiletries (e.g. toothbrush, toothpaste)                    | charging cables                                                      |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| travel journal                                                       | snacks                                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| reusable coffee mug                                                  | neck wallet                                                          |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| sunscreen                                                            | snacks for the journey                                               |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| travel pillow                                                        | ski goggles                                                          |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| trekking poles                                                       | mosquito repellant                                                   |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| business attire                                                      | snorkel gear                                                         |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| snorkel gear                                                         | bug spray                                                            |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| reusable shopping bags                                               | earplugs                                                             |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| sleep wear                                                           | travel insurance documents                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| camping stove                                                        | painkiller                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| travel-sized toiletries                                              | hiking boots                                                         |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| hiking boots                                                         | necessary medication                                                 |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| travel insurance documents                                           | socks                                                                |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| long pants                                                           | underwear                                                            |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| charging cables                                                      | trekking poles                                                       |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| entertainment for downtime (e.g. book/ebook, games, laptop, journal) | sleep wear                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| bug spray                                                            | gifts                                                                |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| day pack                                                             | lip balm                                                             |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| travel adapter                                                       | snowboard                                                            |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| malaria medication                                                   | malaria medication                                                   |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| compass                                                              | snow boots                                                           |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n",
+      "| mosquito repellant                                                   | winter shoes                                                         |\n",
+      "+----------------------------------------------------------------------+----------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
    "source": [
     "# Creating a table\n",
     "table = zip(result_bart_base[\"labels\"], \n",

space/space/space/space/space/space/space/packing_templates_self_supported_offgrid_expanded.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)"
+  ],
+  "vehicle": [
+    "rijbewijs",
+    "autopapieren",
+    "EHBO-set",
+    "navigatie of smartphone",
+    "telefoonhouder"
+  ],
+  "no 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 food/cooking)": [
+    "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)"
+  ],
+  "child-friendly": [
+    "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"
+  ],
+  "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/space/space/space/space/space/space/space/labels.txt

@@ -1,89 +1,93 @@
-Swimsuit
-Sunscreen
-Flip-flops
-Beach towel
-Sunglasses
-Waterproof phone case
-Hat
-Beach bag
-Snorkel gear
-Aloe vera gel
-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
-Language phrasebook
-SIM card
-Travel pillow
-Neck wallet
-Travel insurance documents
-Power bank
-Laptop
-Notebook
-Business attire
-Dress shoes
-Charging cables
-Presentation materials
-Work ID badge
-Pen
-Headphones
-Lightweight backpack
-Travel-sized toiletries
-Packable rain jacket
-Reusable water bottle
-Dry bag
-Trekking poles
-Hostel lock
-Quick-dry towel
-Travel journal
-Energy bars
-Car charger
-Snacks
-Map
-Sunglasses
-Cooler
-Blanket
-Emergency roadside kit
-Reusable coffee mug
-Playlist
-Reusable shopping bags
-Earplugs
-Fanny pack
-Portable charger
-Poncho
-Bandana
-Comfortable shoes
-Tent
-Refillable water bottle
-Glow sticks
-Festival tickets
-Diapers
-Baby wipes
-Baby food
-Stroller
-Pacifier
-Baby clothes
-Baby blanket
-Travel crib
-Toys
-Nursing cover
+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/space/space/space/space/space/space/space/packing_list_api.ipynb

@@ -124,7 +124,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 35,
+   "id": "1d01a363-572b-450c-8fce-0721234f9a1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First trip: 7-Day Island Beach Holiday in Greece (Summer). 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",
+      "Packing list: ['bathing suit', 'beach towel', 'beach bag', 'sandals', 'comfortable walking shoes', 'light jacket', 'sunscreen', 'sunglasses', 'sunhat', 'entertainment for downtime (e.g. book/ebook, games, laptop, journal)', 'short pants/skirts', 't-shirts/tops']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prerequisites\n",
+    "from tabulate import tabulate\n",
+    "from transformers import pipeline\n",
+    "import json\n",
+    "\n",
+    "# input text\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",
+    "\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",
+    "\n",
+    "# Load test data (in dictionary)\n",
+    "with open(\"test_data.json\", \"r\") as file:\n",
+    "    packing_data = json.load(file)\n",
+    "# Get a list of trip descriptions (keys)\n",
+    "trips = list(packing_data.keys())\n",
+    "# Access the first trip description\n",
+    "first_trip = trips[0]\n",
+    "# Get the packing list for the second trip\n",
+    "first_trip_items = packing_data[first_trip]\n",
+    "\n",
+    "print(f\"First trip: {first_trip} \\n\")\n",
+    "print(f\"Packing list: {first_trip_items}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88aa1d7e-8a32-4530-9ddd-60fa38e4a342",
+   "metadata": {},
+   "source": [
+    "Load classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
    "id": "d0d8f7c0-c2d9-4fbe-b1a7-699a5b99466c",
    "metadata": {},
    "outputs": [
@@ -140,82 +191,89 @@
     }
    ],
    "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Load the model and create a pipeline for zero-shot classification\n",
-    "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-base\")"
+    "# Load smaller the model and create a pipeline for zero-shot classification (1min loading + classifying with 89 labels)\n",
+    "classifier_bart_base = pipeline(\"zero-shot-classification\", model=\"facebook/bart-base\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "4682d620-c9a6-40ad-ab4c-268ee0ef7212",
+   "execution_count": 37,
+   "id": "a971ca1c-d478-489f-9592-bc243d587eb4",
    "metadata": {},
    "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"
+      "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": [
+    "# Load larger the model and create a pipeline for zero-shot classification (5min loading model + classifying with 89 labels)\n",
+    "classifier_bart_large_mnli = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38805499-9919-40fe-9d42-de6869ba01dc",
+   "metadata": {},
+   "source": [
+    "Try classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "abb13524-71c6-448d-948d-fb22a0e0ceeb",
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "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': ['Swimsuit', 'Travel crib', 'Business attire', 'Toys', 'Notebook', 'Travel adapter', 'Compass', 'Travel pillow', 'Headphones', 'Travel journal', 'Playlist', 'Flip-flops', 'Hiking boots', 'Reusable coffee mug', 'Comfortable shoes', 'Nursing cover', 'Gloves', 'Tent', 'Tent', 'Sunglasses', 'Sunglasses', 'Charging cables', 'Travel-sized toiletries', 'Refillable water bottle', 'Energy bars', 'Dress shoes', 'Festival tickets', 'Lightweight backpack', 'Packable rain jacket', 'Flashlight', 'Hostel lock', 'Presentation materials', 'Thermal clothing', 'Snowboard', 'Camping stove', 'Reusable shopping bags', 'Reusable water bottle', 'Blanket', 'Diapers', 'Snorkel gear', 'Snacks', 'Emergency roadside kit', 'Beach towel', 'Sunscreen', 'Car charger', 'Bug spray', 'Passport', 'Currency', 'Beach bag', 'Ski jacket', 'First aid kit', 'Cooler', 'Quick-dry towel', 'Laptop', 'Aloe vera gel', 'Earplugs', 'Baby wipes', 'Ski goggles', 'Travel insurance documents', 'Portable charger', 'Beanie', 'Bandana', 'Multi-tool', 'Pacifier', 'Stroller', 'Language phrasebook', 'Waterproof phone case', 'Dry bag', 'Map', 'Lip balm', 'Fanny pack', 'Trekking poles', 'Power bank', 'Baby clothes', 'Baby food', 'Poncho', 'Sleeping bag', 'Work ID badge', 'Visa documents', 'SIM card', 'Water filter', 'Snow boots', 'Hand warmers', 'Baby blanket', 'Base layers', 'Pen', 'Hat', 'Neck wallet', 'Glow sticks'], 'scores': [0.012542711570858955, 0.012216676957905293, 0.012068654410541058, 0.011977529153227806, 0.011932261288166046, 0.011920000426471233, 0.011883101426064968, 0.011842883192002773, 0.011819617822766304, 0.011810989119112492, 0.011761271394789219, 0.011756575666368008, 0.011726364493370056, 0.011664840392768383, 0.011632450856268406, 0.01163020171225071, 0.01158054918050766, 0.011572858318686485, 0.011572858318686485, 0.011541635729372501, 0.011541635729372501, 0.011517350561916828, 0.011510960757732391, 0.011489875614643097, 0.011469963937997818, 0.011466587893664837, 0.011442759074270725, 0.011438597925007343, 0.011437375098466873, 0.011433145962655544, 0.011407203041017056, 0.011401104740798473, 0.01135423593223095, 0.011333385482430458, 0.011328010819852352, 0.011325137689709663, 0.01131997536867857, 0.011306566186249256, 0.011299673467874527, 0.011281789280474186, 0.011264320462942123, 0.011257764883339405, 0.011256475001573563, 0.011253912933170795, 0.011252702213823795, 0.011248898692429066, 0.011247594840824604, 0.011239985004067421, 0.01121864840388298, 0.011208567768335342, 0.011174682527780533, 0.011166973039507866, 0.011159253306686878, 0.011151333339512348, 0.011140624061226845, 0.011139076203107834, 0.01113345380872488, 0.011126152239739895, 0.011093570850789547, 0.011078842915594578, 0.011067545972764492, 0.011044573038816452, 0.01101986039429903, 0.011016158387064934, 0.011015082709491253, 0.011007890105247498, 0.010997296310961246, 0.010962157510221004, 0.01095755398273468, 0.010940180160105228, 0.01088095735758543, 0.010869039222598076, 0.010858545079827309, 0.010820968076586723, 0.01080892514437437, 0.010798529721796513, 0.01077410951256752, 0.010764310136437416, 0.010748079977929592, 0.010681436397135258, 0.010675576515495777, 0.010557047091424465, 0.010552684776484966, 0.010509641841053963, 0.010396942496299744, 0.01037551462650299, 0.01033466774970293, 0.010237698443233967, 0.009954877197742462]}\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"
      ]
     }
    ],
    "source": [
-    "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",
-    "\n",
-    "# Candidate labels\n",
-    "candidate_labels = [\n",
-    "    \"Swimsuit\", \"Sunscreen\", \"Flip-flops\", \"Beach towel\", \"Sunglasses\", \n",
-    "    \"Waterproof phone case\", \"Hat\", \"Beach bag\", \"Snorkel gear\", \"Aloe vera gel\",\n",
-    "    \"Tent\", \"Sleeping bag\", \"Camping stove\", \"Flashlight\", \"Hiking boots\",\n",
-    "    \"Water filter\", \"Compass\", \"First aid kit\", \"Bug spray\", \"Multi-tool\",\n",
-    "    \"Thermal clothing\", \"Ski jacket\", \"Ski goggles\", \"Snow boots\", \"Gloves\",\n",
-    "    \"Hand warmers\", \"Beanie\", \"Lip balm\", \"Snowboard\", \"Base layers\",\n",
-    "    \"Passport\", \"Visa documents\", \"Travel adapter\", \"Currency\", \"Language phrasebook\",\n",
-    "    \"SIM card\", \"Travel pillow\", \"Neck wallet\", \"Travel insurance documents\", \"Power bank\",\n",
-    "    \"Laptop\", \"Notebook\", \"Business attire\", \"Dress shoes\", \"Charging cables\",\n",
-    "    \"Presentation materials\", \"Work ID badge\", \"Pen\", \"Headphones\", \n",
-    "    \"Lightweight backpack\", \"Travel-sized toiletries\", \"Packable rain jacket\",\n",
-    "    \"Reusable water bottle\", \"Dry bag\", \"Trekking poles\", \"Hostel lock\", \"Quick-dry towel\",\n",
-    "    \"Travel journal\", \"Energy bars\", \"Car charger\", \"Snacks\", \"Map\",\n",
-    "    \"Sunglasses\", \"Cooler\", \"Blanket\", \"Emergency roadside kit\", \"Reusable coffee mug\",\n",
-    "    \"Playlist\", \"Reusable shopping bags\", \"Earplugs\", \"Fanny pack\", \"Portable charger\",\n",
-    "    \"Poncho\", \"Bandana\", \"Comfortable shoes\", \"Tent\", \"Refillable water bottle\",\n",
-    "    \"Glow sticks\", \"Festival tickets\", \"Diapers\", \"Baby wipes\", \"Baby food\",\n",
-    "    \"Stroller\", \"Pacifier\", \"Baby clothes\", \"Baby blanket\", \"Travel crib\",\n",
-    "    \"Toys\", \"Nursing cover\"\n",
-    "]\n",
-    "\n",
-    "\n",
-    "# Run the classification\n",
-    "result = classifier(input_text, candidate_labels)\n",
-    "\n",
-    "# Print the result\n",
-    "print(result)"
+    "# Run the classification (ca 30 seconds classifying)\n",
+    "result_bart_base = classifier_bart_base(first_trip, class_labels)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a344a80f-7645-4c2c-b960-580aa0b345f6",
+   "id": "116c7ee3-2b59-4623-a416-162c487aab70",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Run the classification (ca 1 minute classifying)\n",
+    "result_bart_large_mnli = classifier_bart_large_mnli(first_trip, class_labels)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5eb705d6-c31c-406c-9739-ff45b66c7ca4",
+   "id": "8591425b-ce55-4a36-a4b6-70974e8d4e59",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Creating a table\n",
+    "table = zip(result_bart_base[\"labels\"], \n",
+    "            result_bart_large_mnli[\"labels\"])\n",
+    "headers = [\"bart_base\", \"bart_large_mnli\"]\n",
+    "\n",
+    "print(tabulate(table, headers=headers, tablefmt=\"grid\"))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21a35d0c-9451-433a-b14c-87e8dac21d68",
+   "metadata": {},
+   "source": [
+    "**Try simple prompt engineering**"
+   ]
   },
   {
    "cell_type": "code",
@@ -224,21 +282,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Example text to classify\n",
-    "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",
-    "\n",
     "# No prompt\n",
-    "no_prompt = text\n",
-    "no_result = classifier(no_prompt, candidate_labels)\n",
-    "\n",
+    "no_prompt = input_text\n",
+    "no_result = classifier(no_prompt, class_labels)\n",
     "\n",
     "# Simple prompt\n",
-    "simple_prompt = \"Classify the following text: \" + text\n",
-    "simple_result = classifier(simple_prompt, candidate_labels)\n",
+    "simple_prompt = \"Classify the following text: \" + input_text\n",
+    "simple_result = classifier(simple_prompt, class_labels)\n",
     "\n",
     "# Primed prompt\n",
-    "primed_prompt = \"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. What are the most important things to pack for the trip?\"\n",
-    "primed_result = classifier(primed_prompt, candidate_labels)"
+    "primed_prompt = input_text + \"What are the most important things to pack for the trip?\"\n",
+    "primed_result = classifier(primed_prompt, class_labels)"
    ]
   },
   {
@@ -436,9 +490,6 @@
     }
    ],
    "source": [
-    "from tabulate import tabulate\n",
-    "\n",
-    "\n",
     "# Creating a table\n",
     "table = zip(no_result[\"labels\"], no_result[\"scores\"], \n",
     "            simple_result[\"labels\"], simple_result[\"scores\"], \n",
@@ -447,14 +498,6 @@
     "\n",
     "print(tabulate(table, headers=headers, tablefmt=\"grid\"))\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5ed9bda0-41f2-4c7c-b055-27c1998c1d4e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

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

@@ -1,25 +1,25 @@
-import gradio as gr
 from transformers import pipeline
+import gradio as gr
 
-# Initialize the zero-shot classification pipeline
+# Load the model and create a pipeline for zero-shot classification
 classifier = pipeline("zero-shot-classification", model="facebook/bart-base")
 
-# Define the classification function
-def classify_text(text, labels):
-    labels = labels.split(",")  # Convert the comma-separated string into a list
-    result = classifier(text, candidate_labels=labels)
-    return result
+# 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()]
 
-# Set up the Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# Zero-Shot Classification")
-    text_input = gr.Textbox(label="Input Text")
-    label_input = gr.Textbox(label="Comma-separated Labels")
-    output = gr.JSON(label="Result")
-    classify_button = gr.Button("Classify")
+# Define the Gradio interface
+def classify(text):
+    return classifier(text, class_labels)
 
-    # Link the button to the classification function
-    classify_button.click(classify_text, inputs=[text_input, label_input], outputs=output)
+demo = gr.Interface(
+    fn=classify,
+    inputs="text",
+    outputs="json",
+    title="Zero-Shot Classification",
+    description="Enter a text describing your trip",
+)
 
-# Launch the Gradio interface
-demo.launch()
+# Launch the Gradio app
+if __name__ == "__main__":
+    demo.launch()

space/space/space/space/space/space/space/space/space/.ipynb_checkpoints/gradio_tryout-checkpoint.ipynb

@@ -1,6 +1,186 @@
 {
- "cells": [],
- "metadata": {},
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e25090fa-f990-4f1a-84f3-b12159eedae8",
+   "metadata": {},
+   "source": [
+    "# Try out gradio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bbee2e4-55c8-4b06-9929-72026edf7932",
+   "metadata": {},
+   "source": [
+    "Try model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fa0d8126-e346-4412-9197-7d51baf868da",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    }
+   ],
+   "source": [
+    "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",
+    "\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",
+    "\n",
+    "# Perform classification\n",
+    "result = classifier(input_text, class_labels)\n",
+    "\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e856a9c-a66c-4c4b-b7cf-8c52abbbc6fa",
+   "metadata": {},
+   "source": [
+    "Use model with gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "521d9118-b59d-4cc6-b637-20202eaf8f33",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\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>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the Gradio interface\n",
+    "def classify(text):\n",
+    "    return classifier(text, class_labels)\n",
+    "\n",
+    "demo = gr.Interface(\n",
+    "    fn=classify,\n",
+    "    inputs=\"text\",\n",
+    "    outputs=\"json\",\n",
+    "    title=\"Zero-Shot Classification\",\n",
+    "    description=\"Enter a text describing your trip\",\n",
+    ")\n",
+    "\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5496ded9-7294-4da4-af05-00e5846cdd04",
+   "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()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (huggingface_env)",
+   "language": "python",
+   "name": "huggingface_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.20"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

space/space/space/space/space/space/space/space/space/app.py

@@ -1,25 +1,25 @@
-import gradio as gr
 from transformers import pipeline
+import gradio as gr
 
-# Initialize the zero-shot classification pipeline
+# Load the model and create a pipeline for zero-shot classification
 classifier = pipeline("zero-shot-classification", model="facebook/bart-base")
 
-# Define the classification function
-def classify_text(text, labels):
-    labels = labels.split(",")  # Convert the comma-separated string into a list
-    result = classifier(text, candidate_labels=labels)
-    return result
+# 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()]
 
-# Set up the Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# Zero-Shot Classification")
-    text_input = gr.Textbox(label="Input Text")
-    label_input = gr.Textbox(label="Comma-separated Labels")
-    output = gr.JSON(label="Result")
-    classify_button = gr.Button("Classify")
+# Define the Gradio interface
+def classify(text):
+    return classifier(text, class_labels)
 
-    # Link the button to the classification function
-    classify_button.click(classify_text, inputs=[text_input, label_input], outputs=output)
+demo = gr.Interface(
+    fn=classify,
+    inputs="text",
+    outputs="json",
+    title="Zero-Shot Classification",
+    description="Enter a text describing your trip",
+)
 
-# Launch the Gradio interface
-demo.launch()
+# Launch the Gradio app
+if __name__ == "__main__":
+    demo.launch()

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

@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "fa0d8126-e346-4412-9197-7d51baf868da",
    "metadata": {
     "scrolled": true
@@ -39,7 +39,7 @@
      "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': ['Compass', 'Travel insurance documents', 'Cooler', 'Poncho', 'Comfortable shoes', 'Thermal clothing', 'Business attire', 'Stroller', 'Refillable water bottle', 'Sunscreen', 'Hiking boots', 'Trekking poles', 'Tent', 'Tent', 'Swimsuit', 'Lightweight backpack', 'Diapers', 'Pen', 'Lip balm', 'Bandana', 'Presentation materials', 'Snorkel gear', 'Sunglasses', 'Sunglasses', 'Snowboard', 'Baby wipes', 'Emergency roadside kit', 'Blanket', 'Passport', 'Aloe vera gel', 'Currency', 'Beanie', 'Hand warmers', 'Reusable shopping bags', 'Hat', 'Travel-sized toiletries', 'Waterproof phone case', 'Energy bars', 'Baby food', 'Reusable water bottle', 'Flashlight', 'Gloves', 'Baby clothes', 'Hostel lock', 'Visa documents', 'Camping stove', 'Bug spray', 'Packable rain jacket', 'Travel pillow', 'Power bank', 'Earplugs', 'Quick-dry towel', 'Reusable coffee mug', 'Travel journal', 'Fanny pack', 'Headphones', 'Notebook', 'Dress shoes', 'Nursing cover', 'Playlist', 'Base layers', 'Work ID badge', 'Festival tickets', 'Sleeping bag', 'Laptop', 'Baby blanket', 'Charging cables', 'Snow boots', 'First aid kit', 'Snacks', 'Flip-flops', 'Toys', 'Car charger', 'Ski jacket', 'Dry bag', 'Pacifier', 'Map', 'Portable charger', 'Travel crib', 'Multi-tool', 'Beach bag', 'Ski goggles', 'SIM card', 'Glow sticks', 'Beach towel', 'Travel adapter', 'Neck wallet', 'Language phrasebook', 'Water filter'], 'scores': [0.011984821408987045, 0.011970506981015205, 0.011933253146708012, 0.011915490962564945, 0.011904211714863777, 0.011892491020262241, 0.01188766211271286, 0.011866495944559574, 0.011842762120068073, 0.011789090000092983, 0.011770269833505154, 0.011769718490540981, 0.011746660806238651, 0.011746660806238651, 0.011718676425516605, 0.01164235919713974, 0.011551206931471825, 0.011529732495546341, 0.011518468149006367, 0.011516833677887917, 0.011508049443364143, 0.011507270857691765, 0.01149584911763668, 0.01149584911763668, 0.011495097540318966, 0.01149324607104063, 0.011486946605145931, 0.01148668210953474, 0.011478666216135025, 0.011473646387457848, 0.011412998661398888, 0.011398673988878727, 0.011378799565136433, 0.01135518029332161, 0.011335738934576511, 0.011330211535096169, 0.011329339817166328, 0.011324702762067318, 0.01131915021687746, 0.01131164189428091, 0.011294065974652767, 0.011273612268269062, 0.011272135190665722, 0.011252084746956825, 0.01122584380209446, 0.011216048151254654, 0.011204490438103676, 0.011203117668628693, 0.01117485947906971, 0.01117344293743372, 0.011145292781293392, 0.011137993074953556, 0.011128612793982029, 0.011123239994049072, 0.011122280731797218, 0.011065744794905186, 0.011053262278437614, 0.011045967228710651, 0.011041177436709404, 0.011033336631953716, 0.01102971937507391, 0.0110141197219491, 0.01100961398333311, 0.011002525687217712, 0.010937424376606941, 0.01093329582363367, 0.010918675921857357, 0.010917853564023972, 0.010890142060816288, 0.01088369358330965, 0.010871977545320988, 0.010870742611587048, 0.010863195173442364, 0.010844682343304157, 0.01084016915410757, 0.010835953988134861, 0.010834810324013233, 0.010826902464032173, 0.010796850547194481, 0.010746038518846035, 0.010692491196095943, 0.010686952620744705, 0.010679351165890694, 0.010655333288013935, 0.010604050010442734, 0.010574583895504475, 0.010439733043313026, 0.010402928106486797, 0.010294477455317974]}\n"
+      "{'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"
      ]
     }
    ],
@@ -73,15 +73,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "id": "521d9118-b59d-4cc6-b637-20202eaf8f33",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7866\n",
+      "Running on local URL:  http://127.0.0.1:7860\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -89,7 +91,7 @@
     {
      "data": {
       "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7866/\" 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>"
@@ -116,6 +118,48 @@
     "if __name__ == \"__main__\":\n",
     "    demo.launch()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6526d18-6ba6-4a66-8310-21337b832d84",
+   "metadata": {},
+   "source": [
+    "Simple app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5496ded9-7294-4da4-af05-00e5846cdd04",
+   "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()"
+   ]
   }
  ],
  "metadata": {

space/space/space/space/space/space/space/space/space/packing_list_api.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "id": "05a29daa-b70e-4c7c-ba03-9ab641f424cb",
    "metadata": {},
    "outputs": [],
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "id": "21b4f8b6-e774-45ad-8054-bf5db2b7b07c",
    "metadata": {},
    "outputs": [
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 4,
    "id": "c5f75916-aaf2-4ca7-8d1a-070579940952",
    "metadata": {},
    "outputs": [
@@ -114,66 +114,6 @@
     "print(output)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "8a6318c1-fa5f-4d16-8507-eaebe6294ac0",
-   "metadata": {},
-   "source": [
-    "**Use batches of 10 labels and combine results**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "fe42a222-5ff4-4442-93f4-42fc22001af6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'sequence': \"I'm going on a 2-week hiking trip in the Alps during winter.\", 'labels': ['Map', 'Backpack', 'Tent', 'Thermal clothing', 'Hiking boots', 'Flashlight', 'Gloves', 'Camping stove', 'Water filter', 'Sleeping bag'], 'scores': [0.30358555912971497, 0.12884855270385742, 0.10985139012336731, 0.10500500351190567, 0.10141848027706146, 0.08342219144105911, 0.0704946368932724, 0.05127469450235367, 0.024876652285456657, 0.021222807466983795]}\n",
-      "{'sequence': \"I'm going on a 2-week hiking trip in the Alps during winter.\", 'labels': ['Ski jacket', 'Snow boots', 'Hand warmers', 'Beanie', 'Ski goggles', 'Flip-flops', 'First aid kit', 'Sunscreen', 'Swimsuit', 'Lip balm'], 'scores': [0.20171622931957245, 0.1621972620487213, 0.12313881516456604, 0.10742709040641785, 0.09418268501758575, 0.08230196684598923, 0.07371978461742401, 0.06208840385079384, 0.05506424233317375, 0.038163457065820694]}\n",
-      "\n",
-      "Recommended packing list: ['Map', 'Backpack', 'Tent', 'Thermal clothing', 'Hiking boots', 'Ski jacket', 'Snow boots', 'Hand warmers', 'Beanie']\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "input_text = \"I'm going on a 2-week hiking trip in the Alps during winter.\"\n",
-    "\n",
-    "\n",
-    "# Define the full list of possible packing items (split into groups of 10)\n",
-    "candidate_labels = [\n",
-    "    [\"Hiking boots\", \"Tent\", \"Sleeping bag\", \"Camping stove\", \"Backpack\",\n",
-    "     \"Water filter\", \"Flashlight\", \"Thermal clothing\", \"Gloves\", \"Map\"],\n",
-    "    \n",
-    "    [\"Swimsuit\", \"Sunscreen\", \"Flip-flops\", \"Ski jacket\", \"Ski goggles\",\n",
-    "     \"Snow boots\", \"Beanie\", \"Hand warmers\", \"Lip balm\", \"First aid kit\"]\n",
-    "]\n",
-    "\n",
-    "# Run classification in batches\n",
-    "packing_list = []\n",
-    "for batch in candidate_labels:\n",
-    "    result = query({\"inputs\": input_text, \"parameters\": {\"candidate_labels\": batch}})\n",
-    "    print(result)\n",
-    "    for label, score in zip(result[\"labels\"], result[\"scores\"]):\n",
-    "        if score > 0.1:  # Adjust threshold as needed\n",
-    "            packing_list.append(label)\n",
-    "\n",
-    "# Print the final packing list\n",
-    "print(\"\\nRecommended packing list:\", packing_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "660072ea-b72f-4bee-a9ed-81019775ae85",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "edf44387-d166-4e0f-a8ad-621230aee115",
@@ -184,92 +124,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "id": "d0d8f7c0-c2d9-4fbe-b1a7-699a5b99466c",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5e371dee58d64e7b8bf6635e0e88f8db",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/1.72k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d479e18a65314ad5927ea2bf7453db7c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/558M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "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"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8dc911686edb4b15baa880ae657c163d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e60a6df28292441bb5317ef80c9de795",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c7eaab50789b42a796d0deb3008f247e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "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"
      ]
@@ -284,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "id": "4682d620-c9a6-40ad-ab4c-268ee0ef7212",
    "metadata": {},
    "outputs": [
@@ -299,7 +163,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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