Please work

app.py

@@ -83,29 +83,6 @@ def training():
     train_data = dataset["train"]
     # For agility we only 1/2 of our available data
     n_examples = dataset["train"].num_rows // 2
-    # n_remaining = dataset["train"].num_rows - n_examples
-    # dataset_clean = {}
-    # # dataset_0 = []
-    # # dataset_1 = []
-    # # dataset_2 = []
-    # # dataset_3 = []
-    # for i in range(n_examples):
-    #     dataset_clean[i] = {}
-    #     dataset_clean[i]["text"] = normalize(train_data[i]["text"], lowercase=True, remove_stopwords=True)
-    #     dataset_clean[i]["label"] = train_data[i]["label"]
-        # if train_data[i]["label"] == 0:
-        #     dataset_0.append(dataset_clean[i])
-        # elif train_data[i]["label"] == 1:
-        #     dataset_1.append(dataset_clean[i])
-        # elif train_data[i]["label"] == 2:
-        #     dataset_2.append(dataset_clean[i])
-        # elif train_data[i]["label"] == 3:
-        #     dataset_3.append(dataset_clean[i])
-    # n_0 = len(dataset_0) // 2
-    # n_1 = len(dataset_1) // 2
-    # n_2 = len(dataset_2) // 2
-    # n_3 = len(dataset_3) // 2
-    # print("Label lengths:", len(dataset_0), len(dataset_1), len(dataset_2), len(dataset_3))
     
     for i in range(n_examples):
         example = train_data[i]
@@ -113,30 +90,6 @@ def training():
         # print(example["text"])
         train_examples.append(InputExample(texts=[example['text']], label=example['label']))
         
-    # for i in range(n_0):
-    #     example = dataset_0[i]
-    #     # example_opposite = dataset_0[-(i)]
-    #     # print(example["text"])
-    #     train_examples.append(InputExample(texts=[example['text']], label=0))
-        
-    # for i in range(n_1):
-    #     example = dataset_1[i]
-    #     # example_opposite = dataset_1[-(i)]
-    #     # print(example["text"])
-    #     train_examples.append(InputExample(texts=[example['text']], label=1))
-        
-    # for i in range(n_2):
-    #     example = dataset_2[i]
-    #     # example_opposite = dataset_2[-(i)]
-    #     # print(example["text"])
-    #     train_examples.append(InputExample(texts=[example['text']], label=2))
-        
-    # for i in range(n_3):
-    #     example = dataset_3[i]
-    #     # example_opposite = dataset_3[-(i)]
-    #     # print(example["text"])
-    #     train_examples.append(InputExample(texts=[example['text']], label=3))
-        
     train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=25)
     
     print("END DATALOADER")

train.py

@@ -92,59 +92,13 @@ def training():
     train_data = dataset["train"]
     # For agility we only 1/2 of our available data
     n_examples = dataset["train"].num_rows // 2
-    # n_remaining = dataset["train"].num_rows - n_examples
-    # dataset_clean = {}
-    # # dataset_0 = []
-    # # dataset_1 = []
-    # # dataset_2 = []
-    # # dataset_3 = []
-    # for i in range(n_examples):
-    #     dataset_clean[i] = {}
-    #     dataset_clean[i]["text"] = normalize(train_data[i]["text"], lowercase=True, remove_stopwords=True)
-    #     dataset_clean[i]["label"] = train_data[i]["label"]
-        # if train_data[i]["label"] == 0:
-        #     dataset_0.append(dataset_clean[i])
-        # elif train_data[i]["label"] == 1:
-        #     dataset_1.append(dataset_clean[i])
-        # elif train_data[i]["label"] == 2:
-        #     dataset_2.append(dataset_clean[i])
-        # elif train_data[i]["label"] == 3:
-        #     dataset_3.append(dataset_clean[i])
-    # n_0 = len(dataset_0) // 2
-    # n_1 = len(dataset_1) // 2
-    # n_2 = len(dataset_2) // 2
-    # n_3 = len(dataset_3) // 2
-    # print("Label lengths:", len(dataset_0), len(dataset_1), len(dataset_2), len(dataset_3))
     
     for i in range(n_examples):
         example = train_data[i]
         # example_opposite = dataset_clean[-(i)]
         # print(example["text"])
         train_examples.append(InputExample(texts=[example['text']], label=example['label']))
-        
-    # for i in range(n_0):
-    #     example = dataset_0[i]
-    #     # example_opposite = dataset_0[-(i)]
-    #     # print(example["text"])
-    #     train_examples.append(InputExample(texts=[example['text']], label=0))
-        
-    # for i in range(n_1):
-    #     example = dataset_1[i]
-    #     # example_opposite = dataset_1[-(i)]
-    #     # print(example["text"])
-    #     train_examples.append(InputExample(texts=[example['text']], label=1))
-        
-    # for i in range(n_2):
-    #     example = dataset_2[i]
-    #     # example_opposite = dataset_2[-(i)]
-    #     # print(example["text"])
-    #     train_examples.append(InputExample(texts=[example['text']], label=2))
-        
-    # for i in range(n_3):
-    #     example = dataset_3[i]
-    #     # example_opposite = dataset_3[-(i)]
-    #     # print(example["text"])
-    #     train_examples.append(InputExample(texts=[example['text']], label=3))
+    
         
     train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=25)