Create chat.py

chat.py

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+import torch
+from torch import nn
+from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments
+
+class CustomTrainer(Trainer):
+    def compute_loss(self, model, inputs, return_outputs=False):
+        labels = inputs.get("labels")
+        outputs = model(**inputs)
+        logits = outputs.get("logits")
+        loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0]))
+        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
+        return (loss, outputs) if return_outputs else loss
+
+# Load pre-trained model
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
+
+# Load tokenizer
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+# Tokenize your dataset (you would need to define this yourself)
+# This is a placeholder and will not run
+train_encodings = tokenizer(train_texts, truncation=True, padding=True)
+train_labels = torch.tensor(train_labels)
+
+# Define a PyTorch Dataset from the encodings and the labels
+class MyDataset(torch.utils.data.Dataset):
+    def __init__(self, encodings, labels):
+        self.encodings = encodings
+        self.labels = labels
+
+    def __getitem__(self, idx):
+        item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()}
+        item['labels'] = torch.tensor(self.labels[idx])
+        return item
+
+    def __len__(self):
+        return len(self.labels)
+
+# Create a Dataset object
+train_dataset = MyDataset(train_encodings, train_labels)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir='./results',
+    num_train_epochs=3,
+    per_device_train_batch_size=16,
+    warmup_steps=500,
+    weight_decay=0.01,
+)
+
+# Initialize the trainer
+trainer = CustomTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+)
+
+# Train the model
+trainer.train()