Create main.py

main.py

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+# Import libraries
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from torch.utils.data import Dataset, DataLoader
+import os
+
+# Define dataset and functions
+class TextDataset(Dataset):
+    def __init__(self, file_path, block_size):
+        self.block_size = block_size
+        with open(file_path, 'r', encoding='utf-8') as f:
+            self.examples = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
+
+        self.tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')
+        self.special_tokens_dict = {'pad_token': '<PAD>'}
+        self.num_added_toks = self.tokenizer.add_special_tokens(self.special_tokens_dict)
+
+    def __len__(self):
+        return len(self.examples)
+
+    def __getitem__(self, idx):
+        text = self.examples[idx]
+        tokenized_text = self.tokenizer.encode(text)
+        if len(tokenized_text) > self.block_size:
+            tokenized_text = tokenized_text[:self.block_size]
+        tokenized_text += [self.tokenizer.pad_token_id] * (self.block_size - len(tokenized_text))
+        return torch.tensor(tokenized_text)
+
+# Define training
+def train():
+    train_dataset = TextDataset('path/to/your/text/file.txt', block_size=512)
+    train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
+
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model = GPT2LMHeadModel.from_pretrained('gpt2-medium')
+    model.resize_token_embeddings(len(train_dataset.tokenizer))
+
+    model.to(device)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
+    criterion = torch.nn.CrossEntropyLoss(ignore_index=train_dataset.tokenizer.pad_token_id)
+
+    epochs = 5
+
+    for epoch in range(epochs):
+        model.train()
+        total_loss = 0
+        for batch in train_loader:
+            batch = batch.to(device)
+            optimizer.zero_grad()
+            outputs = model(input_ids=batch[:, :-1], labels=batch[:, 1:])
+            loss = criterion(outputs.logits.view(-1, outputs.logits.shape[-1]), batch[:, 1:].view(-1))
+            loss.backward()
+            optimizer.step()
+            total_loss += loss.item()
+
+        print(f'Epoch {epoch+1}, Loss: {total_loss/len(train_loader):.4f}')
+
+    model.save_pretrained('finetuned_model')
+
+# TRAIN THE MODEL!!!
+train()