👀 Views: 210 💬 Answers: 1 📅 Created: 2025-06-07

gpt-2 hugging-face fine-tuning transformers Python

I'm currently trying to fine-tune a GPT-2 model using the Hugging Face Transformers library (version 4.20.1) for a specific text generation task, but I'm encountering unexpected results. After loading my dataset, which is a simple text file containing tweets, I've set up my training loop but the generated text seems to lack coherence and does not reflect the style of the training data. Here's the code snippet I'm using to set up the model and training: ```python from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments import torch # Load model and tokenizer model_name = 'gpt2' tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name) # Load dataset train_data = open('tweets.txt').readlines() train_encodings = tokenizer(train_data, return_tensors='pt', padding=True, truncation=True) # Prepare dataset for Trainer class TweetDataset(torch.utils.data.Dataset): def __init__(self, encodings): self.encodings = encodings def __getitem__(self, idx): return {'input_ids': self.encodings['input_ids'][idx], 'attention_mask': self.encodings['attention_mask'][idx]} def __len__(self): return len(self.encodings['input_ids']) train_dataset = TweetDataset(train_encodings) # Training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=2, save_steps=10_000, save_total_limit=2, logging_dir='./logs', ) # Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, ) # Start training trainer.train() ``` I've tried experimenting with different parameters in `TrainingArguments`, such as increasing the number of epochs and adjusting the batch size, but it hasn't improved the quality of the generated text. Additionally, I've confirmed that my input text is processed correctly and the tokenizer is not truncating important parts of the tweets. When I generate text after training, I use the following code: ```python input_text = "Today was a great day" input_ids = tokenizer.encode(input_text, return_tensors='pt') output = model.generate(input_ids, max_length=50) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` However, the output just seems disjointed and doesn't capture the essence of the tweets. I'm wondering if I'm missing something fundamental in the fine-tuning process or if there are specific best practices for ensuring that the model learns effectively from this type of data. Any guidance would be greatly appreciated!