fine-tuning

import tensorflow as tf
from transformers import GPT2LMHeadModel, GPT2Tokenizer, TextDataset, DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments

# Load pre-trained model and tokenizer
model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Prepare domain-specific dataset
train_dataset = TextDataset(
    tokenizer=tokenizer,
    file_path="legal_corpus.txt",
    block_size=128
)

data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer, mlm=False
)

# Set up training arguments
training_args = TrainingArguments(
    output_dir="./legal_gpt2",
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=4,
    save_steps=10_000,
    save_total_limit=2,
)

# Create Trainer instance
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_dataset,
)

# Fine-tune the model
trainer.train()