from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset
# 1. Load a pre-trained model and tokenizer
model_name = "bert-base-uncased" # Example model
tokenizer = AutoTokenizer.from_pretrained(model_name)
# For sequence classification, you'd load AutoModelForSequenceClassification
# For other tasks (e.g., QA, token classification), use the appropriate AutoModelForXxx class
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2) # Example: binary classification
# 2. Load and preprocess your task-specific dataset
# Example: Load a sentiment analysis dataset (e.g., IMDB)
# In a real scenario, you'd use your own dataset.
dataset_name = "imdb" # Example
raw_datasets = load_dataset(dataset_name)
def tokenize_function(examples):
# Adjust 'text' to the actual text column name in your dataset
return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
# Prepare datasets for training
# Small subsets for demonstration purposes
train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) # Use more data for real tasks
eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(500)) # Use more data for real tasks
# 3. Define Training Arguments
# These arguments control various aspects of the training process
training_args = TrainingArguments(
output_dir="./results", # Directory to save model checkpoints and logs
num_train_epochs=3, # Total number of training epochs
per_device_train_batch_size=8, # Batch size per device during training
per_device_eval_batch_size=16, # Batch size for evaluation
warmup_steps=500, # Number of warmup steps for learning rate scheduler
weight_decay=0.01, # Strength of weight decay
logging_dir="./logs", # Directory for storing logs
logging_steps=100, # Log every X updates steps
evaluation_strategy="epoch", # Evaluate at the end of each epoch
save_strategy="epoch", # Save checkpoint at the end of each epoch
load_best_model_at_end=True, # Load the best model checkpoint at the end of training
# For PEFT methods like LoRA, you'd integrate with libraries like 'peft' from Hugging Face
# and modify how the model is prepared and trained.
)
# 4. Initialize the Trainer
# The Trainer class handles the training and evaluation loop
trainer = Trainer(
model=model, # The instantiated Transformers model to be trained
args=training_args, # Training arguments, defined above
train_dataset=train_dataset, # Training dataset
eval_dataset=eval_dataset, # Evaluation dataset
# You can also pass a compute_metrics function for custom evaluation metrics
)
# 5. Start Fine-tuning
# trainer.train()
print("Fine-tuning setup complete. Uncomment trainer.train() to start.")
print("Note: This is a conceptual example. For actual LoRA/PEFT, integrate with the 'peft' library.")
# Example of PEFT integration (conceptual, requires 'peft' library installed)
# from peft import LoraConfig, get_peft_model, TaskType
#
# if False: # Set to True to see conceptual PEFT integration
# peft_config = LoraConfig(
# task_type=TaskType.SEQ_CLS, # Task type (e.g., sequence classification)
# inference_mode=False,
# r=8, # LoRA rank
# lora_alpha=32, # LoRA alpha
# lora_dropout=0.1,
# # target_modules=["query", "value"] # Specify target modules for LoRA if needed
# )
#
# # Re-load base model for PEFT to ensure it's not already modified
# base_model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
# peft_model = get_peft_model(base_model, peft_config)
# peft_model.print_trainable_parameters()
#
# peft_trainer = Trainer(
# model=peft_model,
# args=training_args,
# train_dataset=train_dataset,
# eval_dataset=eval_dataset,
# )
# # peft_trainer.train()
# print("PEFT (LoRA) fine-tuning setup complete. Uncomment peft_trainer.train() to start.")