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# Template for model training script for {{phase_name}}
from transformers import AutoModelForSequenceClassification, AutoTokenizer, TrainingArguments, Trainer
from datasets import load_dataset # Example - datasets library
import torch # Example - PyTorch
# Add other necessary imports
def train_model(processed_dataset_path, model_name="bert-base-uncased", output_dir="./model_output"):
"""
Trains a model on the processed dataset.
"""
try:
# Load processed dataset (replace with your actual dataset loading)
dataset = load_dataset('csv', data_files=processed_dataset_path) # Example: CSV dataset loading, replace with your dataset format
print("Dataset loaded. Preparing model and training...")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2) # Example: binary classification
def tokenize_function(examples):
return tokenizer(examples["text_column"], padding="max_length", truncation=True) # Example: tokenize 'text_column'
tokenized_datasets = dataset.map(tokenize_function, batched=True)
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=3, # Example epochs
per_device_train_batch_size=16, # Example batch size
per_device_eval_batch_size=64, # Example batch size
warmup_steps=500, # Example warmup steps
weight_decay=0.01, # Example weight decay
logging_dir='./logs', # Directory for logs
logging_steps=10,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"], # Assuming 'train' split exists
eval_dataset=tokenized_datasets["validation"], # Assuming 'validation' split exists - optional
tokenizer=tokenizer,
)
trainer.train()
print(f"Model training completed. Model saved to {output_dir}")
except Exception as e:
print(f"Error during model training: {e}")
if __name__ == "__main__":
processed_data_filepath = "data/processed_dataset.csv" # Replace with your processed data path
model_output_directory = "models/fine_tuned_model" # Replace with your desired output directory
base_model_name = "bert-base-uncased" # Replace with your base model name
train_model(processed_data_filepath, model_name=base_model_name, output_dir=model_output_directory) |