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import torch
from torch.utils.data import DataLoader, Dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
import pandas as pd
class CustomDataset(Dataset):
def __init__(self, data, tokenizer, max_len):
self.data = data
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.data)
def __getitem__(self, index):
row = self.data.iloc[index]
inputs = self.tokenizer.encode_plus(
row['text'],
add_special_tokens=True,
max_length=self.max_len,
padding='max_length',
return_attention_mask=True,
return_tensors='pt'
)
return {
'input_ids': inputs['input_ids'].flatten(),
'attention_mask': inputs['attention_mask'].flatten(),
'labels': torch.tensor(row['label'], dtype=torch.long)
}
def train_model(model_name, train_data_path, output_dir, epochs=3, batch_size=16, max_len=128):
# Load the dataset
df = pd.read_csv(train_data_path)
tokenizer = AutoTokenizer.from_pretrained(model_name)
dataset = CustomDataset(df, tokenizer, max_len)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
# Load the model
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(df['label'].unique()))
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=epochs,
per_device_train_batch_size=batch_size,
evaluation_strategy="epoch",
save_total_limit=2,
save_steps=10_000,
logging_dir=f'{output_dir}/logs',
)
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
)
# Train the model
trainer.train()
# Save the model
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
model_name = "bert-base-uncased"
train_data_path = "data/example_dataset.csv"
output_dir = "output"
train_model(model_name, train_data_path, output_dir) |