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from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset
import torch
import os
# Load Dataset
dataset = load_dataset('csv', data_files={'train': './data/raw_data.csv'}, delimiter=",")
# Load Pretrained Tokenizer and Model
model_name = "xlm-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
# Tokenization
def preprocess_function(examples):
return tokenizer(examples['text'], truncation=True, padding=True)
encoded_dataset = dataset.map(preprocess_function, batched=True)
# Training Arguments
training_args = TrainingArguments(
output_dir="./checkpoints",
num_train_epochs=3,
per_device_train_batch_size=8,
save_steps=100,
save_total_limit=1,
logging_dir="./logs",
logging_steps=10,
evaluation_strategy="no",
push_to_hub=False,
load_best_model_at_end=False
)
# Trainer Setup
trainer = Trainer(
model=model,
args=training_args,
train_dataset=encoded_dataset['train']
)
# Start Training
trainer.train()
# Save Final Fine-tuned Model
save_directory = "./models/fine_tuned_xlm_roberta"
os.makedirs(save_directory, exist_ok=True)
model.save_pretrained(save_directory)
tokenizer.save_pretrained(save_directory)
# Quantize Model (Make Lightweight)
def quantize_model(model_path):
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model.to(torch.device('cpu'))
model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
quantized_model_path = model_path + "_quantized"
os.makedirs(quantized_model_path, exist_ok=True)
model.save_pretrained(quantized_model_path)
tokenizer.save_pretrained(quantized_model_path)
print(f"Quantized model saved to {quantized_model_path}")
quantize_model(save_directory)
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