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import os
from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoTokenizer
from datasets import load_dataset
import json
# Load configuration
with open('../config/config.json') as f:
config = json.load(f)
# Load dataset
dataset = load_dataset('csv', data_files={'train': '../data/train.csv', 'validation': '../data/valid.csv'})
# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(config['model_name'], num_labels=config['num_labels'])
tokenizer = AutoTokenizer.from_pretrained(config['model_name'])
# Tokenize dataset
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
# Training arguments
training_args = TrainingArguments(
output_dir='./results',
learning_rate=config['learning_rate'],
per_device_train_batch_size=config['batch_size'],
num_train_epochs=config['num_epochs'],
evaluation_strategy="epoch",
save_strategy="epoch",
logging_dir='./logs'
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets['train'],
eval_dataset=tokenized_datasets['validation'],
tokenizer=tokenizer
)
trainer.train()
trainer.save_model('../model')
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