How can I fine-tune a pre-trained model for my specific task?

How can I fine-tune a pre-trained model for my specific task?

Fine-tuning a pre-trained model involves adjusting its weights on a new dataset specific to your task. What model are you using?

I'm using BERT for a text classification task.

Great choice! You can start by loading the pre-trained BERT model and then use your labeled dataset to train it further. Do you need help with the code?

Yes, that would be helpful. Can you provide a code snippet?

Sure! Here's a simple example using Hugging Face's Transformers library:

from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments

# Load pre-trained model and tokenizer
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Prepare your dataset and training arguments
# ... (your dataset preparation code here)

training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=16,
    save_steps=10_000,
    save_total_limit=2,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

trainer.train()

Let me know if you need further assistance!