How to fine-tune a language model for a specific task

Short answer

Fine-tuning adapts a pretrained language model to a downstream task by continuing training on task-specific labeled data.

Steps

1. Select a pretrained model appropriate for your language and domain.
2. Prepare labeled data and split it into training and validation sets.
3. Add a task-specific head such as a classification layer on top of the base model.
4. Train with a low learning rate to preserve pretrained knowledge while adapting to the new task.
5. Evaluate and iterate, adjusting hyperparameters and data augmentation as needed.

Tips

• Use differential learning rates with lower rates for lower layers.
• Freeze embedding layers when the dataset is very small.
• Apply dynamic padding and truncation to optimize batch efficiency.
• Save checkpoints frequently to preserve the best validation performance.

Common issues

• Catastrophic forgetting where the model loses general language understanding.
• Overfitting when the downstream dataset is small relative to model size.
• Mismatched tokenizers between pretraining and fine-tuning causing representation gaps.
• Long training times without early stopping wasting compute resources.

Example

import torch
import torch.nn as nn

model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Dropout(0.2),
    nn.Linear(256, 10)
)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

This snippet defines a simple neural network with dropout for regularization, a cross-entropy loss, and the Adam optimizer in PyTorch.