Fine-Tuning BertForSequenceClassification
Learners will load a pre-trained BERT checkpoint, add a classification head, create a PyTorch DataLoader, and fine-tune for two epochs on an IMDB dataset.
What Is Fine-Tuning?
Fine-tuning takes a pre-trained model that already understands language structure and adapts it to a specific task with a small labelled dataset. BERT pre-trained on 3.3 billion words already knows grammar, semantics, and world knowledge. Fine-tuning adds a task-specific head (e.g., a classification layer) and trains the entire model end-to-end on your labelled data for a few epochs, achieving state-of-the-art results with far less data and compute than training from scratch.
BertForSequenceClassification Overview
BertForSequenceClassification is a BERT model with a linear classification head on top of the [CLS] token's final hidden state. It is the standard Hugging Face class for sentiment analysis, topic classification, and any task that assigns a single label to an entire text. The head is initialised randomly and trained alongside the BERT backbone during fine-tuning.
from transformers import BertForSequenceClassification
import torch
# 2 classes: negative (0) and positive (1)
model = BertForSequenceClassification.from_pretrained(
'bert-base-uncased',
num_labels=2
)
print(model.config.num_labels) # 2
print(model.classifier) # Linear(in=768, out=2)All lessons in this course
- Transformer Architecture: Attention, Tokens, and Context
- Hugging Face Tokenizers: Encoding Text for BERT
- Fine-Tuning BertForSequenceClassification
- Evaluation and Inference: From Logits to Predicted Labels