Fine-Tuning BertForSequenceClassification
Peserta akan memuat checkpoint BERT pralatih, menambahkan kepala klasifikasi, membuat PyTorch DataLoader, dan melakukan fine-tuning selama dua epoch pada dataset IMDB.
Fine-Tuning BertForSequenceClassification adalah pelajaran Machine Learning Academy gratis di CoddyKit. Ini adalah pelajaran 3 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Machine Learning Academy, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Machine Learning Academy mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
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)Loading the IMDB Dataset
The IMDB dataset contains 50,000 movie reviews labelled positive (1) or negative (0). We use the Hugging Face datasets library to download and split it. The dataset object behaves like a dictionary of lists and can be mapped over with a tokenization function. We use a 25k train / 25k test split provided by the original dataset authors.
from datasets import load_dataset
from transformers import BertTokenizer
dataset = load_dataset('imdb')
print(dataset) # DatasetDict with train/test splits
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
def tokenize_fn(examples):
return tokenizer(
examples['text'],
truncation=True,
padding='max_length',
max_length=256
)
tokenized = dataset.map(tokenize_fn, batched=True)
tokenized.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])Creating PyTorch DataLoaders
After tokenisation, wrap the Hugging Face dataset in PyTorch DataLoader objects. The batch_size controls how many examples the model processes per forward pass; 16 or 32 is typical for BERT given GPU memory constraints. Shuffle the training set each epoch so the model does not overfit to example order, but keep the validation set fixed for reproducible evaluation.
from torch.utils.data import DataLoader
train_dataset = tokenized['train'].select(range(2000)) # small subset for demo
test_dataset = tokenized['test'].select(range(500))
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
print('Train batches:', len(train_loader))
print('Test batches:', len(test_loader))Setting Up the Optimizer
Fine-tuning BERT uses the AdamW optimizer with a small learning rate (typically 2e-5 to 5e-5). Using a large learning rate destroys the pre-trained representations (catastrophic forgetting). Weight decay (L2 regularisation) is applied to non-bias parameters to reduce overfitting. Hugging Face's get_linear_schedule_with_warmup is commonly used to warm up the LR over the first 10% of steps then decay linearly.
from torch.optim import AdamW
from transformers import get_linear_schedule_with_warmup
optimizer = AdamW(model.parameters(), lr=2e-5, weight_decay=0.01)
num_epochs = 2
num_steps = len(train_loader) * num_epochs
warmup_steps = int(0.1 * num_steps)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=num_steps
)The Fine-Tuning Training Loop
The PyTorch training loop for fine-tuning BERT follows the standard pattern: iterate over batches, compute the forward pass, extract the loss from the model output (BertForSequenceClassification returns loss automatically when labels are passed), call loss.backward(), clip gradients to prevent explosion, and step the optimizer and scheduler. Training BERT for 2 epochs on IMDB typically yields ~92% accuracy.
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
for epoch in range(2):
model.train()
total_loss = 0
for batch in train_loader:
optimizer.zero_grad()
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
labels = batch['label'].to(device)
outputs = model(input_ids=input_ids,
attention_mask=attention_mask,
labels=labels)
loss = outputs.loss
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
total_loss += loss.item()
print(f'Epoch {epoch+1} loss: {total_loss/len(train_loader):.4f}')Evaluating on the Validation Set
During evaluation, call model.eval() to disable dropout and use torch.no_grad() to skip gradient computation, saving memory and speeding up inference. Extract logits from the model output (raw scores before softmax), apply argmax to get predicted class indices, and compare against ground-truth labels to compute accuracy.
import torch
from sklearn.metrics import accuracy_score
model.eval()
all_preds, all_labels = [], []
with torch.no_grad():
for batch in test_loader:
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
labels = batch['label'].to(device)
outputs = model(input_ids=input_ids,
attention_mask=attention_mask)
preds = torch.argmax(outputs.logits, dim=1)
all_preds.extend(preds.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
print('Accuracy:', accuracy_score(all_labels, all_preds))Saving the Fine-Tuned Model
After fine-tuning, save the model and tokenizer together so they can be reloaded for inference without re-training. model.save_pretrained(path) saves the model weights and config; tokenizer.save_pretrained(path) saves the vocabulary and tokenization settings. Reload with from_pretrained(path) in any new Python session.
import os
save_dir = './bert_imdb_finetuned'
os.makedirs(save_dir, exist_ok=True)
model.save_pretrained(save_dir)
tokenizer.save_pretrained(save_dir)
print('Model saved to', save_dir)
# Reload in a new session:
# from transformers import BertForSequenceClassification, BertTokenizer
# model = BertForSequenceClassification.from_pretrained(save_dir)
# tokenizer = BertTokenizer.from_pretrained(save_dir)Using the Trainer API
Hugging Face's Trainer class encapsulates the training loop, evaluation, checkpointing, and logging in a single high-level API. Define a TrainingArguments object with learning rate, batch size, and number of epochs, then call trainer.train(). The Trainer handles gradient clipping, LR scheduling, and mixed-precision training automatically, reducing boilerplate significantly.
from transformers import Trainer, TrainingArguments
import numpy as np
from sklearn.metrics import accuracy_score
def compute_metrics(eval_pred):
logits, labels = eval_pred
preds = np.argmax(logits, axis=1)
return {'accuracy': accuracy_score(labels, preds)}
training_args = TrainingArguments(
output_dir='./bert_trainer',
num_train_epochs=2,
per_device_train_batch_size=16,
per_device_eval_batch_size=32,
learning_rate=2e-5,
evaluation_strategy='epoch',
save_strategy='epoch',
load_best_model_at_end=True
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=test_dataset,
compute_metrics=compute_metrics
)
trainer.train()Monitoring Training with TensorBoard
Pass report_to='tensorboard' in TrainingArguments to log loss and metrics to TensorBoard automatically. Launch TensorBoard with tensorboard --logdir ./bert_trainer/runs in a terminal to view real-time loss curves, learning rate schedules, and evaluation metrics. Monitoring training loss vs validation loss helps you detect overfitting early and stop training at the right epoch.
from transformers import TrainingArguments
training_args = TrainingArguments(
output_dir='./bert_trainer',
num_train_epochs=3,
per_device_train_batch_size=16,
learning_rate=2e-5,
evaluation_strategy='steps',
eval_steps=100,
logging_steps=50,
report_to='tensorboard', # enable TensorBoard logging
logging_dir='./bert_trainer/runs'
)
# Then: tensorboard --logdir ./bert_trainer/runsChoosing How Many Layers to Freeze
Sometimes the dataset is too small to fine-tune all 12 BERT layers without overfitting. A common strategy is to freeze the lower layers (which learn general linguistic features) and only update the upper layers (which learn task-specific features). Freeze by setting requires_grad=False on selected parameter groups. Monitor validation accuracy to choose the optimal freezing depth for your dataset size.
from transformers import BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
# Freeze embedding layer and first 6 encoder layers
for name, param in model.named_parameters():
if 'embeddings' in name or 'encoder.layer.0' in name or \
'encoder.layer.1' in name or 'encoder.layer.2' in name:
param.requires_grad = False
# Count trainable parameters
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'Trainable parameters: {trainable:,}')Quick Check
Test your understanding of Machine Learning with Python concepts from this lesson.
Lesson Recap
In this lesson you learned: BertForSequenceClassification adds a linear head on [CLS] for classification tasks, fine-tuning uses AdamW with a very small learning rate to avoid catastrophic forgetting, and the Hugging Face Trainer API simplifies the training loop with built-in evaluation and checkpointing. Next up we look at how to extract predictions from logits and evaluate the fine-tuned model with accuracy and F1.
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Fine-Tuning BertForSequenceClassification” gratis?
Ya — teks lengkap “Fine-Tuning BertForSequenceClassification” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Machine Learning Academy, upgrade ke CoddyKit PRO. Kursus Machine Learning Academy mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Fine-Tuning BertForSequenceClassification”?
Peserta akan memuat checkpoint BERT pralatih, menambahkan kepala klasifikasi, membuat PyTorch DataLoader, dan melakukan fine-tuning selama dua epoch pada dataset IMDB. Kamu berlatih Machine Learning Academy dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
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Semua pelajaran dalam kursus ini
- Arsitektur Transformer: Attention, Token, dan Konteks
- Tokenizer Hugging Face: Mengodekan Teks untuk BERT
- Fine-Tuning BertForSequenceClassification
- Evaluasi dan Inferensi: Dari Logit ke Label Prediksi