How to Fine-tune NLP Models?

NLP model fine-tuning is a key technique for adapting pre-trained models to specific tasks. Through fine-tuning, you can leverage the general knowledge learned by pre-trained models to achieve better performance on target tasks.

Basic Concepts of Fine-tuning

Definition

• Train on top of pre-trained models
• Use small-scale datasets for target tasks
• Adjust model parameters to fit specific tasks

Advantages

• Reduce training data requirements
• Accelerate convergence
• Improve model performance
• Lower computational costs

Fine-tuning Strategies

1. Full Parameter Fine-tuning

Method

• Unfreeze all model parameters
• Train on target task data
• Usually use smaller learning rate

Advantages

• Fully utilize pre-trained knowledge
• Strong adaptability
• Typically best performance

Disadvantages

• High computational cost
• Requires large memory
• May overfit

Applicable Scenarios

• Large-scale target task data
• Sufficient computational resources
• Pursuing best performance

2. Partial Layer Fine-tuning

Method

• Only unfreeze some layers (usually top layers)
• Freeze bottom layer parameters
• Use smaller learning rate for top layers

Advantages

• Reduce computational cost
• Lower overfitting risk
• Preserve bottom-level general features

Disadvantages

• Performance may be slightly lower than full fine-tuning
• Need to select appropriate number of layers

Applicable Scenarios

• Medium-scale data
• Limited computational resources
• Task similar to pre-training task

3. Parameter-Efficient Fine-tuning (PEFT)

LoRA (Low-Rank Adaptation)

• Add low-rank decomposition to weight matrices
• Only train low-rank matrices
• Significantly reduce trainable parameters

Adapter

• Insert small adapter modules between layers
• Only train adapter parameters
• Keep original model parameters unchanged

Prefix Tuning

• Add trainable prefixes before input
• Only optimize prefix vectors
• Suitable for generation tasks

Prompt Tuning

• Similar to Prefix Tuning
• Simpler prefix representation
• Suitable for large language models

Advantages

• Greatly reduce trainable parameters
• Lower storage requirements
• Fast task switching

Disadvantages

• Performance may be slightly lower than full fine-tuning
• Relatively complex implementation

4. Instruction Fine-tuning

Method

• Train with instruction-response pairs
• Improve model's ability to follow instructions
• Suitable for dialogue and generation tasks

Data Format

shell
Instruction: Please translate the following sentence into English
Input: Natural Language Processing is interesting
Output: 自然语言处理很有趣

Advantages

• Improve model versatility
• Enhance zero-shot capabilities
• Suitable for interactive applications

Fine-tuning Process

1. Data Preparation

Data Collection

• Collect target task data
• Ensure data quality
• Label data (if needed)

Data Preprocessing

• Text cleaning
• Tokenization
• Format conversion
• Data augmentation (optional)

Data Splitting

• Training, validation, and test sets
• Stratified sampling (for class imbalance)
• Maintain consistent data distribution

2. Model Selection

Select Pre-trained Model

• BERT series: Understanding tasks
• GPT series: Generation tasks
• T5: Text-to-text tasks
• RoBERTa: Optimized BERT
• Domain-specific models: BioBERT, SciBERT

Considerations

• Task type
• Data scale
• Computational resources
• Performance requirements

3. Fine-tuning Configuration

Learning Rate

• Usually 10-100x smaller than pre-training learning rate
• Common range: 1e-5 to 5e-5
• Use learning rate scheduler

Batch Size

• Adjust based on memory
• Common range: 8-32
• Gradient accumulation (when memory is insufficient)

Training Epochs

• Usually 3-10 epochs
• Early stopping to prevent overfitting
• Monitor validation performance

Optimizer

• AdamW: Common choice
• Adam: Classic optimizer
• SGD: May generalize better

Regularization

• Dropout: 0.1-0.3
• Weight decay: 0.01
• Label smoothing: 0.1

4. Training Process

Training Steps

1. Load pre-trained model
2. Prepare data loader
3. Set up optimizer and scheduler
4. Training loop
5. Validation and early stopping
6. Save best model

Monitor Metrics

• Training loss
• Validation loss
• Task-specific metrics (accuracy, F1, etc.)
• Gradient norm

5. Evaluation and Optimization

Evaluation Methods

• Evaluate on test set
• Cross-validation
• Ablation studies

Optimization Directions

• Hyperparameter tuning
• Data augmentation
• Model ensemble
• Post-processing

Practical Tips

1. Learning Rate Strategies

Learning Rate Warm-up

• Use smaller learning rate for first few epochs
• Gradually increase to target learning rate
• Prevent model instability

Cosine Annealing

• Learning rate decays by cosine function
• Help model escape local optima
• Improve final performance

Linear Decay

• Learning rate decreases linearly
• Simple and effective
• Suitable for most cases

2. Batch Size Adjustment

When Memory is Insufficient

• Reduce batch size
• Use gradient accumulation
• Mixed precision training

Large Batch Training

• May need to adjust learning rate
• Linear scaling rule
• May affect generalization

3. Data Augmentation

Text Augmentation Methods

• Synonym replacement
• Random deletion
• Random swap
• Back-translation

Augmentation Strategies

• Use only during training
• Maintain semantic consistency
• Avoid over-augmentation

4. Multi-task Learning

Method

• Fine-tune multiple related tasks simultaneously
• Share bottom-level parameters
• Task-specific top layers

Advantages

• Improve generalization
• Reduce overfitting
• Leverage task relationships

Common Issues and Solutions

1. Overfitting

Symptoms

• Training loss continues to decrease
• Validation loss starts to increase
• Poor test performance

Solutions

• Increase data size
• Use data augmentation
• Increase regularization
• Early stopping
• Reduce model size

2. Underfitting

Symptoms

• Both training and validation losses are high
• Poor model performance

Solutions

• Increase training epochs
• Increase learning rate
• Reduce regularization
• Increase model capacity

3. Unstable Training

Symptoms

• Loss oscillates
• Gradient explosion/vanishing

Solutions

• Gradient clipping
• Lower learning rate
• Use learning rate warm-up
• Check data quality

4. Insufficient Memory

Solutions

• Reduce batch size
• Use gradient accumulation
• Mixed precision training
• Use PEFT methods
• Use smaller model

Tools and Frameworks

1. Hugging Face Transformers

Features

• Rich pre-trained models
• Simple API
• Supports PEFT methods
• Active community

Example Code

python
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments

model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    learning_rate=2e-5,
    evaluation_strategy="epoch",
)

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

trainer.train()

2. PEFT Library

Supported PEFT Methods

• LoRA
• Prefix Tuning
• Prompt Tuning
• Adapter

Example Code

python
from peft import get_peft_model, LoraConfig, TaskType

peft_config = LoraConfig(
    task_type=TaskType.SEQ_CLS,
    inference_mode=False,
    r=8,
    lora_alpha=32,
    lora_dropout=0.1
)

model = get_peft_model(model, peft_config)
model.print_trainable_parameters()

3. Other Frameworks

• PyTorch Lightning: Simplify training workflow
• Fairseq: Sequence-to-sequence tasks
• spaCy: Industrial-grade NLP

Best Practices

1. Start Small

• Validate workflow with small dataset first
• Gradually increase data scale
• Fast iteration

2. Fully Utilize Pre-training

• Choose appropriate pre-trained model
• Understand pre-training task
• Consider domain adaptation

3. Systematic Tuning

• Controlled experiments
• Record all configurations
• Use experiment tracking tools

4. Evaluate and Iterate

• Multi-dimensional evaluation
• Error analysis
• Continuous improvement

Case Studies

Case 1: Text Classification

• Task: Sentiment analysis
• Model: BERT-base
• Data: 10k samples
• Method: Full parameter fine-tuning
• Result: F1 improved from 0.75 to 0.92

Case 2: Named Entity Recognition

• Task: Medical NER
• Model: BioBERT
• Data: 5k samples
• Method: LoRA fine-tuning
• Result: 95% parameter reduction, comparable performance

Case 3: Dialogue Generation

• Task: Customer service dialogue
• Model: GPT-2
• Data: 100k dialogues
• Method: Instruction fine-tuning
• Result: Improved dialogue quality and relevance