# Natural Language Processing Models

This directory contains implementations and examples for various NLP models and tools.

## BERT Implementations

### Custom Fine-tuning
- Task-specific adaptation
- Domain adaptation
- Multi-task learning
- Transfer learning

### Implementation Examples
```python
# Example: BERT Fine-tuning
from transformers import BertForSequenceClassification, BertTokenizer
from transformers import Trainer, TrainingArguments

model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Training arguments
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=64,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir='./logs',
)
```

## Transformer Models

### Architecture Details
- Attention mechanisms
- Position encoding
- Multi-head attention
- Feed-forward networks

### Custom Implementations
- Model architecture
- Training pipeline
- Inference optimization
- Model compression

## Text Classification

### Pre-trained Models
- Sentiment analysis
- Topic classification
- Intent recognition
- Entity recognition

### Features
- Multi-label classification
- Hierarchical classification
- Zero-shot classification
- Few-shot learning

## Best Practices

1. Data preprocessing
2. Model selection
3. Hyperparameter tuning
4. Evaluation metrics
5. Error analysis
6. Model deployment
7. Performance monitoring

## Performance Optimization

- Model quantization
- Batch processing
- Hardware acceleration
- Memory optimization
- Inference speed
- Resource utilization

## Contributing

Please follow these guidelines:
1. Include model architecture
2. Document training process
3. Add evaluation metrics
4. Include usage examples
5. Document dependencies

## Dependencies

- transformers
- torch
- numpy
- scikit-learn
- pandas
- tensorboard
- wandb