98 lines
No EOL
1.9 KiB
Markdown
98 lines
No EOL
1.9 KiB
Markdown
# Natural Language Processing Models
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This directory contains implementations and examples for various NLP models and tools.
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## BERT Implementations
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### Custom Fine-tuning
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- Task-specific adaptation
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- Domain adaptation
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- Multi-task learning
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- Transfer learning
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### Implementation Examples
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```python
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# Example: BERT Fine-tuning
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from transformers import BertForSequenceClassification, BertTokenizer
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from transformers import Trainer, TrainingArguments
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model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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# Training arguments
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training_args = TrainingArguments(
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output_dir='./results',
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num_train_epochs=3,
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per_device_train_batch_size=16,
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per_device_eval_batch_size=64,
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warmup_steps=500,
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weight_decay=0.01,
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logging_dir='./logs',
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)
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```
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## Transformer Models
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### Architecture Details
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- Attention mechanisms
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- Position encoding
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- Multi-head attention
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- Feed-forward networks
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### Custom Implementations
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- Model architecture
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- Training pipeline
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- Inference optimization
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- Model compression
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## Text Classification
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### Pre-trained Models
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- Sentiment analysis
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- Topic classification
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- Intent recognition
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- Entity recognition
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### Features
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- Multi-label classification
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- Hierarchical classification
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- Zero-shot classification
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- Few-shot learning
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## Best Practices
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1. Data preprocessing
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2. Model selection
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3. Hyperparameter tuning
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4. Evaluation metrics
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5. Error analysis
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6. Model deployment
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7. Performance monitoring
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## Performance Optimization
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- Model quantization
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- Batch processing
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- Hardware acceleration
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- Memory optimization
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- Inference speed
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- Resource utilization
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## Contributing
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Please follow these guidelines:
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1. Include model architecture
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2. Document training process
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3. Add evaluation metrics
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4. Include usage examples
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5. Document dependencies
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## Dependencies
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- transformers
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- torch
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- numpy
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- scikit-learn
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- pandas
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- tensorboard
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- wandb |