HuggingFace Classifier Skill

Capabilities

• Fine-tune transformer models for classification
• Configure training pipelines with Trainer API
• Implement inference with optimizations
• Design label schemas and mappings
• Set up model evaluation and metrics
• Deploy models with HF Inference API

Target Processes

• intent-classification-system
• entity-extraction-slot-filling

Implementation Details

Model Types

1. BERT-based: bert-base-uncased, distilbert
2. RoBERTa-based: roberta-base, xlm-roberta
3. DeBERTa: deberta-v3-base
4. Domain-specific: FinBERT, BioBERT

Training Configuration

• Dataset preparation
• Tokenization settings
• Training arguments
• Evaluation metrics
• Early stopping

Configuration Options

• Model selection
• Number of labels
• Training hyperparameters
• Batch sizes
• Learning rate schedules

Best Practices

• Use appropriate base model
• Proper train/val/test splits
• Monitor for overfitting
• Evaluate on representative data

Dependencies

• transformers
• datasets
• accelerate