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huggingface-classifier

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Hugging Face transformer model fine-tuning and inference for intent classification

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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