huggingface-classifier

name	huggingface-classifier
description	Hugging Face transformer model fine-tuning and inference for intent classification
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep"]
graph	{"domains":["domain:software-engineering"],"specializations":["specialization:ai-agents-conversational"],"skillAreas":["skill-area:natural-language-processing","skill-area:ml-fine-tuning"],"roles":["role:ml-engineer","role:backend-engineer"],"workflows":["workflow:ml-model-lifecycle","workflow:feature-development"]}

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