huggingface-classifier
Hugging Face transformer model fine-tuning and inference for intent classification
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技能内容
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
- BERT-based: bert-base-uncased, distilbert
- RoBERTa-based: roberta-base, xlm-roberta
- DeBERTa: deberta-v3-base
- 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
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原文件路径
library/specializations/ai-agents-conversational/skills/huggingface-classifier/SKILL.md