huggingface-classifier
Hugging Face transformer model fine-tuning and inference for intent classification
npx skills add a5c-ai/babysitter --skill huggingface-classifier --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Capabilities
- Target Processes
- Implementation Details
- Model Types
- Training Configuration
- Configuration Options
- Best Practices
- Dependencies
What does the huggingface-classifier skill do?
Hugging Face transformer model fine-tuning and inference for intent classification
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill huggingface-classifier --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.