huggingface-classifier

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

509 stars

Best use case

huggingface-classifier is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using huggingface-classifier should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/huggingface-classifier/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/ai-agents-conversational/skills/huggingface-classifier/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/huggingface-classifier/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How huggingface-classifier Compares

Feature / Agent	huggingface-classifier	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

Related Skills

kraken2-taxonomic-classifier

509

from a5c-ai/babysitter

Kraken2 taxonomic classification skill for rapid metagenomic read assignment

acmg-variant-classifier

509

from a5c-ai/babysitter

ACMG/AMP variant classification skill for systematic pathogenicity assessment

abc-xyz-classifier

509

from a5c-ai/babysitter

Multi-dimensional inventory classification skill combining value (ABC) and demand variability (XYZ) analysis for differentiated policies

llm-classifier

509

from a5c-ai/babysitter

LLM-based zero-shot and few-shot classification for flexible intent detection

process-builder

509

from a5c-ai/babysitter

Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.

Workflow & Productivity

babysitter

509

from a5c-ai/babysitter

Orchestrate via @babysitter. Use this skill when asked to babysit a run, orchestrate a process or whenever it is called explicitly. (babysit, babysitter, orchestrate, orchestrate a run, workflow, etc.)