yourbench 0.2.0

Dynamic Evaluation Set Generation with Large Language Models

YourBench: A Dynamic Benchmark Generation Framework

[GitHub] · [Dataset] · [Documentation]

Watch our 3-minute demo of the YourBench pipeline

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YourBench is an open-source framework for generating domain-specific benchmarks in a zero-shot manner. It aims to keep your large language models on their toes—even as new data sources, domains, and knowledge demands evolve.

Highlights:

• Dynamic Benchmark Generation: Produce diverse, up-to-date questions from real-world source documents (PDF, Word, HTML, even multimedia).
• Scalable & Structured: Seamlessly handles ingestion, summarization, and multi-hop chunking for large or specialized datasets.
• Zero-Shot Focus: Emulates real-world usage scenarios by creating fresh tasks that guard against memorized knowledge.
• Extensible: Out-of-the-box pipeline stages (ingestion, summarization, question generation), plus an easy plugin mechanism to accommodate custom models or domain constraints.

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Quick Start (Alpha)

# 1. Clone the repo
git clone https://github.com/huggingface/yourbench.git
cd yourbench

# Use uv to install the dependencies
# pip install uv # if you do not have uv already
uv venv
source .venv/bin/activate
uv sync
uv pip install -e .

# 3. Get a key from https://openrouter.ai/ and add it to the .env file (or make your own config with a different model!)
touch .env
echo "HF_TOKEN=<your_huggingface_token>" >> .env

# 4. Run the pipeline with an example config
yourbench run --config configs/example.yaml

Note: The above instructions are a work-in-progress, and more comprehensive usage info will be provided soon.

Process Flow

Key Features

• Automated Benchmark Generation
  Generate question-answer pairs that test LLMs on specific domains or knowledge slices, derived directly from your raw documents.

• Flexible Pipeline
  Each stage (ingestion, summarization, chunking, multi-/single-hop QG, deduplication) can be enabled or disabled via YAML config. Fine-grained control allows minimal or comprehensive runs.

• Robust Config System
  A single YAML config controls model roles, data paths, chunking parameters, question generation instructions, deduplication thresholds, etc.

• Multi-Model Ensemble Support
  Use different LLMs for ingestion, summarization, question generation, or answering. This fosters broader coverage and question style diversity.

• Deduplication & Quality Filtering
  Automatic grouping of near-duplicate questions to prune and keep a curated set.

• Extensive Logging & Analysis
  Built-in modules measure dataset coverage, question distribution, difficulty metrics, and more.

• Public or Private
  Optionally push ingested or generated data to the Hugging Face Hub or keep it local.

• Extensible
  Each pipeline step is modular. Easily add custom question-generation prompts, chunking logic, or domain-specific expansions.

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Core Concepts & Workflow

YourBench follows a multi-stage approach:

1. Document Ingestion
   Convert PDFs, HTML, Word, or text into a standardized Markdown format.

2. Summarization
   Generate a concise "global summary" for each document, using a designated summarization LLM.

3. Chunking
   Split or chunk documents (and optionally combine multiple smaller segments) based on text similarity or length constraints.

4. Question Generation

   • Single-Shot: Create straightforward, single-chunk questions.
   • Multi-Hop: Combine multiple chunks to produce more complex, integrative questions.

5. Deduplication
   Remove or group near-duplicate questions across your dataset using embedding-based similarity.

6. Analysis
   Evaluate question distribution, difficulty, coverage, or run custom analyses.

7. Export
   The resulting question sets can be stored locally or uploaded as a new dataset on the Hugging Face Hub.

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🧰 Development

We use:

• Ruff for code formatting and linting
• pytest for testing

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Install development dependencies
4. Make your changes
5. Run tests and ensure code style compliance
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• LiteLLM for model inference
• Sentence Transformers for semantic embeddings
• Hugging Face for dataset infrastructure