hugging-face-evaluation-manager

To add evaluation results to a Hugging Face model card, you can parse benchmark tables from READMEs and import scores from Artificial Analysis to generate and merge YAML model-index metadata directly into the card.

Yes, you can import Artificial Analysis scores into Hugging Face model-index metadata by extracting the benchmark data and merging it with existing model card evaluations using an automated workflow.

You can automate updating Hugging Face model cards by configuring your HF_TOKEN, parsing evaluation tables, and coordinating with scripts to push model-index metadata changes to the HF hub via direct commits or pull requests.

Yes, the Hugging Face evaluation manager supports batch processing for multiple models, allowing you to parse multiple READMEs, apply consistent formatting, and push updates across several repositories simultaneously.

The YAML model-index structure in Hugging Face model cards is used to standardize evaluation metrics and benchmark results, enabling automated parsing, validation against Papers with Code standards, and consistent metadata formatting.

Yes, you need to configure an AA_API_KEY to import benchmarks from Artificial Analysis, along with an HF_TOKEN, to authenticate and push the merged evaluation results to your Hugging Face model repositories.