jhu-clsp

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mmBERT-base

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Center for Language and Speech Processing @ JHU 249

This PR significantly improves the model card for jhu-clsp/mmBERT-base by:

1. Updating Metadata:

   • Changing the pipeline_tag from fill-mask to feature-extraction to better reflect the model's primary use cases as a multilingual encoder for classification, embedding, and retrieval tasks. This ensures the model appears in the correct search results at https://huggingface.co/models?pipeline_tag=feature-extraction.
   • Adding fill-mask as an additional tag to maintain discoverability for masked language modeling, which is a supported capability as demonstrated in the usage examples.

2. Enhancing Content:

   • Updating the main title to the full paper title: "mmBERT: A Modern Multilingual Encoder with Annealed Language Learning".
   • Adding a direct link to the Hugging Face paper page: https://huggingface.co/papers/2509.06888, alongside the existing Arxiv link.
   • Including the comprehensive paper abstract.
   • Integrating and expanding sections like "Overview", "Quick Start", "Model Family", "Usage Examples", "Fine-tuning Examples", "Training Details", "Evaluation", "FAQ", and "Limitations" using the more detailed and up-to-date information from the official GitHub repository README, making the model card a more complete resource for users.
   • Updating the transformers installation requirement to transformers>=4.48.0 for consistency with the GitHub README.
   • Renaming the "Base Model for Classification" example in the "Quick Start" to "Base Model for Masked Language Modeling" to accurately reflect its content.

These changes provide users with a clearer understanding of the model's capabilities, how to use it, and its underlying design.