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Created model card for XLM model #38595

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Merged
merged 3 commits into from
Jun 9, 2025
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AshAnand34
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What does this PR do?

This pull request updates the XLM model documentation to improve clarity, add usage examples, and enhance the overall presentation. The changes include a new layout for badges, detailed usage examples with code snippets, and reorganized content for better readability as outlined in #36979.

Documentation Enhancements:

  • Added a floating badge section for PyTorch and TensorFlow at the top of the page to visually highlight supported frameworks.
  • Included detailed usage examples for masked language modeling using both the Pipeline and AutoModel classes, complete with Python code snippets.

Content Reorganization:

  • Rewrote and reorganized the "Overview" and "Model Details" sections to improve clarity and readability. Key features and training objectives are now presented in a concise bullet-point format.
  • Moved usage tips to a dedicated "Usage Tips" section, emphasizing the importance of selecting the correct checkpoint and using the lang parameter for multilingual tasks.

Additional Features:

  • Added a "TIP" section to guide users to explore more examples of XLM applications via the sidebar.

Before submitting

  • This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).

Who can review?

@stevhliu

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@stevhliu stevhliu left a comment

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Thanks! See my comments on your other PRs and apply them to this one as well :)

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Thanks for your contribution!

@HuggingFaceDocBuilderDev

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@stevhliu stevhliu merged commit 29ca043 into huggingface:main Jun 9, 2025
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bvantuan pushed a commit to bvantuan/transformers that referenced this pull request Jun 12, 2025
* Created model card for XLM model

* Revised model card structure and content of XLM model

* Update XLM model documentation with improved examples and code snippets for predicting <mask> tokens using Pipeline and AutoModel.
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3 participants