Created model card for xlm-roberta-xl #38597
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stevhliu
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Jun 5, 2025
This was referenced Jun 5, 2025
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stevhliu
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Jun 6, 2025
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Just a few more suggestions!
stevhliu
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Jun 9, 2025
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Thanks, just a few more comments to ensure we're using the correct mask token.
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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. |
bvantuan
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Jun 12, 2025
* Created model card for xlm-roberta-xl * Update XLM-RoBERTa-XL model card with improved descriptions and usage examples * Minor option labeling fix * Added MaskedLM version of XLM RoBERTa XL to model card * Added quantization example for XLM RoBERTa XL model card * minor fixes to xlm roberta xl model card * Minor fixes to mask format in xlm roberta xl model card
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So this was the third MR that unfortunately removes attribution of people, who actually contributed to add the model into the library years ago. |
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Hi sorry about that, but I'll add that back in in a follow up PR 🙂 |
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What does this PR do?
This pull request enhances the documentation for the XLM-RoBERTa-XL model, providing a more comprehensive overview of its features, usage, and implementation details as per #36979. The updates include new examples, usage tips, and key model details, making it easier for users to understand and apply the model.
Documentation Enhancements:
Added an overview of XLM-RoBERTa-XL, including its multilingual capabilities, parameter size (3.5B and 10.7B), and performance improvements over XLM-R and RoBERTa-Large. The section highlights its effectiveness for low-resource languages and benchmarks.
Included Python code examples for using XLM-RoBERTa-XL with the
pipelineandAutoModelAPIs for masked language modeling, demonstrating its application in English and French.Added a "Key Features" section detailing the model's architecture, multilingual training, and language detection capabilities, along with suggestions for handling its large size during training and inference.
Introduced a "Usage Tips" section with practical advice on model usage, such as avoiding the need for
langtensors, using model parallelism or gradient checkpointing, and optimizing inference with quantization or sharding.Added a
<Tip>section linking to the XLM-RoBERTa documentation for additional usage examples and input/output details.Before submitting
Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@stevhliu