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tags: |
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- transformers |
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- xlm-roberta |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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language: |
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- multilingual |
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- af |
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- am |
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- ar |
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- as |
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- az |
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- be |
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- bn |
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- ca |
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- cy |
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- da |
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- de |
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- el |
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- en |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fr |
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- gd |
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- gl |
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- he |
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- id |
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- it |
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- ja |
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- lv |
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- mg |
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- ml |
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- mr |
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- ms |
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- my |
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- ne |
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- nl |
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- 'no' |
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- or |
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- pa |
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- pt |
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- ro |
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- ru |
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- sd |
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- so |
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- uz |
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- vi |
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- xh |
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- yi |
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- zh |
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--- |
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Core implementation of Jina XLM-RoBERTa |
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This implementation is adapted from [XLM-Roberta](https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta). In contrast to the original implementation, this model uses Rotary positional encodings and supports flash-attention 2. |
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### Models that use this implementation |
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to be added soon |
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### Converting weights |
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Weights from an [original XLMRoberta model](https://huggingface.co/FacebookAI/xlm-roberta-large) can be converted using the `convert_roberta_weights_to_flash.py` script in the model repository. |
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