Xlm Roberta Large



XLM-RoBERTa is a multilingual version of the RoBERTa model, which is pre-trained on a large corpus of text from 100 languages. It is trained using a masked language modeling (MLM) objective, where 15% of the words in a sentence are randomly masked and the model has to predict the masked words. This allows the model to learn a bidirectional representation of the sentence. The model can be used to extract features for downstream tasks such as classification or question answering. It can also be fine-tuned for specific tasks.

Use cases

XLM-RoBERTa has a wide range of use cases and practical applications for a technical audience. One possible use case is in natural language processing tasks where multilingual support is required. The model's ability to understand and represent text from 100 different languages makes it valuable for tasks such as machine translation, sentiment analysis, named entity recognition, and text classification. Another use case is in cross-lingual transfer learning, where the model can be fine-tuned on a specific task using labeled data from one language and then applied to other languages without the need for additional training. This can significantly reduce the amount of labeled data needed for each individual language. Furthermore, the model can be used for masked language modeling, allowing for the generation of text with missing words, which could be useful for text completion or language generation tasks. Overall, the versatility and multilingual capabilities of XLM-RoBERTa make it a powerful tool for a wide range of natural language processing applications.



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Creator Models

Bert Large Uncased Whole Word Masking Finetuned Squad$?294,128
Bert Large Cased Whole Word Masking$?4,430
Gpt2 Large$?263,455
Xlm Roberta Large Finetuned Conll02 Dutch$?378
Xlm Roberta Large Finetuned Conll02 Spanish$?171

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Summary of this model and related resources.

Model NameXlm Roberta Large

XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data contain...

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API SpecView on HuggingFace
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Paper LinkNo paper link provided


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