XLM-R Encoder

The XLM-R Encoder block provides the xlm-roberta-base model, published in Unsupervised Cross-lingual Representation Learning at Scale.

The XLM-R Encoder is a good model for processing text written naturally and in many languages, like the Multilingual BERT encoder block. However, the XLM-R Encoder was pretrained more specifically for sentence embedding, making it a better choice for text similarity tasks out of the box.

Note

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Using the XLM-R Encoder

The XLM-R Encoder block is initialized with weights pretrained on CommonCrawl data from 100 languages.

Use the XLM-R Embedding snippet to directly get a complete model for text embedding that uses the XLM-R Encoder.

Input

The input of the XLM-R Encoder must come from a XLM-R Tokenizer block.

Output

The XLM-R Encoder returns the so-called CLS output. This output is a vector that can be used as embedding for text similarity tasks, or passed to other blocks to perform regression or classification.

Languages

Here is the list of languages used in pretraining, which are supported out of the box by the XLM-R Encoder block and the XLM-R Embedding snippet.
More details, like how much of each languages was used in pretraining, can be found in Appendix 1 of Unsupervised Cross-lingual Representation Learning at Scale.

Table 1. Languages used to pretrain the XLM-R Encoder.
Afrikaans Albanian Amharic Arabic Armenian Assamese Azerbaijani Basque Belarusian Bengali Bengali Romanized Bosnian Breton Bulgarian Burmese Burmese Catalan Chinese (Simplified) Chinese (Traditional) Croatian Czech Danish Dutch English Esperanto	Estonian Filipino Finnish French Galician Georgian German Greek Gujarati Hausa Hebrew Hindi Hindi Romanized Hungarian Icelandic Indonesian Irish Italian Japanese Javanese Kannada Kazakh Khmer Korean Kurdish (Kurmanji)	Kyrgyz Lao Latin Latvian Lithuanian Macedonian Malagasy Malay Malayalam Marathi Mongolian Nepali Norwegian Oriya Oromo Pashto Persian Polish Portuguese Punjabi Romanian Russian Sanskrit Scottish Gaelic Serbian	Sindhi Sinhala Slovak Slovenian Somali Spanish Sundanese Swahili Swedish Tamil Tamil Romanized Telugu Telugu Romanized Thai Turkish Ukrainian Urdu Urdu Romanized Uyghur Uzbek Vietnamese Welsh Western Frisian Xhosa Yiddish

Available weights

The XLM-R Encoder block uses the xlm-roberta-base model with weights, pre-trained by Hugging face on 100 languages from CommonCrawl.

Terms

When using pretrained snippets, additional terms apply: XLM-R with weights licence.

Reference

Alexis Conneau, Kartikay Khandelwal, et al.: Unsupervised Cross-lingual Representation Learning at Scale, 2020.

Guillaume Wenzek, Marie-Anne Lachaux, et al.: CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data, 2019.

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