> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/natural-language-processing-nlp/multi-language.md). # Multi Language A model trained on one language does not help users of another, unless every language shares one embedding space. The page starts with LASER's multilingual sentence embeddings, then cross-lingual language-model pretraining with XLM and XLM-R, and ends with Google's universal embedding space. The same notes are in [LANGUAGE EMBEDDINGS](/natural-language-processing-nlp/language-embeddings.md#language-embeddings) and [LANGUAGE TRANSLATION](/natural-language-processing-nlp/language-detection-identification-generation-nld-nli-nlg.md#language-translation). The sentence-level route comes first. [Fb’s laser](https://engineering.fb.com/ai-research/laser-multilingual-sentence-embeddings/) is the LASER toolkit, which performs zero-shot cross-lingual transfer with more than 90 languages and is now open source. The pretraining route follows. [Xlm](https://github.com/facebookresearch/XLM) is the PyTorch original implementation of Cross-lingual Language Model Pretraining, and [xlm-r](https://ai.facebook.com/blog/-xlm-r-state-of-the-art-cross-lingual-understanding-through-self-supervision/) is Facebook AI open-sourcing XLM-R, a multilingual model that uses self-supervised training to reach state-of-the-art performance on four cross-lingual understanding benchmarks. Google universal embedding space is the last pointer, without its own link on this page. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/natural-language-processing-nlp/multi-language.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.