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This model was released on 2023-01-25 and added to Hugging Face Transformers on 2023-06-20.

XLM-V

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Overview

XLM-V is multilingual language model with a one million token vocabulary trained on 2.5TB of data from Common Crawl (same as XLM-R). It was introduced in the XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models paper by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer and Madian Khabsa.

From the abstract of the XLM-V paper:

Large multilingual language models typically rely on a single vocabulary shared across 100+ languages. As these models have increased in parameter count and depth, vocabulary size has remained largely unchanged. This vocabulary bottleneck limits the representational capabilities of multilingual models like XLM-R. In this paper, we introduce a new approach for scaling to very large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity to achieve sufficient coverage for each individual language. Tokenizations using our vocabulary are typically more semantically meaningful and shorter compared to XLM-R. Leveraging this improved vocabulary, we train XLM-V, a multilingual language model with a one million token vocabulary. XLM-V outperforms XLM-R on every task we tested on ranging from natural language inference (XNLI), question answering (MLQA, XQuAD, TyDiQA), and named entity recognition (WikiAnn) to low-resource tasks (Americas NLI, MasakhaNER).

This model was contributed by stefan-it, including detailed experiments with XLM-V on downstream tasks. The experiments repository can be found here.

Usage tips

• XLM-V is compatible with the XLM-RoBERTa model architecture, only model weights from fairseq library had to be converted.
• The XLMTokenizer implementation is used to load the vocab and performs tokenization.

A XLM-V (base size) model is available under the facebook/xlm-v-base identifier.

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XLM-V architecture is the same as XLM-RoBERTa, refer to XLM-RoBERTa documentation for API reference, and examples. </Tip>