Model 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. **Disclaimer**: The team releasing XLM-V did not write a model card for this model so this model card has been written by the Hugging Face team. This repository documents all necessary integeration steps. > 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).
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Public benchmarks & run configuration
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Sources & traceability
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。