xlm-v-base

模型介绍

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).

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

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。