XLM V (Base sized model) 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. Model description 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 covera…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy