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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 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).


You can use this model directly with a pipeline for masked language modeling:

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='facebook/xlm-v-base')
>>> unmasker("Paris is the <mask> of France.")

[{'score': 0.9286897778511047,
  'token': 133852,
  'token_str': 'capital',
  'sequence': 'Paris is the capital of France.'},
 {'score': 0.018073994666337967,
  'token': 46562,
  'token_str': 'Capital',
  'sequence': 'Paris is the Capital of France.'},
 {'score': 0.013238662853837013,
  'token': 8696,
  'token_str': 'centre',
  'sequence': 'Paris is the centre of France.'},
 {'score': 0.010450296103954315,
  'token': 550136,
  'token_str': 'heart',
  'sequence': 'Paris is the heart of France.'},
 {'score': 0.005028395913541317,
  'token': 60041,
  'token_str': 'center',
  'sequence': 'Paris is the center of France.'}]

Bias, Risks, and Limitations

Please refer to the model card of XLM-R, because XLM-V has a similar architecture and has been trained on similar training data.

BibTeX entry and citation info

       author = {{Liang}, Davis and {Gonen}, Hila and {Mao}, Yuning and {Hou}, Rui and {Goyal}, Naman and {Ghazvininejad}, Marjan and {Zettlemoyer}, Luke and {Khabsa}, Madian},
        title = "{XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
         year = 2023,
        month = jan,
          eid = {arXiv:2301.10472},
        pages = {arXiv:2301.10472},
          doi = {10.48550/arXiv.2301.10472},
archivePrefix = {arXiv},
       eprint = {2301.10472},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230110472L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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