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bert-base-multilingual-cased

BERT-base-multilingual-cased is Google's multilingual BERT trained on 104-language Wikipedia data with case preserved, making it better suited than the uncased variant for named entity recognition and tasks where capitalization carries semantic meaning. It shares the same 12-layer Transformer architecture and 768-dimensional embedding space as BERT-base-uncased. Despite its age, it remains a common transfer learning starting point for multilingual tasks.

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Fields below are copied from the tags and counters on the HuggingFace repository google-bert/bert-base-multilingual-cased at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
google-bert
Pipeline tag
fill-mask
Library
Transformers
Framework tags
PyTorch, TensorFlow, JAX
Weight formats
safetensors
License tag
apache-2.0 — read the license file in the repo before relying on it
Language tags
multilingual; Afrikaans (af), Albanian (sq), Arabic (ar), Aragonese (an), Armenian (hy), Asturian (ast), Azerbaijani (az), Bashkir (ba), Basque (eu), Bavarian (bar), Belarusian (be), Bangla (bn), Bosnian (bs), Breton (br), Bulgarian (bg), Burmese (my), Catalan (ca), Cebuano (ceb), Chechen (ce), Chinese (zh), Chuvash (cv), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), Galician (gl), Georgian (ka), German (de), Greek (el), Gujarati (gu), Haitian Creole (ht), Hebrew (he), Hindi (hi), Hungarian (hu), Icelandic (is), Ido (io), Indonesian (id), Irish (ga), Italian (it), Japanese (ja), Javanese (jv), Kannada (kn), Kazakh (kk), Kyrgyz (ky), Korean (ko), Latin (la), Latvian (lv), Lithuanian (lt), Low German (nds), Macedonian (mk), Malagasy (mg), Malay (ms), Malayalam (ml), Marathi (mr), Mongolian (mn), Minangkabau (min), Nepali (ne), Newari (new), Norwegian Bokmål (nb), Norwegian Nynorsk (nn), Occitan (oc), Persian (fa), Piedmontese (pms), Polish (pl), Portuguese (pt), Punjabi (pa), Romanian (ro), Russian (ru), Scots (sco), Serbian (sr), Sicilian (scn), Slovak (sk), Slovenian (sl), Azerbaijani (aze), Spanish (es), Sundanese (su), Swahili (sw), Swedish (sv), Filipino (tl), Tajik (tg), Thai (th), Tamil (ta), Tatar (tt), Telugu (te), Turkish (tr), Ukrainian (uk), Uzbek (uz), Vietnamese (vi), Volapük (vo), Waray (war), Welsh (cy), Western Frisian (fry), Western Panjabi (pnb), Yoruba (yo)
Papers cited
arXiv:1810.04805
Datasets declared
wikipedia
Downloads (HF counter at last fetch)
1,822,299
Likes (HF counter at last fetch)
603
Model card
https://huggingface.co/google-bert/bert-base-multilingual-cased

Use cases

  • Multilingual named entity recognition where proper noun casing matters
  • Cross-lingual sequence labeling and part-of-speech tagging
  • Zero-shot classification across the 104 supported languages
  • Baseline transfer learning evaluation for low-resource language research

Pros

  • Preserves case information critical for NER performance across languages
  • Single model spans 104 languages with a shared vocabulary
  • Broadly supported across HuggingFace pipelines and downstream NLP libraries

Cons

  • Outperformed on nearly all tasks by XLM-RoBERTa-base and larger variants
  • Fixed 512-token limit is problematic for longer multilingual documents
  • Shared multilingual vocabulary dilutes effective token budget per language

Tags

transformerspytorchtfjaxsafetensorsbertfill-maskmultilingualafsqaranhyastazbaeubarbebn