metadata

language:
  - multilingual
  - en
  - fr
  - es
  - de
  - zh
  - ar
  - ru
  - vi
  - el
  - bg
  - th
  - tr
  - hi
  - ur
  - sw
datasets: wikipedia
license: apache-2.0
widget:
  - text: Google generated 46 billion [MASK] in revenue.
  - text: Paris is the capital of [MASK].
  - text: Algiers is the largest city in [MASK].
  - text: Paris est la [MASK] de la France.
  - text: Paris est la capitale de la [MASK].
  - text: L'élection américaine a eu [MASK] en novembre 2020.
  - text: تقع سويسرا في [MASK] أوروبا
  - text: إسمي محمد وأسكن في [MASK].

bert-base-15lang-cased

We are sharing smaller versions of bert-base-multilingual-cased that handle a custom number of languages.

Unlike distilbert-base-multilingual-cased, our versions give exactly the same representations produced by the original model which preserves the original accuracy.

The measurements below have been computed on a Google Cloud n1-standard-1 machine (1 vCPU, 3.75 GB):

Model	Num parameters	Size	Memory	Loading time
bert-base-multilingual-cased	178 million	714 MB	1400 MB	4.2 sec
Geotrend/bert-base-15lang-cased	141 million	564 MB	1098 MB	3.1 sec

Handled languages: en, fr, es, de, zh, ar, ru, vi, el, bg, th, tr, hi, ur and sw.

For more information please visit our paper: Load What You Need: Smaller Versions of Multilingual BERT.

How to use

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-15lang-cased")
model = AutoModel.from_pretrained("Geotrend/bert-base-15lang-cased")

To generate other smaller versions of multilingual transformers please visit our Github repo.

How to cite

@inproceedings{smallermbert,
  title={Load What You Need: Smaller Versions of Multilingual BERT},
  author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
  booktitle={SustaiNLP / EMNLP},
  year={2020}
}

Contact

Please contact [email protected] for any question, feedback or request.