Instructions to use Geotrend/bert-base-10lang-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Geotrend/bert-base-10lang-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Geotrend/bert-base-10lang-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-10lang-cased") model = AutoModelForMaskedLM.from_pretrained("Geotrend/bert-base-10lang-cased") - Notebooks
- Google Colab
- Kaggle
bert-base-10lang-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.
This model handles the following languages: english, french, spanish, german, chinese, arabic, russian, portuguese, italian, and urdu. It produces the same representations as bert-base-multilingual-cased while being 22.5% smaller in size.
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-10lang-cased")
model = AutoModel.from_pretrained("Geotrend/bert-base-10lang-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 amine@geotrend.fr for any question, feedback or request.
- Downloads last month
- 6