
# bert-base-en-no-cased

We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.

Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.


For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).

## How to use

```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-no-cased")
model = AutoModel.from_pretrained("Geotrend/bert-base-en-no-cased")

```

To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).

### How to cite

```bibtex
@inproceedings{smallermbert,
  title={Load What You Need: Smaller Versions of Mutlilingual 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.
{"language": "multilingual", "license": "apache-2.0", "datasets": "wikipedia"}
transformers pytorch tf jax bert fill-mask multilingual dataset:wikipedia license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# bert-base-en-no-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.


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

## How to use



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

### How to cite



## Contact 

Please contact amine@URL for any question, feedback or request.
# bert-base-en-no-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.


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



To generate other smaller versions of multilingual transformers please visit our Github repo. ### How to cite ## Contact 

Please contact amine@URL for any question, feedback or request.
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-en-no-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.


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



To generate other smaller versions of multilingual transformers please visit our Github repo. ### How to cite ## Contact 

Please contact amine@URL for any question, feedback or request.
passage: TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-en-no-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.


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



To generate other smaller versions of multilingual transformers please visit our Github repo.### How to cite## Contact 

Please contact amine@URL for any question, feedback or request.