BERTje: A Dutch BERT model

Wietse de Vries • Andreas van Cranenburgh • Arianna Bisazza • Tommaso Caselli • Gertjan van Noord • Malvina Nissim

Model description

BERTje is a Dutch pre-trained BERT model developed at the University of Groningen.

For details, check out our paper on arXiv, the model on the 🤗 Hugging Face model hub and related work on Semantic Scholar.

Publications with BERTje

• BERTje: A Dutch BERT Model
• What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models
• (40+ papers citing BERTje)

Transformers

You can play with BERTje without any training with the following snippet (or use the hosted version by Huggingface):

from transformers import pipeline

pipe = pipeline('fill-mask', model='GroNLP/bert-base-dutch-cased')
for res in pipe('Ik wou dat ik een [MASK] was.'):
    print(res['sequence'])
    
# [CLS] Ik wou dat ik een kind was. [SEP]
# [CLS] Ik wou dat ik een mens was. [SEP]
# [CLS] Ik wou dat ik een vrouw was. [SEP]
# [CLS] Ik wou dat ik een man was. [SEP]
# [CLS] Ik wou dat ik een vriend was. [SEP]

If you want to actually train your own model based on BERTje, you can load the tokenizer and model with this snippet:

from transformers import AutoTokenizer, AutoModel, TFAutoModel

tokenizer = AutoTokenizer.from_pretrained("GroNLP/bert-base-dutch-cased")
model = AutoModel.from_pretrained("GroNLP/bert-base-dutch-cased")  # PyTorch
model = TFAutoModel.from_pretrained("GroNLP/bert-base-dutch-cased")  # Tensorflow

That's all! Check out the Transformers documentation for further instructions.

WARNING: The vocabulary size of BERTje has changed in 2021. If you use an older fine-tuned model and experience problems with the GroNLP/bert-base-dutch-cased tokenizer, use use the following tokenizer:

tokenizer = AutoTokenizer.from_pretrained("GroNLP/bert-base-dutch-cased", revision="v1")  # v1 is the old vocabulary

Benchmarks

The arXiv paper lists benchmarks. Here are a couple of comparisons between BERTje, multilingual BERT, BERT-NL and RobBERT that were done after writing the paper. Unlike some other comparisons, the fine-tuning procedures for these benchmarks are identical for each pre-trained model. You may be able to achieve higher scores for individual models by optimizing fine-tuning procedures.

More experimental results will be added to this page when they are finished. Technical details about how a fine-tuned these models will be published later as well as downloadable fine-tuned checkpoints.

All of the tested models are base sized (12) layers with cased tokenization.

Headers in the tables below link to original data sources. Scores link to the model pages that corresponds to that specific fine-tuned model. These tables will be updated when more simple fine-tuned models are made available.

Named Entity Recognition

Model	CoNLL-2002	SoNaR-1	spaCy UD LassySmall
BERTje	90.24	84.93	86.10
mBERT	88.61	84.19	86.77
BERT-NL	85.05	80.45	81.62
RobBERT	84.72	81.98	79.84

Part-of-speech tagging

Model	UDv2.5 LassySmall
BERTje	96.48
mBERT	96.20
BERT-NL	96.10
RobBERT	95.91

Download

The recommended download method is using the Transformers library. The model is available at the model hub.

You can manually download the model files here: https://huggingface.co/GroNLP/bert-base-dutch-cased/tree/main

Thanks to Hugging Face for hosting the model files!

Code

The main code that is used for pretraining data preparation, finetuning and probing are given in the appropriate directies. Do not expect the code to be fully functional, complete or documented since this is research code that has been written and collected in the course of multiple months. Nevertheless, the code can be useful for reference.

Acknowledgements

Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).

Citation

Please use the following citation if you use BERTje or our fine-tuned models:

@misc{devries2019bertje,
	title = {{BERTje}: {A} {Dutch} {BERT} {Model}},
	shorttitle = {{BERTje}},
	author = {de Vries, Wietse  and  van Cranenburgh, Andreas  and  Bisazza, Arianna  and  Caselli, Tommaso  and  Noord, Gertjan van  and  Nissim, Malvina},
	year = {2019},
	month = dec,
	howpublished = {arXiv:1912.09582},
	url = {http://arxiv.org/abs/1912.09582},
}

Use the following citation if you use anything from the probing classifiers:

@inproceedings{devries2020bertlayers,
	title = {What's so special about {BERT}'s layers? {A} closer look at the {NLP} pipeline in monolingual and multilingual models},
	author = {de Vries, Wietse  and  van Cranenburgh, Andreas  and  Nissim, Malvina},
	year = {2020},
	booktitle = {Findings of EMNLP},
	pages = {4339--4350},
	url = {https://www.aclweb.org/anthology/2020.findings-emnlp.389},
}