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--- |
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language: nl |
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thumbnail: "https://raw.githubusercontent.com/wietsedv/bertje/master/bertje.png" |
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tags: |
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- BERTje |
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--- |
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# BERTje: A Dutch BERT model |
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[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) • |
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[Andreas van Cranenburgh](https://www.semanticscholar.org/author/Andreas-van-Cranenburgh/2791585) • |
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[Arianna Bisazza](https://www.semanticscholar.org/author/Arianna-Bisazza/3242253) • |
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[Tommaso Caselli](https://www.semanticscholar.org/author/Tommaso-Caselli/1864635) • |
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[Gertjan van Noord](https://www.semanticscholar.org/author/Gertjan-van-Noord/143715131) • |
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[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475) |
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## Model description |
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BERTje is a Dutch pre-trained BERT model developed at the University of Groningen. |
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<img src="https://raw.githubusercontent.com/wietsedv/bertje/master/bertje.png" height="250"> |
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For details, check out our paper on [arXiv](https://arxiv.org/abs/1912.09582), the code on [Github](https://github.com/wietsedv/bertje) and related work on [Semantic Scholar](https://www.semanticscholar.org/paper/BERTje%3A-A-Dutch-BERT-Model-Vries-Cranenburgh/a4d5e425cac0bf84c86c0c9f720b6339d6288ffa). |
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The paper and Github page mention fine-tuned models that are available [here](https://huggingface.co/wietsedv). |
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## How to use |
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```python |
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from transformers import AutoTokenizer, AutoModel, TFAutoModel |
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tokenizer = AutoTokenizer.from_pretrained("GroNLP/bert-base-dutch-cased") |
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model = AutoModel.from_pretrained("GroNLP/bert-base-dutch-cased") # PyTorch |
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model = TFAutoModel.from_pretrained("GroNLP/bert-base-dutch-cased") # Tensorflow |
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``` |
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**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: |
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```python |
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tokenizer = AutoTokenizer.from_pretrained("GroNLP/bert-base-dutch-cased", revision="v1") # v1 is the old vocabulary |
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``` |
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## Benchmarks |
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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. |
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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. |
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All of the tested models are *base* sized (12) layers with cased tokenization. |
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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. |
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### Named Entity Recognition |
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| Model | [CoNLL-2002](https://www.clips.uantwerpen.be/conll2002/ner/) | [SoNaR-1](https://ivdnt.org/downloads/taalmaterialen/tstc-sonar-corpus) | spaCy UD LassySmall | |
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| ---------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | |
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| **BERTje** | [**90.24**](https://huggingface.co/wietsedv/bert-base-dutch-cased-finetuned-conll2002-ner) | [**84.93**](https://huggingface.co/wietsedv/bert-base-dutch-cased-finetuned-sonar-ner) | [86.10](https://huggingface.co/wietsedv/bert-base-dutch-cased-finetuned-udlassy-ner) | |
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| [mBERT](https://github.com/google-research/bert/blob/master/multilingual.md) | [88.61](https://huggingface.co/wietsedv/bert-base-multilingual-cased-finetuned-conll2002-ner) | [84.19](https://huggingface.co/wietsedv/bert-base-multilingual-cased-finetuned-sonar-ner) | [**86.77**](https://huggingface.co/wietsedv/bert-base-multilingual-cased-finetuned-udlassy-ner) | |
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| [BERT-NL](http://textdata.nl) | 85.05 | 80.45 | 81.62 | |
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| [RobBERT](https://github.com/iPieter/RobBERT) | 84.72 | 81.98 | 79.84 | |
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### Part-of-speech tagging |
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| Model | [UDv2.5 LassySmall](https://universaldependencies.org/treebanks/nl_lassysmall/index.html) | |
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| ---------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | |
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| **BERTje** | **96.48** | |
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| [mBERT](https://github.com/google-research/bert/blob/master/multilingual.md) | 96.20 | |
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| [BERT-NL](http://textdata.nl) | 96.10 | |
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| [RobBERT](https://github.com/iPieter/RobBERT) | 95.91 | |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{devries2019bertje, |
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\ttitle = {{BERTje}: {A} {Dutch} {BERT} {Model}}, |
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\tshorttitle = {{BERTje}}, |
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\tauthor = {de Vries, Wietse and van Cranenburgh, Andreas and Bisazza, Arianna and Caselli, Tommaso and Noord, Gertjan van and Nissim, Malvina}, |
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\tyear = {2019}, |
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\tmonth = dec, |
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\thowpublished = {arXiv:1912.09582}, |
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\turl = {http://arxiv.org/abs/1912.09582}, |
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} |
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``` |
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