RobBERT is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging task. As such, it has been successfully used by many researchers and practitioners for achieving state-of-the-art performance for a wide range of Dutch natural language processing tasks, including:

and also achieved outstanding, near-sota results for:

\* Note that several evaluations use RobBERT-v1, and that the second and improved RobBERT-v2 outperforms this first model on everything we tested

More in-depth information about RobBERT can be found in our blog post, our paper and the RobBERT Github repository

RobBERT uses the RoBERTa architecture and pre-training but with a Dutch tokenizer and training data. RoBERTa is the robustly optimized English BERT model, making it even more powerful than the original BERT model. Given this same architecture, RobBERT can easily be finetuned and inferenced using code to finetune RoBERTa models and most code used for BERT models, e.g. as provided by HuggingFace Transformers library.

By default, RobBERT has the masked language model head used in training. This can be used as a zero-shot way to fill masks in sentences. It can be tested out for free on RobBERT's Hosted infererence API of Huggingface. You can also create a new prediction head for your own task by using any of HuggingFace's RoBERTa-runners, their fine-tuning notebooks by changing the model name to pdelobelle/robbert-v2-dutch-base, or use the original fairseq RoBERTa training regimes.

Use the following code to download the base model and finetune it yourself, or use one of our finetuned models (documented on our project site).

Starting with transformers v2.4.0 (or installing from source), you can use AutoTokenizer and AutoModel. You can then use most of HuggingFace's BERT-based notebooks for finetuning RobBERT on your type of Dutch language dataset.

All experiments are described in more detail in our paper, with the code in our GitHub repository.

Predicting whether a review is positive or negative using the Dutch Book Reviews Dataset.

We measured how well the models are able to do coreference resolution by predicting whether "die" or "dat" should be filled into a sentence. For this, we used the EuroParl corpus.

We also measured the performance using only 10K training examples. This experiment clearly illustrates that RobBERT outperforms other models when there is little data available.

Since BERT models are pre-trained using the word masking task, we can use this to predict whether "die" or "dat" is more likely. This experiment shows that RobBERT has internalised more information about Dutch than other models.

Using the Lassy UD dataset.

Interestingly, we found that when dealing with small data sets, RobBERT v2 significantly outperforms other models.

Using the CoNLL 2002 evaluation script.

We pre-trained RobBERT using the RoBERTa training regime. We pre-trained our model on the Dutch section of the OSCAR corpus, a large multilingual corpus which was obtained by language classification in the Common Crawl corpus. This Dutch corpus is 39GB large, with 6.6 billion words spread over 126 million lines of text, where each line could contain multiple sentences, thus using more data than concurrently developed Dutch BERT models.

RobBERT shares its architecture with RoBERTa's base model, which itself is a replication and improvement over BERT. Like BERT, it's architecture consists of 12 self-attention layers with 12 heads with 117M trainable parameters. One difference with the original BERT model is due to the different pre-training task specified by RoBERTa, using only the MLM task and not the NSP task. During pre-training, it thus only predicts which words are masked in certain positions of given sentences. The training process uses the Adam optimizer with polynomial decay of the learning rate l_r=10^-6 and a ramp-up period of 1000 iterations, with hyperparameters beta_1=0.9 and RoBERTa's default beta_2=0.98. Additionally, a weight decay of 0.1 and a small dropout of 0.1 helps prevent the model from overfitting.

RobBERT was trained on a computing cluster with 4 Nvidia P100 GPUs per node, where the number of nodes was dynamically adjusted while keeping a fixed batch size of 8192 sentences. At most 20 nodes were used (i.e. 80 GPUs), and the median was 5 nodes. By using gradient accumulation, the batch size could be set independently of the number of GPUs available, in order to maximally utilize the cluster. Using the Fairseq library, the model trained for two epochs, which equals over 16k batches in total, which took about three days on the computing cluster. In between training jobs on the computing cluster, 2 Nvidia 1080 Ti's also covered some parameter updates for RobBERT v2.

In the RobBERT paper, we also investigated potential sources of bias in RobBERT.

We found that the zeroshot model estimates the probability of hij (he) to be higher than zij (she) for most occupations in bleached template sentences, regardless of their actual job gender ratio in reality.

By augmenting the DBRB Dutch Book sentiment analysis dataset with the stated gender of the author of the review, we found that highly positive reviews written by women were generally more accurately detected by RobBERT as being positive than those written by men.

Replicating our paper experiments is described in detail on teh RobBERT repository README.

Most BERT-like models have the word BERT in their name (e.g. RoBERTa, ALBERT, CamemBERT, and many, many others). As such, we queried our newly trained model using its masked language model to name itself \<mask\>bert using all kinds of prompts, and it consistently called itself RobBERT. We thought it was really quite fitting, given that RobBERT is a very Dutch name (and thus clearly a Dutch language model), and additionally has a high similarity to its root architecture, namely RoBERTa.

Since "rob" is a Dutch words to denote a seal, we decided to draw a seal and dress it up like Bert from Sesame Street for the RobBERT logo.

This project is created by Pieter Delobelle, Thomas Winters and Bettina Berendt. If you would like to cite our paper or model, you can use the following BibTeX:

@inproceedings{delobelle2020robbert,
    title = "{R}ob{BERT}: a {D}utch {R}o{BERT}a-based {L}anguage {M}odel",
    author = "Delobelle, Pieter  and
      Winters, Thomas  and
      Berendt, Bettina",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.292",
    doi = "10.18653/v1/2020.findings-emnlp.292",
    pages = "3255--3265"
}