A Dutch language model based on RoBERTa with some tasks specific to Dutch.

Read more on our blog post or on the paper.

RobBERT can easily be used in two different ways, namely either using Fairseq RoBERTa code or using HuggingFace Transformers

You can download your model for 🤗 Transformers directly. You can use the following code to download the base model and finetune it yourself. We'll explain how to do that in the next section!

from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("pdelobelle/Robbert-base")
model = AutoModelForSequenceClassification.from_pretrained("pdelobelle/robbert-base")

Or you can also download a model that we finetuned. Check our project site for a list of all models, the base model is available as pdelobelle/robbert-base.

You can also use RobBERT using the RoBERTa architecture code. We use the same encoder and dictionary as Fairseq, which you can download like this (use curl on Mac OS):

mkdir data
cd data
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json'
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe'
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt'

Then download our Fairseq model from here (RobBERT-base, 1.5 GB).

You can then use all functions of RoBERTa, but using RobBERT's model.pt.

We also provide the following fine-tuned heads:

• Prediction of die or dat in sentences as a classification problem (trained on EuroParl sentences)
• Sentiment analysis (trained on DBRD)

You can replicate the experiments done in our paper by following the following steps. You can install the required dependencies either the requirements.txt or pipenv:

• Installing the dependencies from the requirements.txt file using pip install -r requirements.txt
• OR install using Pipenv (install by running pip install pipenv in your terminal) by running pipenv install.

In this section we describe how to use the scripts we provide to fine-tune models, which should be general enough to reuse for other desired textual classification tasks.

• Download the Dutch book review dataset from https://github.com/benjaminvdb/110kDBRD, and save it to data/raw/110kDBRD
• Run src/preprocess_dbrd.py to prepare the dataset.
• Follow the notebook notebooks/finetune_dbrd.ipynb to finetune the model.

We fine-tune our model on the Dutch Europarl corpus. You can download it first with:

cd data\raw\europarl\
wget -N 'http://www.statmt.org/europarl/v7/nl-en.tgz'
tar zxvf nl-en.tgz

As a sanity check, now you should have the following files in your data/raw/europarl folder:

europarl-v7.nl-en.en
europarl-v7.nl-en.nl
nl-en.tgz

Then you can run the preprocessing with the following script, which fill first process the Europarl corpus to remove sentences without any die or dat. Afterwards, it will flip the pronoun and join both sentences together with a <sep> token.

python src/preprocess_diedat.py data/europarl-v7.nl-en.nl
./preprocess_diedat.sh

note: You can monitor the progress of the first preprocessing step with watch -n 2 wc -l data/europarl-v7.nl-en.nl.sentences. This will take a while, but it's certainly not needed to use all inputs. This is after all why you want to use a pre-trained language model. You can terminate the python script at any time and the second step will only use those._

This project is created by Pieter Delobelle, Thomas Winters and Bettina Berendt.

We are grateful to Liesbeth Allein, for her work on die-dat disambiguation, Huggingface for their transformer package, Facebook for their Fairseq package and all other people whose work we could use.

We release our models and this code under MIT.

Even though MIT doesn't require it, we would like to ask if you could nevertheless cite our paper if it helped you!

@misc{delobelle2020robbert,
    title={RobBERT: a Dutch RoBERTa-based Language Model},
    author={Pieter Delobelle and Thomas Winters and Bettina Berendt},
    year={2020},
    eprint={2001.06286},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}