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https://github.com/iPieter/RobBERT
A Dutch RoBERTa-based language model
https://github.com/iPieter/RobBERT
bert bert-model language-model nlp nlp-resources roberta transformers
Last synced: about 2 months ago
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A Dutch RoBERTa-based language model
- Host: GitHub
- URL: https://github.com/iPieter/RobBERT
- Owner: iPieter
- License: mit
- Created: 2019-12-19T13:26:27.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-04-08T09:00:17.000Z (6 months ago)
- Last Synced: 2024-05-17T14:32:42.401Z (4 months ago)
- Topics: bert, bert-model, language-model, nlp, nlp-resources, roberta, transformers
- Language: Jupyter Notebook
- Homepage: https://pieter.ai/robbert/
- Size: 1.58 MB
- Stars: 193
- Watchers: 11
- Forks: 28
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://huggingface.co/pdelobelle/robbert-v2-dutch-base)
# RobBERT: Dutch RoBERTa-based Language Model.
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](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=7180110604335112086) and [practitioners](https://huggingface.co/models?search=robbert) for achieving state-of-the-art performance for a wide range of Dutch natural language processing tasks, including:
- [Emotion detection](https://www.aclweb.org/anthology/2021.wassa-1.27/)
- Sentiment analysis ([book reviews](https://arxiv.org/pdf/2001.06286.pdf), [news articles](https://biblio.ugent.be/publication/8704637/file/8704638.pdf)*)
- [Coreference resolution](https://arxiv.org/pdf/2001.06286.pdf)
- Named entity recognition ([CoNLL](https://arxiv.org/pdf/2001.06286.pdf), [job titles](https://arxiv.org/pdf/2004.02814.pdf)*, [SoNaR](https://github.com/proycon/deepfrog))
- Part-of-speech tagging ([Small UD Lassy](https://arxiv.org/pdf/2001.06286.pdf), [CGN](https://github.com/proycon/deepfrog))
- [Zero-shot word prediction](https://arxiv.org/pdf/2001.06286.pdf)
- [Humor detection](https://arxiv.org/pdf/2010.13652.pdf)
- [Cyberbullying detection](https://www.cambridge.org/core/journals/natural-language-engineering/article/abs/automatic-classification-of-participant-roles-in-cyberbullying-can-we-detect-victims-bullies-and-bystanders-in-social-media-text/A2079C2C738C29428E666810B8903342)
- [Correcting dt-spelling mistakes](https://gitlab.com/spelfouten/dutch-simpletransformers/)*
and also achieved outstanding, near-sota results for:
- [Natural language inference](https://arxiv.org/pdf/2101.05716.pdf)*
- [Review classification](https://medium.com/broadhorizon-cmotions/nlp-with-r-part-5-state-of-the-art-in-nlp-transformers-bert-3449e3cd7494)*
\* *Note that several evaluations use RobBERT-v1, and that the second and improved RobBERT-v2 outperforms this first model on everything we tested*
*(Also note that this list is not exhaustive. If you used RobBERT for your application, we are happy to know about it! Send us a mail, or add it yourself to this list by sending a pull request with the edit!)*
To use the RobBERT model using [HuggingFace transformers](https://huggingface.co/transformers/), use the name [`pdelobelle/robbert-v2-dutch-base`](https://huggingface.co/pdelobelle/robbert-v2-dutch-base).
More in-depth information about RobBERT can be found in our [blog post](https://pieter.ai/robbert/) and in [our paper](https://arxiv.org/abs/2001.06286).
## Table of contents
* [How To Use](#how-to-use)
+ [Using Huggingface Transformers (easiest)](#using-huggingface-transformers-easiest)
+ [Using Fairseq (harder)](#using-fairseq-harder)
* [Technical Details From The Paper](#technical-details-from-the-paper)
+ [Our Performance Evaluation Results](#our-performance-evaluation-results)
+ [Sentiment analysis](#sentiment-analysis)
+ [Die/Dat (coreference resolution)](#diedat-coreference-resolution)
- [Finetuning on whole dataset](#finetuning-on-whole-dataset)
- [Finetuning on 10K examples](#finetuning-on-10k-examples)
- [Using zero-shot word masking task](#using-zero-shot-word-masking-task)
+ [Part-of-Speech Tagging.](#part-of-speech-tagging)
+ [Named Entity Recognition](#named-entity-recognition)
* [Pre-Training Procedure Details](#pre-training-procedure-details)
* [Investigating Limitations and Bias](#investigating-limitations-and-bias)
* [How to Replicate Our Paper Experiments](#how-to-replicate-our-paper-experiments)
+ [Classification](#classification)
- [Sentiment analysis using the Dutch Book Review Dataset](#sentiment-analysis-using-the-dutch-book-review-dataset)
- [Predicting the Dutch pronouns _die_ and _dat_](#predicting-the-dutch-pronouns-die-and-dat)
* [Name Origin of RobBERT](#name-origin-of-robbert)
* [Credits and citation](#credits-and-citation)
## How To Use
RobBERT uses the [RoBERTa](https://arxiv.org/abs/1907.11692) 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](https://huggingface.co/transformers/model_doc/roberta.html) models and most code used for BERT models, e.g. as provided by [HuggingFace Transformers](https://huggingface.co/transformers/) library.
RobBERT can easily be used in two different ways, namely either using [Fairseq RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta) code or using [HuggingFace Transformers](https://github.com/huggingface/transformers)
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](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=De+hoofdstad+van+Belgi%C3%AB+is+%3Cmask%3E.). You can also create a new prediction head for your own task by using any of HuggingFace's [RoBERTa-runners](https://huggingface.co/transformers/v2.7.0/examples.html#language-model-training), [their fine-tuning notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) by changing the model name to `pdelobelle/robbert-v2-dutch-base`, or use the original fairseq [RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta) training regimes.
### Using Huggingface Transformers (easiest)
You can easily [download RobBERT v2](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) using [🤗 Transformers](https://github.com/huggingface/transformers).
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](https://pieter.ai/robbert/)).
```python
from transformers import RobertaTokenizer, RobertaForSequenceClassification
tokenizer = RobertaTokenizer.from_pretrained("pdelobelle/robbert-v2-dutch-base")
model = RobertaForSequenceClassification.from_pretrained("pdelobelle/robbert-v2-dutch-base")
```
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](https://huggingface.co/transformers/v4.1.1/notebooks.html) for finetuning RobBERT on your type of Dutch language dataset.
### Using Fairseq (harder)
Alternatively, you can also use RobBERT using the [RoBERTa architecture code]((https://github.com/pytorch/fairseq/tree/master/examples/roberta)).
You can download RobBERT v2's Fairseq model here: [(RobBERT-base, 1.5 GB)](https://github.com/iPieter/BERDT/releases/download/v1.0/RobBERT-base.pt).
Using RobBERT's `model.pt`, this method allows you to use all other functionalities of [RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta).
## Technical Details From The Paper
### Our Performance Evaluation Results
All experiments are described in more detail in our [paper](https://arxiv.org/abs/2001.06286), with the code in [our GitHub repository](https://github.com/iPieter/RobBERT).
### Sentiment analysis
Predicting whether a review is positive or negative using the [Dutch Book Reviews Dataset](https://github.com/benjaminvdb/DBRD).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| ULMFiT | 93.8 |
| BERTje | 93.0 |
| RobBERT v2 | **95.1** |
### Die/Dat (coreference resolution)
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](https://www.statmt.org/europarl/).
#### Finetuning on whole dataset
| Model | Accuracy [%] | F1 [%] |
|-------------------|--------------------------|--------------|
| [Baseline](https://arxiv.org/abs/2001.02943) (LSTM) | | 75.03 |
| mBERT | 98.285 | 98.033 |
| BERTje | 98.268 | 98.014 |
| RobBERT v2 | **99.232** | **99.121** |
#### Finetuning on 10K examples
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.
| Model | Accuracy [%] | F1 [%] |
|-------------------|--------------------------|--------------|
| mBERT | 92.157 | 90.898 |
| BERTje | 93.096 | 91.279 |
| RobBERT v2 | **97.816** | **97.514** |
#### Using zero-shot word masking task
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.
| Model | Accuracy [%] |
|-------------------|--------------------------|
| ZeroR | 66.70 |
| mBERT | 90.21 |
| BERTje | 94.94 |
| RobBERT v2 | **98.75** |
### Part-of-Speech Tagging.
Using the [Lassy UD dataset](https://universaldependencies.org/treebanks/nl_lassysmall/index.html).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| Frog | 91.7 |
| mBERT | **96.5** |
| BERTje | 96.3 |
| RobBERT v2 | 96.4 |
Interestingly, we found that when dealing with **small data sets**, RobBERT v2 **significantly outperforms** other models.
### Named Entity Recognition
Using the [CoNLL 2002 evaluation script](https://www.clips.uantwerpen.be/conll2002/ner/).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| Frog | 57.31 |
| mBERT | **90.94** |
| BERT-NL | 89.7 |
| BERTje | 88.3 |
| RobBERT v2 | 89.08 |
## Pre-Training Procedure Details
We pre-trained RobBERT using the RoBERTa training regime.
We pre-trained our model on the Dutch section of the [OSCAR corpus](https://oscar-corpus.com/), 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](https://github.com/pytorch/fairseq/tree/master/examples/roberta), 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](https://github.com/pytorch/fairseq/tree/master/examples/roberta), 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.
## Investigating Limitations and Bias
In the [RobBERT paper](https://arxiv.org/abs/2001.06286), 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.
## How to Replicate Our Paper Experiments
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](https://pipenv.readthedocs.io/en/latest/) *(install by running `pip install pipenv` in your terminal)* by running `pipenv install`.
### Classification
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.
#### Sentiment analysis using the Dutch Book Review Dataset
- Download the Dutch book review dataset from [https://github.com/benjaminvdb/DBRD](https://github.com/benjaminvdb/DBRD), and save it to `data/raw/DBRD`
- Run `src/preprocess_dbrd.py` to prepare the dataset.
- To not be blind during training, we recommend to keep aside a small evaluation set from the training set. For this run `src/split_dbrd_training.sh`.
- Follow the notebook `notebooks/finetune_dbrd.ipynb` to finetune the model.
#### Predicting the Dutch pronouns _die_ and _dat_
We fine-tune our model on the Dutch [Europarl corpus](http://www.statmt.org/europarl/). 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 `` token.
```
python src/preprocess_diedat.py
. src/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._
## Name Origin of RobBERT
Most BERT-like models have the word *BERT* in their name (e.g. [RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html), [ALBERT](https://arxiv.org/abs/1909.11942), [CamemBERT](https://camembert-model.fr/), and [many, many others](https://huggingface.co/models?search=bert)).
As such, we queried our newly trained model using its masked language model to name itself *\bert* using [all](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Mijn+naam+is+%3Cmask%3Ebert.) [kinds](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Hallo%2C+ik+ben+%3Cmask%3Ebert.) [of](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Leuk+je+te+ontmoeten%2C+ik+heet+%3Cmask%3Ebert.) [prompts](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Niemand+weet%2C+niemand+weet%2C+dat+ik+%3Cmask%3Ebert+heet.), and it consistently called itself RobBERT.
We thought it was really quite fitting, given that RobBERT is a [*very* Dutch name](https://en.wikipedia.org/wiki/Robbert) *(and thus clearly a Dutch language model)*, and additionally has a high similarity to its root architecture, namely [RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html).
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](https://muppet.fandom.com/wiki/Bert) for the [RobBERT logo](https://github.com/iPieter/RobBERT/blob/master/res/robbert_logo.png).
## Credits and citation
This project is created by [Pieter Delobelle](https://github.com/iPieter), [Thomas Winters](https://github.com/twinters) 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.
If you would like to cite our paper or model, you can use the following BibTeX code:
```
@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"
}
```