Description
BERTje is a Dutch pre-trained BERT model developed at the University of Groningen.
For details, check out our paper on arXiv, the code on Github and related work on Semantic Scholar. The paper and Github page mention fine-tuned models that are available here.
How to use
embeddings = BertEmbeddings.pretrained("bert_base_dutch_cased", "nl") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
val embeddings = BertEmbeddings.pretrained("bert_base_dutch_cased", "nl")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
import nlu
nlu.load("nl.embed.bert").predict("""Put your text here.""")
Model Information
| Model Name: | bert_base_dutch_cased |
| Compatibility: | Spark NLP 3.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, sentence] |
| Output Labels: | [embeddings] |
| Language: | nl |
| Case sensitive: | true |
Data Source
https://huggingface.co/dbmdz/bert-base-german-cased
Benchmarking
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 correspond 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](https://www.clips.uantwerpen.be/conll2002/ner/) | [SoNaR-1](https://ivdnt.org/downloads/taalmaterialen/tstc-sonar-corpus) | spaCy UD LassySmall |
| ---------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| **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) |
| [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) |
| [BERT-NL](http://textdata.nl) | 85.05 | 80.45 | 81.62 |
| [RobBERT](https://github.com/iPieter/RobBERT) | 84.72 | 81.98 | 79.84 |
### Part-of-speech tagging
| Model | [UDv2.5 LassySmall](https://universaldependencies.org/treebanks/nl_lassysmall/index.html) |
| ---------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| **BERTje** | **96.48** |
| [mBERT](https://github.com/google-research/bert/blob/master/multilingual.md) | 96.20 |
| [BERT-NL](http://textdata.nl) | 96.10 |
| [RobBERT](https://github.com/iPieter/RobBERT) | 95.91 |
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