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# Monolingual Dutch Models for Zero-Shot Text Classification |
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This family of Dutch models were finetuned on combined data from the (translated) [snli](https://nlp.stanford.edu/projects/snli/) and [SICK-NL](https://github.com/gijswijnholds/sick_nl) datasets. They are intended to be used in zero-shot classification for Dutch through Huggingface Pipelines. |
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## The Models |
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| Base Model | Huggingface id (fine-tuned) | |
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| [BERTje](https://huggingface.co/GroNLP/bert-base-dutch-cased) | LoicDL/bert-base-dutch-cased-finetuned-snli | |
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| [RobBERT V2](http://github.com/iPieter/robbert) | this model | |
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| [RobBERTje](https://github.com/iPieter/robbertje) | loicDL/robbertje-dutch-finetuned-snli | |
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## How to use |
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While this family of models can be used for evaluating (monolingual) NLI datasets, it's primary intended use is zero-shot text classification in Dutch. In this setting, classification tasks are recast as NLI problems. Consider the following sentence pairing that can be used to simulate a sentiment classification problem: |
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- Premise: The food in this place was horrendous |
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- Hypothesis: This is a negative review |
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For more information on using Natural Language Inference models for zero-shot text classification, we refer to [this paper](https://arxiv.org/abs/1909.00161). |
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By default, all our models are fully compatible with the Huggingface pipeline for zero-shot classification. They can be downloaded and accessed through the following code: |
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```python |
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from transformers import pipeline |
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classifier = pipeline( |
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task="zero-shot-classification", |
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model='LoicDL/robbert-v2-dutch-finetuned-snli' |
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) |
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text_piece = "Het eten in dit restaurant is heel lekker." |
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labels = ["positief", "negatief", "neutraal"] |
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template = "Het sentiment van deze review is {}" |
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predictions = classifier(text_piece, |
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labels, |
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multi_class=False, |
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hypothesis_template=template |
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) |
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``` |
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## Model Performance |
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### Performance on NLI task |
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| Model | Accuracy [%] | F1 [%] | |
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|-------------------|--------------------------|--------------| |
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| bert-base-dutch-cased-finetuned-snli | 86.21 | 86.42 | |
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| robbert-v2-dutch-finetuned-snli | **87.61** | **88.02** | |
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| robbertje-dutch-finetuned-snli | 83.28 | 84.11 | |
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## Credits and citation |
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TBD |
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