# Monolingual Dutch Models for Zero-Shot Text CLassification

This family of Dutch models were finetuned on combined data from the (translated) snli (cite) and SICK-NL datasets (cite). They are intended to be used in zero-shot classification for Dutch through Huggingface Pipelines.

## The Models

|   Base Model      | Huggingface id (fine-tuned)      |
|-------------------|---------------------|
| [BERTje](https://huggingface.co/GroNLP/bert-base-dutch-cased)        | this model             |
| [RobBERT V2](http://github.com/iPieter/robbert)    | robbert-v2-dutch-snli              |
| [RobBERTje](https://github.com/iPieter/robbertje)     | robbertje-dutch-nli          |  



## How to use

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:

- Premise: The food in this place was horrendous 
- Hypothesis: This is a negative review

For more information on using Natural Language Inference models for zero-shot text classification, we refer to this(link) paper.

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:


```python
from transformers import pipeline

classifier = pipeline(
                      task="zero-shot-classification",
                      model='robbert-v2-dutch-base-snli'
                    )


text_piece = "Het eten in dit restaurant is heel lekker."
labels = ["positief", "negatief", "neutraal"]
template = "Het sentiment van deze review is {}"

predictions = classifier(text_piece,
                         labels,
                         multi_class=False,
                         hypothesis_template=template
                         )
```


## Model Performance


### Performance on NLI task

|   Model           | Accuracy [%]             |  F1 [%]      |
|-------------------|--------------------------|--------------|
| BERTje-nli        | 92.157                   | 90.898       |
| RobBERT-v2-nli    | 93.096                   | 91.279       |
| RobBERTje-nli     | **97.816**               | **97.514**   |



## Credits and citation

TBD