Create README.md

README.md

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+# Monolingual Dutch Models for Zero-Shot Text CLassification
+
+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.
+
+## The Models
+
+|   Base Model      | Huggingface id (fine-tuned)      |
+|-------------------|---------------------|
+| [BERTje](https://huggingface.co/GroNLP/bert-base-dutch-cased)        | LoicDL/bert-base-dutch-cased-finetuned-snli            |
+| [RobBERT V2](http://github.com/iPieter/robbert)    | LoicDL/robbert-v2-dutch-finetuned-snli              |
+| [RobBERTje](https://github.com/iPieter/robbertje)     | this model          |  
+
+
+
+## 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 paper](https://arxiv.org/abs/1909.00161).
+
+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='LoicDL/robbertje-dutch-finetuned-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 [%]      |
+|-------------------|--------------------------|--------------|
+| bert-base-dutch-cased-finetuned-snli        | 86.21                   | 86.42       |
+| robbert-v2-dutch-finetuned-snli    | **87.61**                   | **88.02**       |
+| robbertje-dutch-finetuned-snli     | 83.28                   | 84.11       |
+
+
+
+## Credits and citation
+
+TBD