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	this model
RobBERT V2	robbert-v2-dutch-snli
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:

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