RobBERT

Description:

RobBERT is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging task. As such, it has been successfully used by many researchers and practitioners for achieving state-of-the-art performance for a wide range of Dutch natural language processing tasks, including:

• Emotion detection

• Sentiment analysis (book reviews,news articles)

• Coreference resolution

• Named entity recognition (CoNLL, job titles, SoNaR)

• Part-of-speech tagging (Small UD Lassy, CGN)

• Zero-shot word prediction

• Humor detection

• Cyberbullying detection

• Correcting dt-spelling mistakes

and also achieved outstanding, near-sota results for:

• Natural language inference

• Review classification

More Information:

• Originates from the AI-Driven Data Science research challenge (FAIR 2019–2023).

• Responsible Research Lead: Bettina Berendt, Luc De Raedt

• Authors: Pieter Delobelle, Thomas Winters

• Programming Language: Python

• Link to the repository

• Categories: Natural Language Processing

Publications:

• P. Delobelle, T. Winters, B. Berendt, "RobBERT: a Dutch RoBERTa-based Language Model",  arXiv:2001.06286v2 [cs.CL], Sep. 2020.