CoNTACT

Model description

Co ntextual N eural T ransformer A dapted to C OVID-19 T weets or CoNTACT is a Dutch RobBERT model ( pdelobelle/robbert-v2-dutch-base ) adapted to the domain of COVID-19 tweets. The model was developed at CLiPS by Jens Lemmens, Jens Van Nooten, Tim Kreutz and Walter Daelemans. A full description of the model, the data that was used and the experiments that were conducted can be found in this ArXiv preprint: https://arxiv.org/abs/2203.07362

Intended use

The model was developed with the intention of achieving high results on NLP tasks involving Dutch social media messages related to COVID-19.

How to use

CoNTACT should be fine-tuned on a downstream task. This can be achieved by referring to clips/contact in the --model_name_or_path argument in Huggingface/Transformers' example scripts, or by loading CoNTACT (as shown below) and fine-tuning it using your own code:

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained('clips/contact')
tokenizer = AutoTokenizer.from_pretrained('clips/contact')

...

Training data

CoNTACT was trained on 2.8M Dutch tweets related to COVID-19 that were posted in 2021.

Training Procedure

The model's pre-training phase was extended by performing Masked Language Modeling (MLM) on the training data described above. This was done for 4 epochs, using the largest possible batch size that fit working memory (32).

Evaluation

The model was evaluated on two tasks using data from two social media platforms: Twitter and Facebook. Task 1 involved the binary classification of COVID-19 vaccine stance (hesitant vs. not hesitant), whereas task 2 consisted of the mulilabel, multiclass classification of arguments for vaccine hesitancy. CoNTACT outperformed out-of-the-box RobBERT in virtually all our experiments, and with statistical significance in most cases.

How to cite

@misc{lemmens2022contact,
    title={CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection},
    author={Jens Lemmens and Jens Van Nooten and Tim Kreutz and Walter Daelemans},
    year={2022},
    eprint={2203.07362},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}