Edit model card

SciBERT-SQuAD-QuAC

This is the SciBERT language representation model fine tuned for Question Answering. SciBERT is a pre-trained language model based on BERT that has been trained on a large corpus of scientific text. When fine tuning for Question Answering we combined SQuAD2.0 and QuAC datasets.

If using this model, please cite the following paper:

@inproceedings{otegi-etal-2020-automatic,
    title = "Automatic Evaluation vs. User Preference in Neural Textual {Q}uestion{A}nswering over {COVID}-19 Scientific Literature",
    author = "Otegi, Arantxa  and
      Campos, Jon Ander  and
      Azkune, Gorka  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.15",
    doi = "10.18653/v1/2020.nlpcovid19-2.15",
}
Downloads last month
77
Safetensors
Model size
110M params
Tensor type
F32
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Spaces using ixa-ehu/SciBERT-SQuAD-QuAC 3