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AdapterHub/roberta-base-pf-commonsense_qa for roberta-baseroberta-base model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice.adapter-transformers:pip install -U adapter-transformers1from transformers import AutoModelWithHeads
2
3model = AutoModelWithHeads.from_pretrained("roberta-base")
4adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-commonsense_qa", source="hf")
5model.active_adapters = adapter_name1@inproceedings{poth-etal-2021-what-to-pre-train-on,
2 title={What to Pre-Train on? Efficient Intermediate Task Selection},
3 author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
4 booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
5 month = nov,
6 year = "2021",
7 address = "Online",
8 publisher = "Association for Computational Linguistics",
9 url = "https://arxiv.org/abs/2104.08247",
10 pages = "to appear",
11}