Views
No views yet
AdapterHub/bert-base-uncased-pf-cq for bert-base-uncasedbert-base-uncased model that was trained on the qa/cq dataset and includes a prediction head for question answering.adapter-transformers:pip install -U adapter-transformers1from transformers import AutoModelWithHeads
2
3model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
4adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-cq", source="hf")
5model.active_adapters = adapter_name1@inproceedings{poth-etal-2021-pre,
2 title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
3 author = {Poth, Clifton and
4 Pfeiffer, Jonas and
5 R{"u}ckl{'e}, Andreas and
6 Gurevych, Iryna},
7 booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
8 month = nov,
9 year = "2021",
10 address = "Online and Punta Cana, Dominican Republic",
11 publisher = "Association for Computational Linguistics",
12 url = "https://aclanthology.org/2021.emnlp-main.827",
13 pages = "10585--10605",
14}