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model = AutoAdapterModel.from_pretrained("distilbert-base-uncased")
config = AdapterConfig.load("pfeiffer")
model.load_adapter("qa/boolq@ukp", config=config)

Description

Adapter for distilbert-base-uncased in Pfeiffer architecture trained on the BoolQ dataset for 15 epochs with early stopping and a learning rate of 1e-4.

Properties

Pre-trained model
distilbert-base-uncased
Adapter type
Prediction Head
  Yes
Task
Question Answering
Dataset

Architecture

Name
pfeiffer
Non-linearity
relu
Reduction factor
16
{
  "ln_after": false,
  "ln_before": false,
  "mh_adapter": false,
  "output_adapter": true,
  "adapter_residual_before_ln": false,
  "non_linearity": null,
  "original_ln_after": true,
  "original_ln_before": true,
  "reduction_factor": null,
  "residual_before_ln": true
}

Author

  Name
Clifton Poth
  GitHub
  Twitter

Versions

Identifier Comment Score Download
1 DEFAULT

Citations

Architecture
@misc{pfeiffer2020adapterfusion,
  title={AdapterFusion: Non-Destructive Task Composition for Transfer Learning},
  author={Jonas Pfeiffer and Aishwarya Kamath and Andreas Rücklé and Kyunghyun Cho and Iryna Gurevych},
  year={2020},
  eprint={2005.00247},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
Task
@inproceedings{clark-etal-2019-boolq,
    title = "{B}ool{Q}: Exploring the Surprising Difficulty of Natural Yes/No Questions",
    author = "Clark, Christopher  and
      Lee, Kenton  and
      Chang, Ming-Wei  and
      Kwiatkowski, Tom  and
      Collins, Michael  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1300",
    doi = "10.18653/v1/N19-1300",
    pages = "2924--2936",
}