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model = AutoAdapterModel.from_pretrained("roberta-base")
config = AdapterConfig.load("pfeiffer")
model.load_adapter("argument/ukpsent@ukp", config=config)

Description

Pfeiffer Adapter trained on the UKP Sentential Argument Mining dataset.

Properties

Pre-trained model
roberta-base
Adapter type
Prediction Head
  No
Task
Argument Mining

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
Jonas Pfeiffer
  GitHub
  Twitter

Versions

Identifier Comment Score Download
AdapterFusion DEFAULT

Citations

Adapter
@article{Pfeiffer2020AdapterFusion,
author = {Pfeiffer, Jonas and Kamath, Aishwarya and R{\"{u}}ckl{\'{e}}, Andreas and Cho, Kyunghyun and Gurevych, Iryna},
journal = {arXiv preprint},
title = {{AdapterFusion}:  Non-Destructive Task Composition for Transfer Learning},
 url       = {https://arxiv.org/pdf/2005.00247.pdf},
year = {2020}
}
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{stab-etal-2018-cross,
    title = "Cross-topic Argument Mining from Heterogeneous Sources",
    author = "Stab, Christian  and
      Miller, Tristan  and
      Schiller, Benjamin  and
      Rai, Pranav  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1402",
    doi = "10.18653/v1/D18-1402",
    pages = "3664--3674",
}