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model = AutoAdapterModel.from_pretrained("facebook/bart-large")
config = AdapterConfig.load("houlsby", non_linearity="swish", reduction_factor=2)
model.load_adapter("sum/xsum@ukp", config=config)

Description

Adapter for bart-large in Pfeiffer architecture trained on the XSum dataset for 10 epochs with early stopping and a learning rate of 1e-4.

Properties

Pre-trained model
facebook/bart-large
Adapter type
Task
Prediction Head
  Yes
Task
Summarization
Dataset
XSum

Architecture

Name
houlsby
Non-linearity
swish
Reduction factor
2
{
  "ln_after": false,
  "ln_before": false,
  "mh_adapter": true,
  "output_adapter": true,
  "adapter_residual_before_ln": false,
  "non_linearity": "swish",
  "original_ln_after": true,
  "original_ln_before": false,
  "reduction_factor": 2,
  "residual_before_ln": true
}

Author

  Name
Clifton Poth
  E-Mail
calpt@mail.de
  Web
https://adapterhub.ml
  GitHub
calpt
  Twitter
@clifapt

Versions

Identifier Comment Score Download
1 DEFAULT 20.9

Citations

Architecture
@misc{houlsby2019parameterefficient,
  title={Parameter-Efficient Transfer Learning for NLP},
  author={Neil Houlsby and Andrei Giurgiu and Stanislaw Jastrzebski and Bruna Morrone and Quentin de Laroussilhe and Andrea Gesmundo and Mona Attariyan and Sylvain Gelly},
  year={2019},
  eprint={1902.00751},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}
Task
@InProceedings{xsum-emnlp,
  author =      "Shashi Narayan and Shay B. Cohen and Mirella Lapata",
  title =       "Don't Give Me the Details, Just the Summary! {T}opic-Aware Convolutional Neural Networks for Extreme Summarization",
  booktitle =   "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ",
  year =        "2018",
  address =     "Brussels, Belgium",
}