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Description

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

Usage

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

Properties

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

Architecture

Name
pfeiffer
Non-linearity
relu
Reduction factor
2
{
    "ln_after": false,
    "ln_before": false,
    "mh_adapter": false,
    "output_adapter": true,
    "adapter_residual_before_ln": false,
    "non_linearity": "relu",
    "original_ln_after": true,
    "original_ln_before": true,
    "reduction_factor": 16,
    "residual_before_ln": true
}

Author

  Name
Clifton Poth
  E-Mail
  GitHub
  Twitter

Versions

Identifier Comment Score Download
1 DEFAULT 20.56

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{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",
}