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

Description

Adapter for bart-large in Pfeiffer architecture trained on the CNN/ DailyMail 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
CNN/ DailyMail

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": 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 20.86

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{Hermann2015TeachingMT,
  title={Teaching Machines to Read and Comprehend},
  author={K. Hermann and Tom{\'a}s Kocisk{\'y} and Edward Grefenstette and Lasse Espeholt and W. Kay and Mustafa Suleyman and P. Blunsom},
  booktitle={NIPS},
  year={2015}
}