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

Description

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

Properties

Pre-trained model
distilbert-base-uncased
Adapter type
Task
Prediction Head
  Yes
Task
Sentiment Analysis
Dataset
Rotten Tomatoes Movie Reviews

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
  E-Mail
poth@ukp.informatik.tu-darmstadt.de
  Web
https://www.informatik.tu-darmstadt.de/ukp
  GitHub
calpt
  Twitter
@clifapt

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{Pang+Lee:05a,
  author = {Bo Pang and Lillian Lee},
  title = {Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales},
  year = {2005},
  pages = {115--124},
  booktitle = {Proceedings of ACL}
}