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model = AutoAdapterModel.from_pretrained("bert-base-uncased")
config = AdapterConfig.load("pfeiffer")
model.load_adapter("ner/conll2003@ukp", config=config)

Description

Adapter trained on the CoNLL2003 dataset for named entity recognition

Properties

Pre-trained model
bert-base-uncased
Adapter type
Task
Prediction Head
  Yes
Task
Named Entity Recognition
Dataset
conll2003

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
Hannah Sterz
  E-Mail
sterz@ukp.informatik.tu-darmstadt.de
  Twitter
@Hannah70676760

Versions

Identifier Comment Score Download
NER 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
@article{sang2003introduction,
  title={Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition},
  author={Sang, Erik F and De Meulder, Fien},
  journal={arXiv preprint cs/0306050},
  year={2003}
}