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model = AutoAdapterModel.from_pretrained("roberta-base")
model.load_adapter("AdapterHub/roberta-base-pf-ud_en_ewt", source="hf")

Description

| | bert-base-uncased | 91.74 | 89.15 | | roberta-base | 91.43 | 88.43 |

Citation

Properties

Pre-trained model
roberta-base
Adapter type
Task
Prediction Head
  Yes
Task
Dependency Parsing
Dataset
UD English EWT

Architecture

{
  "adapter_residual_before_ln": false,
  "cross_adapter": false,
  "inv_adapter": null,
  "inv_adapter_reduction_factor": null,
  "leave_out": [],
  "ln_after": false,
  "ln_before": false,
  "mh_adapter": false,
  "non_linearity": "relu",
  "original_ln_after": true,
  "original_ln_before": true,
  "output_adapter": true,
  "reduction_factor": 16,
  "residual_before_ln": true
}

Citations

Task
@inproceedings{silveira14gold,
  year = {2014},
  author = {Natalia Silveira and Timothy Dozat and Marie-Catherine de
    Marneffe and Samuel Bowman and Miriam Connor and John Bauer and
    Christopher D. Manning},
  title = {A Gold Standard Dependency Corpus for {E}nglish},
  booktitle = {Proceedings of the Ninth International Conference on Language
    Resources and Evaluation (LREC-2014)}
}