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

Description

Adapter AdapterHub/roberta-base-pf-boolq for roberta-base

An adapter for the roberta-base model that was trained on the qa/boolq dataset and includes a prediction head for classification.

This adapter was created for usage with the adapter-transformers library.

Usage

First, install adapter-transformers:

pip install -U adapter-transformers

Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More

Now, the adapter can be loaded and activated like this:

from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-boolq", source="hf")
model.active_adapters = adapter_name

Architecture & Training

The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. In particular, training configurations for all tasks can be found here.

Evaluation results

Refer to the paper for more information on results.

Citation

If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":

@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}

Properties

Pre-trained model
roberta-base
Adapter type
Task
Prediction Head
  Yes
Task
Question Answering
Dataset
BoolQ

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{clark-etal-2019-boolq,
    title = "{B}ool{Q}: Exploring the Surprising Difficulty of Natural Yes/No Questions",
    author = "Clark, Christopher  and
      Lee, Kenton  and
      Chang, Ming-Wei  and
      Kwiatkowski, Tom  and
      Collins, Michael  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1300",
    doi = "10.18653/v1/N19-1300",
    pages = "2924--2936",
}