readme: ---
  tags:
  - roberta
  - adapterhub:comsense/cosmosqa
  - adapter-transformers
  datasets:
  - cosmos_qa
  language:
  - en
  ---
  
  # Adapter `AdapterHub/roberta-base-pf-cosmos_qa` for roberta-base
  
  An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/cosmosqa](https://adapterhub.ml/explore/comsense/cosmosqa/) dataset and includes a prediction head for multiple choice.
  
  This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_
  
  Now, the adapter can be loaded and activated like this:
  
  ```python
  from transformers import AutoModelWithHeads
  
  model = AutoModelWithHeads.from_pretrained("roberta-base")
  adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-cosmos_qa", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
  
  
  ## Evaluation results
  
  Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
  
  ## Citation
  
  If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
  
  ```bibtex
  @inproceedings{poth-etal-2021-what-to-pre-train-on,
      title={What to Pre-Train on? Efficient Intermediate Task Selection},
      author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
      booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
      month = nov,
      year = "2021",
      address = "Online",
      publisher = "Association for Computational Linguistics",
      url = "https://arxiv.org/abs/2104.08247",
      pages = "to appear",
  }
  ```