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---
tags:
- adapterhub:comsense/cosmosqa
- bert
- adapter-transformers
datasets:
- cosmos_qa
license: "apache-2.0"
---
# Adapter `bert-base-uncased-cosmosqa_pfeiffer` for bert-base-uncased
Pfeiffer Adapter trained on Cosmos QA.
**This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library.**
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-cosmosqa_pfeiffer")
model.set_active_adapters(adapter_name)
```
## Architecture & Training
- Adapter architecture: pfeiffer
- Prediction head: None
- Dataset: [Cosmos QA](https://www.tau-nlp.org/commonsenseqa)
## Author Information
- Author name(s): Jonas Pfeiffer
- Author email: jonas@pfeiffer.ai
- Author links: [Website](https://pfeiffer.ai), [GitHub](https://github.com/JoPfeiff), [Twitter](https://twitter.com/@PfeiffJo)
## Citation
```bibtex
@article{Pfeiffer2020AdapterFusion,
author = {Pfeiffer, Jonas and Kamath, Aishwarya and R{\"{u}}ckl{\'{e}}, Andreas and Cho, Kyunghyun and Gurevych, Iryna},
journal = {arXiv preprint},
title = {{AdapterFusion}: Non-Destructive Task Composition for Transfer Learning},
url = {https://arxiv.org/pdf/2005.00247.pdf},
year = {2020}
}
```
*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/bert-base-uncased-cosmosqa_pfeiffer.yaml*. |