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---
tags:
- bert
- adapterhub:nli/qnli
- adapter-transformers
- text-classification
license: "apache-2.0"
---
# Adapter `bert-base-uncased_nli_qnli_pfeiffer` for bert-base-uncased
Adapter in Pfeiffer architecture trained on the QNLI task for 20 epochs with early stopping and a learning rate of 1e-4.
See https://arxiv.org/pdf/2007.07779.pdf.
**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_nli_qnli_pfeiffer")
model.set_active_adapters(adapter_name)
```
## Architecture & Training
- Adapter architecture: pfeiffer
- Prediction head: classification
- Dataset: [QNLI](https://adapterhub.ml/explore/nli/qnli/)
## Author Information
- Author name(s): Clifton Poth
- Author email: calpt@mail.de
- Author links: [Website](https://calpt.github.io), [GitHub](https://github.com/calpt), [Twitter](https://twitter.com/clifapt)
## Citation
```bibtex
@article{pfeiffer2020AdapterHub,
title={AdapterHub: A Framework for Adapting Transformers},
author={Jonas Pfeiffer and
Andreas R\"uckl\'{e} and
Clifton Poth and
Aishwarya Kamath and
Ivan Vuli\'{c} and
Sebastian Ruder and
Kyunghyun Cho and
Iryna Gurevych},
journal={arXiv preprint},
year={2020},
url={https://arxiv.org/abs/2007.07779}
}
```
*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/bert-base-uncased_nli_qnli_pfeiffer.yaml*. |