---
tags:
- adapterhub:sts/qqp
- bert
- adapter-transformers
- text-classification
license: "apache-2.0"
---

# Adapter `bert-base-uncased_sts_qqp_pfeiffer` for bert-base-uncased

Adapter in Pfeiffer architecture trained on the QQP 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_sts_qqp_pfeiffer")
model.set_active_adapters(adapter_name)
```

## Architecture & Training

- Adapter architecture: pfeiffer
- Prediction head: classification
- Dataset: [QQP](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs)

## 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_sts_qqp_pfeiffer.yaml*.