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---
tags:
- text-classification
- adapter-transformers
- roberta
- adapterhub:sts/mrpc
license: "apache-2.0"
---
# Adapter `roberta-large-mrpc_pfeiffer` for roberta-large
MRPC adapter (with head) trained using the `run_glue.py` script with an extension that retains the best checkpoint (out of 30 epochs).
**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("roberta-large")
adapter_name = model.load_adapter("AdapterHub/roberta-large-mrpc_pfeiffer")
model.set_active_adapters(adapter_name)
```
## Architecture & Training
- Adapter architecture: pfeiffer
- Prediction head: classification
- Dataset: [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398)
## Author Information
- Author name(s): Andreas Rücklé
- Author email: [email protected]
- Author links: [Website](http://rueckle.net), [GitHub](https://github.com/arueckle), [Twitter](https://twitter.com/@arueckle)
## Citation
```bibtex
@article{pfeiffer2020AdapterHub,
title={AdapterHub: A Framework for Adapting Transformers},
author={Jonas Pfeiffer,
Andreas R\"uckl\'{e},
Clifton Poth,
Aishwarya Kamath,
Ivan Vuli\'{c},
Sebastian Ruder,
Kyunghyun Cho,
Iryna Gurevych},
journal={ArXiv},
year={2020}
}
```
*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/roberta-large-mrpc_pfeiffer.yaml*. |