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roberta-large-qnli_pfeiffer

Text Classification
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roberta
adapterhub:nli/qnli
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roberta-large-qnli_pfeiffer
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---
tags:
- text-classification
- adapter-transformers
- roberta
- adapterhub:nli/qnli
license: "apache-2.0"
---

# Adapter `roberta-large-qnli_pfeiffer` for roberta-large

QNLI adapter (with head) trained using the `run_glue.py` script with an extension that retains the best checkpoint (out of 10 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-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): 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-qnli_pfeiffer.yaml*.