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--- |
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tags: |
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- adapter-transformers |
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- adapterhub:sentiment/sst-2 |
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- roberta |
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license: "apache-2.0" |
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--- |
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# Adapter `roberta-base-sst_pfeiffer` for roberta-base |
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Pfeiffer Adapter trained on the SST-2 task. |
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**This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library.** |
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## Usage |
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First, install `adapters`: |
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``` |
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pip install -U adapters |
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``` |
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Now, the adapter can be loaded and activated like this: |
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```python |
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from adapters import AutoAdapterModel |
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model = AutoAdapterModel.from_pretrained("roberta-base") |
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adapter_name = model.load_adapter("AdapterHub/roberta-base-sst_pfeiffer") |
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model.set_active_adapters(adapter_name) |
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``` |
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## Architecture & Training |
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- Adapter architecture: pfeiffer |
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- Prediction head: None |
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- Dataset: [SST-2](https://nlp.stanford.edu/sentiment/index.html) |
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## Author Information |
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- Author name(s): Jonas Pfeiffer |
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- Author email: [email protected] |
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- Author links: [Website](https://pfeiffer.ai), [GitHub](https://github.com/JoPfeiff), [Twitter](https://twitter.com/@PfeiffJo) |
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## Citation |
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```bibtex |
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@article{Pfeiffer2020AdapterFusion, |
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author = {Pfeiffer, Jonas and Kamath, Aishwarya and R{\"{u}}ckl{\'{e}}, Andreas and Cho, Kyunghyun and Gurevych, Iryna}, |
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journal = {arXiv preprint}, |
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title = {{AdapterFusion}: Non-Destructive Task Composition for Transfer Learning}, |
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url = {https://arxiv.org/pdf/2005.00247.pdf}, |
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year = {2020} |
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} |
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``` |
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*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/roberta-base-sst_pfeiffer.yaml*. |
