| tags: | |
| - gpt2 | |
| - text-classification | |
| - adapter-transformers | |
| - adapterhub:nli/qnli | |
| license: "apache-2.0" | |
| # Adapter `gpt2_nli_qnli_pfeiffer` for gpt2 | |
| Adapter for gpt2 in Pfeiffer architecture trained on the QNLI dataset for 10 epochs with a learning rate of 1e-4. | |
| **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("gpt2") | |
| adapter_name = model.load_adapter("AdapterHub/gpt2_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): Hannah Sterz | |
| - Author email: hannah.sterz@stud.tu-darmstadt.de | |
| - Author links: [Twitter](https://twitter.com/@h_sterz) | |
| ## Citation | |
| ```bibtex | |
| ``` | |
| *This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/gpt2_nli_qnli_pfeiffer.yaml*. |