| tags: | |
| - adapter-transformers | |
| - adapterhub:qa/boolq | |
| - distilbert | |
| - text-classification | |
| datasets: | |
| - boolq | |
| license: "apache-2.0" | |
| # Adapter `distilbert-base-uncased_qa_boolq_pfeiffer` for distilbert-base-uncased | |
| Adapter for distilbert-base-uncased in Pfeiffer architecture trained on the BoolQ dataset for 15 epochs with early stopping and 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("distilbert-base-uncased") | |
| adapter_name = model.load_adapter("AdapterHub/distilbert-base-uncased_qa_boolq_pfeiffer") | |
| model.set_active_adapters(adapter_name) | |
| ``` | |
| ## Architecture & Training | |
| - Adapter architecture: pfeiffer | |
| - Prediction head: classification | |
| - Dataset: [BoolQ]( https://goo.gl/boolq) | |
| ## 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 | |
| ``` | |
| *This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/distilbert-base-uncased_qa_boolq_pfeiffer.yaml*. |