| | --- |
| | tags: |
| | - adapterhub:eo/cc100 |
| | - adapter-transformers |
| | - xmod |
| | language: |
| | - eo |
| | license: "mit" |
| | --- |
| | |
| | # Adapter `AdapterHub/xmod-base-eo_EO` for AdapterHub/xmod-base |
| | |
| | An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [eo/cc100](https://adapterhub.ml/explore/eo/cc100/) dataset. |
| | |
| | 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("AdapterHub/xmod-base") |
| | adapter_name = model.load_adapter("AdapterHub/xmod-base-eo_EO", source="hf", set_active=True) |
| | ``` |
| | |
| | ## Architecture & Training |
| | |
| | This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library. |
| | For more information on architecture and training, please refer to the original model card. |
| | |
| | ## Evaluation results |
| | |
| | <!-- Add some description here --> |
| | |
| | ## Citation |
| | |
| | [Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) |
| | |
| | ``` |
| | @inproceedings{pfeiffer-etal-2022-lifting, |
| | title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers", |
| | author = "Pfeiffer, Jonas and |
| | Goyal, Naman and |
| | Lin, Xi and |
| | Li, Xian and |
| | Cross, James and |
| | Riedel, Sebastian and |
| | Artetxe, Mikel", |
| | booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
| | month = jul, |
| | year = "2022", |
| | address = "Seattle, United States", |
| | publisher = "Association for Computational Linguistics", |
| | url = "https://aclanthology.org/2022.naacl-main.255", |
| | doi = "10.18653/v1/2022.naacl-main.255", |
| | pages = "3479--3495" |
| | } |
| | ``` |