--- tags: - adapterhub:sv/cc100 - adapters - xmod language: - sv license: "mit" --- # Adapter `AdapterHub/xmod-base-sv_SE` for AdapterHub/xmod-base An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [sv/cc100](https://adapterhub.ml/explore/sv/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-sv_SE", 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 ## 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" } ```