metadata
tags:
- adapters
- adapterhub:mr/cc100
- xmod
language:
- mr
license: mit
Adapter AdapterHub/xmod-base-mr_IN for AdapterHub/xmod-base
An adapter for the AdapterHub/xmod-base model that was trained on the mr/cc100 dataset.
This adapter was created for usage with the Adapters library.
Usage
First, install adapters:
pip install -U adapters
Now, the adapter can be loaded and activated like this:
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-mr_IN", source="hf", set_active=True)
Architecture & Training
This adapter was extracted from the original model checkpoint 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)
@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"
}