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