README.md

---
tags:
- adapters
- adapterhub:ml/cc100
- xmod
language:
- ml
license: "mit"
---

# Adapter `AdapterHub/xmod-base-ml_IN` for AdapterHub/xmod-base

An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ml/cc100](https://adapterhub.ml/explore/ml/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-ml_IN", 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"
}
```