README.md

---
tags:
- adapter-transformers
- xlm-roberta
datasets:
- UKPLab/m2qa
---

# Adapter `AdapterHub/m2qa-xlm-roberta-base-mad-x-domain-qa-head` for xlm-roberta-base

An [adapter](https://adapterhub.ml) for the `xlm-roberta-base` model that was trained on the [UKPLab/m2qa](https://huggingface.co/datasets/UKPLab/m2qa/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.

## Usage

First, install `adapter-transformers`:

```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("xlm-roberta-base")
adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-domain-qa-head", source="hf", set_active=True)
```

## Architecture & Training


See our repository for more information: See https://github.com/UKPLab/m2qa/tree/main/Experiments/mad-x-domain


## Evaluation results

<!-- Add some description here -->

## Citation


```
@article{englaender-etal-2024-m2qa,
    title="M2QA: Multi-domain Multilingual Question Answering",
    author={Engl{\"a}nder, Leon and
        Sterz, Hannah and
        Poth, Clifton and
        Pfeiffer, Jonas and
        Kuznetsov, Ilia and
        Gurevych, Iryna},
    journal={arXiv preprint},
    url="https://arxiv.org/abs/2407.01091",
    month = jul,
    year="2024"
}
```