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AdapterHub/
m2qa-xlm-roberta-base-mad-x-2-wiki

Name: m2qa-xlm-roberta-base-mad-x-2-wiki
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adapter-transformers

xlm-roberta

UKPLab/m2qa

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M2QA Adapter: Domain Adapter for MAD-X² Setup

This adapter is part of the M2QA publication to achieve language and domain transfer via adapters.
📃 Paper: https://aclanthology.org/2024.findings-emnlp.365/
🏗️ GitHub repo: https://github.com/UKPLab/m2qa
💾 Hugging Face Dataset: https://huggingface.co/UKPLab/m2qa

Important: This adapter only works together with the MAD-X-2 language and QA head adapter.

This adapter for the xlm-roberta-base model that was trained using the Adapters library. For detailed training details see our paper or GitHub repository: https://github.com/UKPLab/m2qa. You can find the evaluation results for this adapter on the M2QA dataset in the GitHub repo and in the paper.

Usage

First, install adapters:

pip install -U adapters

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

python

from adapters import AutoAdapterModel
from adapters.composition import Stack

model = AutoAdapterModel.from_pretrained("xlm-roberta-base")

# 1. Load language adapter
language_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-2-english") 

# 2. Load domain adapter
domain_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-2-wiki")

# 3. Load QA head adapter
qa_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-2-qa-head")

# 4. Activate them via the adapter stack
model.active_adapters = Stack(language_adapter_name, domain_adapter_name, qa_adapter_name)

See our repository for more information: See https://github.com/UKPLab/m2qa/tree/main/Experiments/mad-x-2

Contact

Leon Engländer:

Citation

@inproceedings{englander-etal-2024-m2qa,
    title = "M2QA: Multi-domain Multilingual Question Answering",
    author = {Engl{\"a}nder, Leon  and
      Sterz, Hannah  and
      Poth, Clifton A  and
      Pfeiffer, Jonas  and
      Kuznetsov, Ilia  and
      Gurevych, Iryna},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.365",
    pages = "6283--6305",
}