M2QA Adapter: Domain Adapter for MAD-X+Domain 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 language adapters and the M2QA 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:
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("de/wiki@ukp") # MAD-X+Domain uses the MAD-X language adapter
# 2. Load domain adapter
domain_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-domain-product-reviews")
# 3. Load QA head adapter
qa_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-domain-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-domain
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",
}
- Downloads last month
- 2