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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.adapters:pip install -U adapters1from adapters import AutoAdapterModel
2from adapters.composition import Stack
3
4model = AutoAdapterModel.from_pretrained("xlm-roberta-base")
5
6# 1. Load language adapter
7language_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-2-english")
8
9# 2. Load domain adapter
10domain_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-2-news")
11
12# 3. Load QA head adapter
13qa_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-2-qa-head")
14
15# 4. Activate them via the adapter stack
16model.active_adapters = Stack(language_adapter_name, domain_adapter_name, qa_adapter_name)@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",
}