--- tags: - adapter-transformers - xlm-roberta datasets: - UKPLab/m2qa --- # 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: [TODO](TODO) 🏗️ GitHub repo: [https://github.com/UKPLab/m2qa](https://github.com/UKPLab/m2qa) 💾 Hugging Face Dataset: [https://huggingface.co/UKPLab/m2qa](https://huggingface.co/UKPLab/m2qa) **Important:** This adapter only works together with the MAD-X-2 language and QA head adapter. This [adapter](https://adapterhub.ml) for the `xlm-roberta-base` model that was trained using the **[Adapters](https://github.com/Adapter-Hub/adapters)** library. For detailed training details see our paper or GitHub repository: [https://github.com/UKPLab/m2qa](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: - [HuggingFace Profile](https://huggingface.co/lenglaender) - [GitHub](https://github.com/lenglaender) - [Twitter](https://x.com/LeonEnglaender) ## 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={TODO} year="2024" } ```