Instructions to use AdapterHub/m2qa-xlm-roberta-base-mad-x-2-qa-head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use AdapterHub/m2qa-xlm-roberta-base-mad-x-2-qa-head with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("xlm-roberta-base") model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-2-qa-head", set_active=True) - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - adapter-transformers | |
| - xlm-roberta | |
| datasets: | |
| - rajpurkar/squad_v2 | |
| - UKPLab/m2qa | |
| # M2QA Adapter: QA Head 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/](https://aclanthology.org/2024.findings-emnlp.365/) | |
| 🏗️ 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 domain adapters. This QA adapter was trained on the SQuAD v2 dataset. | |
| 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-german") | |
| # 2. Load domain adapter | |
| domain_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-2-product-reviews") | |
| # 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 | |
| ``` | |
| @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", | |
| } | |
| ``` |