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README.md
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- UKPLab/m2qa
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#
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This adapter is part of the M2QA publication to achieve language and domain transfer via adapters.
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📃 Paper: [TODO](TODO)
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🏗️ GitHub repo: [https://github.com/UKPLab/m2qa](https://github.com/UKPLab/m2qa)
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💾 Hugging Face Dataset: [https://huggingface.co/UKPLab/m2qa](https://huggingface.co/UKPLab/m2qa)
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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.
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## Usage
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First, install `
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```
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pip install -U
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```
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Now, the adapter can be loaded and activated like this:
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```python
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from
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from adapters.composition import Stack
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model = AutoAdapterModel.from_pretrained("xlm-roberta-base")
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# 1. Load language adapter
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language_adapter_name = model.load_adapter("de/wiki@ukp") # MAD-X+Domain uses the MAD-X language adapter
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# 2. Load domain adapter
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domain_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-domain-wiki")
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# 3. Load QA head adapter
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qa_adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-domain-qa-head")
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# 4. Activate them via the adapter stack
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model.active_adapters = Stack(language_adapter_name, domain_adapter_name, qa_adapter_name)
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```
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See our repository for more information: See https://github.com/UKPLab/m2qa/tree/main/Experiments/mad-x-domain
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##
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- [GitHub](https://github.com/lenglaender)
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- [Twitter](https://x.com/LeonEnglaender)
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## Citation
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```
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@article{englaender-etal-2024-m2qa,
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title="M2QA: Multi-domain Multilingual Question Answering",
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author={Engl{"a}nder, Leon and
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Sterz, Hannah and
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Poth, Clifton and
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Pfeiffer, Jonas and
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Kuznetsov, Ilia and
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Gurevych, Iryna},
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journal={arXiv preprint},
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url=
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year="2024"
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}
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```
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- UKPLab/m2qa
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# Adapter `AdapterHub/m2qa-xlm-roberta-base-mad-x-domain-wiki` for xlm-roberta-base
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An [adapter](https://adapterhub.ml) for the `xlm-roberta-base` model that was trained on the [UKPLab/m2qa](https://huggingface.co/datasets/UKPLab/m2qa/) dataset.
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This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
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## Usage
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First, install `adapter-transformers`:
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```
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pip install -U adapter-transformers
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```
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_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
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Now, the adapter can be loaded and activated like this:
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```python
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from transformers import AutoAdapterModel
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model = AutoAdapterModel.from_pretrained("xlm-roberta-base")
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adapter_name = model.load_adapter("AdapterHub/m2qa-xlm-roberta-base-mad-x-domain-wiki", source="hf", set_active=True)
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```
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## Architecture & Training
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See our repository for more information: See https://github.com/UKPLab/m2qa/tree/main/Experiments/mad-x-domain
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## Evaluation results
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<!-- Add some description here -->
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## Citation
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```
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@article{englaender-etal-2024-m2qa,
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title="M2QA: Multi-domain Multilingual Question Answering",
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author={Engl{\"a}nder, Leon and
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Sterz, Hannah and
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Poth, Clifton and
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Pfeiffer, Jonas and
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Kuznetsov, Ilia and
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Gurevych, Iryna},
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journal={arXiv preprint},
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url="https://arxiv.org/abs/2407.01091",
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month = jul,
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year="2024"
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}
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```
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