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README.md

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+---
+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-creative-writing")
+
+# 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"
+}
+```

adapter_config.json

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+{
+  "config": {
+    "adapter_residual_before_ln": false,
+    "cross_adapter": false,
+    "factorized_phm_W": true,
+    "factorized_phm_rule": false,
+    "hypercomplex_nonlinearity": "glorot-uniform",
+    "init_weights": "bert",
+    "inv_adapter": null,
+    "inv_adapter_reduction_factor": null,
+    "is_parallel": false,
+    "learn_phm": true,
+    "leave_out": [],
+    "ln_after": false,
+    "ln_before": false,
+    "mh_adapter": false,
+    "non_linearity": "relu",
+    "original_ln_after": true,
+    "original_ln_before": true,
+    "output_adapter": true,
+    "phm_bias": true,
+    "phm_c_init": "normal",
+    "phm_dim": 4,
+    "phm_init_range": 0.0001,
+    "phm_layer": false,
+    "phm_rank": 1,
+    "reduction_factor": 2,
+    "residual_before_ln": true,
+    "scaling": 1.0,
+    "shared_W_phm": false,
+    "shared_phm_rule": true,
+    "use_gating": false
+  },
+  "hidden_size": 768,
+  "model_class": "XLMRobertaAdapterModel",
+  "model_name": "xlm-roberta-base",
+  "model_type": "xlm-roberta",
+  "name": "mad-x-2 creative_writing",
+  "version": "3.2.1"
+}

pytorch_adapter.bin

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