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README.md

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+---
+tags:
+- adapterhub:af/cc100
+- adapters
+- xmod
+language:
+- af
+license: "mit"
+---
+
+# Adapter `AdapterHub/xmod-base-af_ZA` for xmod_output
+
+An [adapter](https://adapterhub.ml) for the `xmod_output` model that was trained on the [af/cc100](https://adapterhub.ml/explore/af/cc100/) dataset.
+
+This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
+
+## Usage
+
+First, install `adapters`:
+
+```
+pip install -U adapters
+```
+
+Now, the adapter can be loaded and activated like this:
+
+```python
+from adapters import AutoAdapterModel
+
+model = AutoAdapterModel.from_pretrained("xmod_output")
+adapter_name = model.load_adapter("AdapterHub/xmod-base-af_ZA", source="hf", set_active=True)
+```
+
+## Architecture & Training
+
+<!-- Add some description here -->
+
+## Evaluation results
+
+<!-- Add some description here -->
+
+## Citation
+
+@inproceedings{pfeiffer-etal-2022-lifting,
+    title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
+    author = "Pfeiffer, Jonas  and
+      Goyal, Naman  and
+      Lin, Xi  and
+      Li, Xian  and
+      Cross, James  and
+      Riedel, Sebastian  and
+      Artetxe, Mikel",
+    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jul,
+    year = "2022",
+    address = "Seattle, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.naacl-main.255",
+    doi = "10.18653/v1/2022.naacl-main.255",
+    pages = "3479--3495"
+}

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": "gelu",
+    "original_ln_after": false,
+    "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": false,
+    "scaling": 1.0,
+    "shared_W_phm": false,
+    "shared_phm_rule": true,
+    "use_gating": false
+  },
+  "hidden_size": 768,
+  "model_class": "XmodAdapterModel",
+  "model_name": "xmod_output",
+  "model_type": "xmod",
+  "name": "af_ZA",
+  "version": "0.0.0"
+}

pytorch_adapter.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b68abe1c92eaa33fad69282e44ff38a497b4e5cf1fb43b78782244f53236dba0
+size 28383333