Upload model
Browse files- README.md +66 -0
- adapter_config.json +40 -0
- pytorch_adapter.bin +3 -0
README.md
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---
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tags:
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- adapterhub:hy/cc100
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- adapters
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- xmod
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language:
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- hy
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license: "mit"
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---
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# Adapter `AdapterHub/xmod-base-hy_AM` for AdapterHub/xmod-base
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An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [hy/cc100](https://adapterhub.ml/explore/hy/cc100/) dataset.
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This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
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## Usage
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First, install `adapters`:
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```
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pip install -U adapters
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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 adapters import AutoAdapterModel
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model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
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adapter_name = model.load_adapter("AdapterHub/xmod-base-hy_AM", source="hf", set_active=True)
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```
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## Architecture & Training
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This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
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For more information on architecture and training, please refer to the original model card.
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## Evaluation results
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<!-- Add some description here -->
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## Citation
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[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
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```
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@inproceedings{pfeiffer-etal-2022-lifting,
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title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
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author = "Pfeiffer, Jonas and
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Goyal, Naman and
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Lin, Xi and
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Li, Xian and
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Cross, James and
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Riedel, Sebastian and
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Artetxe, Mikel",
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booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
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month = jul,
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year = "2022",
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address = "Seattle, United States",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2022.naacl-main.255",
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doi = "10.18653/v1/2022.naacl-main.255",
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pages = "3479--3495"
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}
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```
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adapter_config.json
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{
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"config": {
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"adapter_residual_before_ln": false,
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"cross_adapter": false,
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"factorized_phm_W": true,
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"factorized_phm_rule": false,
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"hypercomplex_nonlinearity": "glorot-uniform",
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"init_weights": "bert",
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"inv_adapter": null,
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"inv_adapter_reduction_factor": null,
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"is_parallel": false,
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"learn_phm": true,
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"leave_out": [],
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"ln_after": false,
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"ln_before": false,
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"mh_adapter": false,
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"non_linearity": "gelu",
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"original_ln_after": false,
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"original_ln_before": true,
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"output_adapter": true,
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"phm_bias": true,
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"phm_c_init": "normal",
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"phm_dim": 4,
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"phm_init_range": 0.0001,
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"phm_layer": false,
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"phm_rank": 1,
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"reduction_factor": 2,
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"residual_before_ln": false,
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"scaling": 1.0,
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"shared_W_phm": false,
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"shared_phm_rule": true,
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"use_gating": false
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},
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"hidden_size": 768,
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"model_class": "XmodAdapterModel",
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"model_name": "AdapterHub/xmod-base",
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"model_type": "xmod",
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"name": "hy_AM",
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"version": "0.0.0"
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}
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pytorch_adapter.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:25254dce8ae76461c760ea30a8f6dee60a3e67b63b42e17247d2964928315348
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size 28383333
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