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Upload model

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  1. README.md +66 -0
  2. adapter_config.json +40 -0
  3. pytorch_adapter.bin +3 -0
README.md ADDED
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+ ---
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+ tags:
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+ - adapterhub:or/cc100
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+ - adapters
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+ - xmod
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+ language:
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+ - or
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+ license: "mit"
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+ ---
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+
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+ # Adapter `AdapterHub/xmod-base-or_IN` for AdapterHub/xmod-base
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+
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+ An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [or/cc100](https://adapterhub.ml/explore/or/cc100/) dataset.
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+
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+ This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
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+
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+ ## Usage
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+
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+ First, install `adapters`:
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+
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+ ```
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+ pip install -U adapters
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+ ```
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+
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+ Now, the adapter can be loaded and activated like this:
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+
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+ ```python
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+ from adapters import AutoAdapterModel
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+
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+ model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
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+ adapter_name = model.load_adapter("AdapterHub/xmod-base-or_IN", source="hf", set_active=True)
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+ ```
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+
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+ ## Architecture & Training
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+
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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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+
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+ ## Evaluation results
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+
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+ <!-- Add some description here -->
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+
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+ ## Citation
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+
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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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+ ```
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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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+ ```
adapter_config.json ADDED
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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": "or_IN",
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+ "version": "0.0.0"
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+ }
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