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Add adapter bert-base-multilingual-cased-tk-wiki_pfeiffer version nd

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._adapter_config.json ADDED
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._tk_pfeiffer_gelu_nd ADDED
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README.md ADDED
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+ ---
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+ tags:
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+ - bert
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+ - adapter-transformers
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+ - adapterhub:tk/wiki
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+ language:
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+ - tk
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+ license: "apache-2.0"
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+ ---
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+
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+ # Adapter `bert-base-multilingual-cased-tk-wiki_pfeiffer` for bert-base-multilingual-cased
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+
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+ Pfeiffer Adapter trained with Masked Language Modelling on Turkmen Wikipedia Articles for 50k steps and a batch size of 64.
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+
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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("bert-base-multilingual-cased")
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+ adapter_name = model.load_adapter("AdapterHub/bert-base-multilingual-cased-tk-wiki_pfeiffer")
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+ model.set_active_adapters(adapter_name)
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+ ```
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+
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+ ## Architecture & Training
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+
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+ - Adapter architecture: pfeiffer
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+ - Prediction head: None
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+ - Dataset: [tk/wiki](https://adapterhub.ml/explore/tk/wiki/)
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+
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+ ## Author Information
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+
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+ - Author name(s): Jonas Pfeiffer
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+ - Author email: jonas@pfeiffer.ai
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+ - Author links: [Website](https://pfeiffer.ai), [GitHub](https://github.com/jopfeiff), [Twitter](https://twitter.com/@PfeiffJo)
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+
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+ ## Versions
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+ - `nd` **(main)**
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+ - `wd`
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @article{pfeiffer20madx,
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+ title={{MAD-X}: An {A}dapter-based {F}ramework for {M}ulti-task {C}ross-lingual {T}ransfer},
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+ author={Pfeiffer, Jonas and Vuli\'{c}, Ivan and Gurevych, Iryna and Ruder, Sebastian},
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+ journal={arXiv preprint},
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+ year={2020},
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+ url={https://arxiv.org/pdf/2005.00052.pdf},
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+ }
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+
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+ ```
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+
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+ *This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/bert-base-multilingual-cased-tk-wiki_pfeiffer.yaml*.
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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+ "dropout": 0.0,
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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": "nice",
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+ "inv_adapter_reduction_factor": 2,
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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_new",
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+ "original_ln_after": true,
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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": true,
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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": "BertAdapterModel",
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+ "model_name": "bert-base-multilingual-cased",
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+ "model_type": "bert",
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+ "name": "tk",
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+ "version": "0.2.0"
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+ }
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