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Add adapter roberta-large-cola_pfeiffer version 1

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._adapter_config.json ADDED
Binary file (280 Bytes). View file
 
._cola ADDED
Binary file (311 Bytes). View file
 
._head_config.json ADDED
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README.md ADDED
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+ ---
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+ tags:
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+ - text-classification
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+ - adapter-transformers
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+ - roberta
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+ - adapterhub:lingaccept/cola
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+ license: "apache-2.0"
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+ ---
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+
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+ # Adapter `roberta-large-cola_pfeiffer` for roberta-large
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+
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+ Adapter (with head) trained using the `run_glue.py` script with an extension that retains the best checkpoint (out of 30 epochs).
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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("roberta-large")
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+ adapter_name = model.load_adapter("AdapterHub/roberta-large-cola_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: classification
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+ - Dataset: [CoLA](https://nyu-mll.github.io/CoLA/)
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+
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+ ## Author Information
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+
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+ - Author name(s): Andreas Rücklé
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+ - Author email: rueckle@ukp.informatik.tu-darmstadt.de
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+ - Author links: [Website](http://rueckle.net), [GitHub](https://github.com/arueckle), [Twitter](https://twitter.com/arueckle)
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+
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+
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @article{pfeiffer2020AdapterHub,
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+ title={AdapterHub: A Framework for Adapting Transformers},
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+ author={Jonas Pfeiffer,
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+ Andreas R\"uckl\'{e},
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+ Clifton Poth,
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+ Aishwarya Kamath,
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+ Ivan Vuli\'{c},
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+ Sebastian Ruder,
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+ Kyunghyun Cho,
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+ Iryna Gurevych},
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+ journal={ArXiv},
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+ year={2020}
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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/roberta-large-cola_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": 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": "relu",
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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": 16,
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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": 1024,
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+ "model_class": "RobertaAdapterModel",
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+ "model_name": "roberta-large",
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+ "model_type": "roberta",
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+ "name": "cola",
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+ "version": "0.2.0"
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+ }
head_config.json ADDED
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+ {
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+ "config": {
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+ "activation_function": "tanh",
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+ "bias": true,
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+ "dropout_prob": null,
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+ "head_type": "classification",
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+ "label2id": {
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+ "LABEL_0": 0,
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+ "LABEL_1": 1
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+ },
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+ "layers": 2,
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+ "num_labels": 2,
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+ "use_pooler": false
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+ },
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+ "hidden_size": 1024,
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+ "model_class": "RobertaAdapterModel",
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+ "model_name": "roberta-large",
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+ "model_type": "roberta",
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+ "name": "cola",
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+ "version": "0.2.0"
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+ }
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