calpt commited on
Commit
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1 Parent(s): 60d9de1

Add adapter bert-base-uncased_sentiment_sst-2_pfeiffer version 1

Browse files
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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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+ - text-classification
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+ - adapter-transformers
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+ - adapterhub:sentiment/sst-2
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+ license: "apache-2.0"
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+ ---
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+
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+ # Adapter `bert-base-uncased_sentiment_sst-2_pfeiffer` for bert-base-uncased
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+
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+ Adapter in Pfeiffer architecture trained on the binary SST task for 20 epochs with early stopping and a learning rate of 1e-4.
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+ See https://arxiv.org/pdf/2007.07779.pdf.
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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-uncased")
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+ adapter_name = model.load_adapter("AdapterHub/bert-base-uncased_sentiment_sst-2_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: [SST-2](https://nlp.stanford.edu/sentiment/index.html)
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+
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+ ## Author Information
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+
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+ - Author name(s): Clifton Poth
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+ - Author email: calpt@mail.de
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+ - Author links: [Website](https://calpt.github.io), [GitHub](https://github.com/calpt), [Twitter](https://twitter.com/clifapt)
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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 and
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+ Andreas R\"uckl\'{e} and
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+ Clifton Poth and
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+ Aishwarya Kamath and
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+ Ivan Vuli\'{c} and
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+ Sebastian Ruder and
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+ Kyunghyun Cho and
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+ Iryna Gurevych},
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+ journal={arXiv preprint},
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+ year={2020},
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+ url={https://arxiv.org/abs/2007.07779}
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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-uncased_sentiment_sst-2_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": 768,
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+ "model_class": "BertAdapterModel",
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+ "model_name": "bert-base-uncased",
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+ "model_type": "bert",
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+ "name": "sst-2",
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+ "version": "0.2.0"
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+ }
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+ "layers": 1,
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+ "num_labels": 2,
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+ "use_pooler": true
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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-uncased",
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+ "model_type": "bert",
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+ "name": "sst-2",
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+ "version": "0.2.0"
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+ }
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