Add adapter bert-base-uncased-cb_pfeiffer version AdapterFusion
Browse files- ._adapter_config.json +0 -0
- README.md +61 -0
- adapter_config.json +41 -0
- pytorch_adapter.bin +3 -0
._adapter_config.json
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Binary file (220 Bytes). View file
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README.md
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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:nli/cb
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license: "apache-2.0"
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---
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# Adapter `bert-base-uncased-cb_pfeiffer` for bert-base-uncased
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Pfeiffer Adapter trained on the CommitmentBank.
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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("bert-base-uncased")
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adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-cb_pfeiffer")
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model.set_active_adapters(adapter_name)
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```
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## Architecture & Training
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- Adapter architecture: pfeiffer
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- Prediction head: None
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- Dataset: [CommitmentBank](https://adapterhub.ml/explore/nli/cb/)
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## Author Information
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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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## Citation
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```bibtex
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@article{Pfeiffer2020AdapterFusion,
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author = {Pfeiffer, Jonas and Kamath, Aishwarya and R{\"{u}}ckl{\'{e}}, Andreas and Cho, Kyunghyun and Gurevych, Iryna},
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journal = {arXiv preprint},
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title = {{AdapterFusion}: Non-Destructive Task Composition for Transfer Learning},
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url = {https://arxiv.org/pdf/2005.00247.pdf},
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year = {2020}
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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-cb_pfeiffer.yaml*.
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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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"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": "cb",
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"version": "0.2.0"
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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 sha256:4b658a20735efc34962887d3102348dbcbca70406a3c8d5ed56a08aff1f728ef
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size 3594662
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