Add adapter bert-base-uncased_nli_rte_houlsby version 1

.gitattributes

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README.md

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+---
+tags:
+- bert
+- text-classification
+- adapter-transformers
+- adapterhub:nli/rte
+license: "apache-2.0"
+---
+
+# Adapter `bert-base-uncased_nli_rte_houlsby` for bert-base-uncased
+
+Adapter in Houlsby architecture trained on the RTE task for 20 epochs with early stopping and a learning rate of 1e-4.
+See https://arxiv.org/pdf/2007.07779.pdf.
+
+
+**This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library.**
+
+## Usage
+
+First, install `adapters`:
+
+```
+pip install -U adapters
+```
+
+Now, the adapter can be loaded and activated like this:
+
+```python
+from adapters import AutoAdapterModel
+
+model = AutoAdapterModel.from_pretrained("bert-base-uncased")
+adapter_name = model.load_adapter("AdapterHub/bert-base-uncased_nli_rte_houlsby")
+model.set_active_adapters(adapter_name)
+```
+
+## Architecture & Training
+
+- Adapter architecture: houlsby
+- Prediction head: classification
+- Dataset: [RTE](https://aclweb.org/aclwiki/Recognizing_Textual_Entailment)
+
+## Author Information
+
+- Author name(s): Clifton Poth
+- Author email: [email protected]
+- Author links: [Website](https://calpt.github.io), [GitHub](https://github.com/calpt), [Twitter](https://twitter.com/clifapt)
+
+
+
+## Citation
+
+```bibtex
+@article{pfeiffer2020AdapterHub,
+    title={AdapterHub: A Framework for Adapting Transformers},
+    author={Jonas Pfeiffer and
+            Andreas R\"uckl\'{e} and
+            Clifton Poth and
+            Aishwarya Kamath and
+            Ivan Vuli\'{c} and
+            Sebastian Ruder and
+            Kyunghyun Cho and
+            Iryna Gurevych},
+    journal={arXiv preprint},
+    year={2020},
+    url={https://arxiv.org/abs/2007.07779}
+}
+
+```
+
+*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/bert-base-uncased_nli_rte_houlsby.yaml*.

adapter_config.json

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+{
+  "config": {
+    "adapter_residual_before_ln": false,
+    "cross_adapter": false,
+    "dropout": 0.0,
+    "factorized_phm_W": true,
+    "factorized_phm_rule": false,
+    "hypercomplex_nonlinearity": "glorot-uniform",
+    "init_weights": "bert",
+    "inv_adapter": null,
+    "inv_adapter_reduction_factor": null,
+    "is_parallel": false,
+    "learn_phm": true,
+    "leave_out": [],
+    "ln_after": false,
+    "ln_before": false,
+    "mh_adapter": true,
+    "non_linearity": "swish",
+    "original_ln_after": true,
+    "original_ln_before": false,
+    "output_adapter": true,
+    "phm_bias": true,
+    "phm_c_init": "normal",
+    "phm_dim": 4,
+    "phm_init_range": 0.0001,
+    "phm_layer": false,
+    "phm_rank": 1,
+    "reduction_factor": 16,
+    "residual_before_ln": true,
+    "scaling": 1.0,
+    "shared_W_phm": false,
+    "shared_phm_rule": true,
+    "use_gating": false
+  },
+  "hidden_size": 768,
+  "model_class": "BertAdapterModel",
+  "model_name": "bert-base-uncased",
+  "model_type": "bert",
+  "name": "rte",
+  "version": "0.2.0"
+}

head_config.json

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+{
+  "config": {
+    "activation_function": null,
+    "bias": true,
+    "dropout_prob": null,
+    "head_type": "classification",
+    "label2id": {
+      "entailment": 0,
+      "not_entailment": 1
+    },
+    "layers": 1,
+    "num_labels": 2,
+    "use_pooler": true
+  },
+  "hidden_size": 768,
+  "model_class": "BertAdapterModel",
+  "model_name": "bert-base-uncased",
+  "model_type": "bert",
+  "name": "rte",
+  "version": "0.2.0"
+}

pytorch_adapter.bin

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pytorch_adapter.bin.backup

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pytorch_model_head.bin

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