Add adapter roberta-large-mrpc_pfeiffer version 1

._pytorch_adapter.bin

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._pytorch_model_head.bin

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README.md

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+---
+tags:
+- text-classification
+- adapter-transformers
+- roberta
+- adapterhub:sts/mrpc
+license: "apache-2.0"
+---
+
+# Adapter `roberta-large-mrpc_pfeiffer` for roberta-large
+
+MRPC adapter (with head) trained using the `run_glue.py` script with an extension that retains the best checkpoint (out of 30 epochs).
+
+
+**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("roberta-large")
+adapter_name = model.load_adapter("AdapterHub/roberta-large-mrpc_pfeiffer")
+model.set_active_adapters(adapter_name)
+```
+
+## Architecture & Training
+
+- Adapter architecture: pfeiffer
+- Prediction head: classification
+- Dataset: [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398)
+
+## Author Information
+
+- Author name(s): Andreas Rücklé
+- Author email: rueckle@ukp.informatik.tu-darmstadt
+- Author links: [Website](http://rueckle.net), [GitHub](https://github.com/arueckle), [Twitter](https://twitter.com/@arueckle)
+
+
+
+## Citation
+
+```bibtex
+@article{pfeiffer2020AdapterHub,
+    title={AdapterHub: A Framework for Adapting Transformers},
+    author={Jonas Pfeiffer,
+            Andreas R\"uckl\'{e},
+            Clifton Poth,
+            Aishwarya Kamath,
+            Ivan Vuli\'{c},
+            Sebastian Ruder,
+            Kyunghyun Cho,
+            Iryna Gurevych},
+    journal={ArXiv},
+    year={2020}
+}
+
+```
+
+*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/roberta-large-mrpc_pfeiffer.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": false,
+    "non_linearity": "relu",
+    "original_ln_after": true,
+    "original_ln_before": true,
+    "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": 1024,
+  "model_class": "RobertaAdapterModel",
+  "model_name": "roberta-large",
+  "model_type": "roberta",
+  "name": "mrpc",
+  "version": "0.2.0"
+}

head_config.json

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+{
+  "config": {
+    "activation_function": "tanh",
+    "bias": true,
+    "dropout_prob": null,
+    "head_type": "classification",
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layers": 2,
+    "num_labels": 2,
+    "use_pooler": false
+  },
+  "hidden_size": 1024,
+  "model_class": "RobertaAdapterModel",
+  "model_name": "roberta-large",
+  "model_type": "roberta",
+  "name": "mrpc",
+  "version": "0.2.0"
+}

pytorch_adapter.bin

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pytorch_model_head.bin

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