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README.md

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+---
+tags:
+- adapter-transformers
+- roberta
+datasets:
+- mnli
+---
+
+# Adapter `julian-fong/roberta-base-reft-adapter` for roberta-base
+
+An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [mnli](https://huggingface.co/datasets/mnli/) dataset and includes a prediction head for classification.
+
+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-base")
+adapter_name = model.load_adapter("julian-fong/roberta-base-reft-adapter", set_active=True)
+```
+
+## Architecture & Training
+
+<!-- Add some description here -->
+
+## Evaluation results
+
+<!-- Add some description here -->
+
+## Citation
+
+<!-- Add some description here -->

adapter_config.json

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+{
+ "config": {
+ "architecture": "reft",
+ "dropout": 0.05,
+ "layers": "all",
+ "non_linearity": null,
+ "orthogonality": true,
+ "output_reft": true,
+ "prefix_positions": 1,
+ "r": 1,
+ "suffix_positions": 0,
+ "tied_weights": false
+ },
+ "hidden_size": 768,
+ "model_class": "RobertaAdapterModel",
+ "model_name": "roberta-base",
+ "model_type": "roberta",
+ "name": "loreft_adapter",
+ "version": "adapters.1.0.0"
+}

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,
+ "LABEL_2": 2
+ },
+ "layers": 2,
+ "num_labels": 3,
+ "use_pooler": false
+ },
+ "hidden_size": 768,
+ "model_class": "RobertaAdapterModel",
+ "model_name": "roberta-base",
+ "model_type": "roberta",
+ "name": "loreft_adapter",
+ "version": "adapters.1.0.0"
+}

pytorch_adapter.bin

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+size 56808534

pytorch_model_head.bin

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