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README.md

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+---
+tags:
+- xlm-roberta
+- adapter-transformers
+---
+
+# Adapter `abhishekkuber/multiclass_cog_dist_xlmr_base` for xlm-roberta-base
+
+An [adapter](https://adapterhub.ml) for the `xlm-roberta-base` model that was trained on the None 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("xlm-roberta-base")
+adapter_name = model.load_adapter("abhishekkuber/multiclass_cog_dist_xlmr_base", 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": {
+    "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": 768,
+  "model_class": "XLMRobertaAdapterModel",
+  "model_name": "xlm-roberta-base",
+  "model_type": "xlm-roberta",
+  "name": "multiclass_cog_dist_xlmr_base",
+  "version": "adapters.1.0.1"
+}

head_config.json

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+{
+  "config": {
+    "activation_function": "tanh",
+    "bias": true,
+    "dropout_prob": null,
+    "head_type": "classification",
+    "label2id": {
+      "All-or-nothing thinking": 7,
+      "Emotional Reasoning": 4,
+      "Fortune-telling": 9,
+      "Labeling": 10,
+      "Magnification": 2,
+      "Mental filter": 8,
+      "Mind Reading": 6,
+      "No Distortion": 0,
+      "Overgeneralization": 1,
+      "Personalization": 3,
+      "Should statements": 5
+    },
+    "layers": 2,
+    "num_labels": 11,
+    "use_pooler": false
+  },
+  "hidden_size": 768,
+  "model_class": "XLMRobertaAdapterModel",
+  "model_name": "xlm-roberta-base",
+  "model_type": "xlm-roberta",
+  "name": "multiclass_cog_dist_xlmr_base",
+  "version": "adapters.1.0.1"
+}

pytorch_adapter.bin

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+oid sha256:82a4db735d453a1312e4e1ac7263f95dd880ee516238dc541962b7837d5db28e
+size 3596710

pytorch_model_head.bin

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+oid sha256:0264de91c47317fc6c34a518d03d14cd25675feb7f8b2789238f141a062b611a
+size 2398376