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README.md

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+---
+tags:
+- bert
+- adapter-transformers
+datasets:
+- redasers/difraud
+---
+
+# Adapter `dainis-boumber/df-adapters-twitter-rumours` for bert-base-uncased
+
+An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [redasers/difraud](https://huggingface.co/datasets/redasers/difraud/) dataset and includes a prediction head for classification.
+
+This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
+
+## Usage
+
+First, install `adapter-transformers`:
+
+```
+pip install -U adapter-transformers
+```
+_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
+
+Now, the adapter can be loaded and activated like this:
+
+```python
+from transformers import AutoAdapterModel
+
+model = AutoAdapterModel.from_pretrained("bert-base-uncased")
+adapter_name = model.load_adapter("dainis-boumber/df-adapters-twitter-rumours", source="hf", 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": "union",
+    "configs": [
+      {
+        "architecture": "prefix_tuning",
+        "bottleneck_size": 512,
+        "cross_prefix": true,
+        "dropout": 0.0,
+        "encoder_prefix": true,
+        "flat": false,
+        "leave_out": [],
+        "non_linearity": "tanh",
+        "prefix_length": 10,
+        "shared_gating": true,
+        "use_gating": true
+      },
+      {
+        "adapter_residual_before_ln": false,
+        "cross_adapter": false,
+        "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": true
+      },
+      {
+        "alpha": 8,
+        "architecture": "lora",
+        "attn_matrices": [
+          "q",
+          "v"
+        ],
+        "composition_mode": "add",
+        "dropout": 0.0,
+        "init_weights": "lora",
+        "intermediate_lora": false,
+        "output_lora": false,
+        "r": 8,
+        "selfattn_lora": true,
+        "use_gating": true
+      }
+    ]
+  },
+  "hidden_size": 768,
+  "model_class": "BertModelWithHeads",
+  "model_name": "bert-base-uncased",
+  "model_type": "bert",
+  "name": "difraud-twitter_rumours",
+  "version": "3.2.1"
+}

head_config.json

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+{
+  "config": {
+    "activation_function": "tanh",
+    "bias": true,
+    "head_type": "classification",
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layers": 2,
+    "num_labels": 2,
+    "use_pooler": false
+  },
+  "hidden_size": 768,
+  "model_class": "BertModelWithHeads",
+  "model_name": "bert-base-uncased",
+  "model_type": "bert",
+  "name": "difraud-twitter_rumours",
+  "version": "3.2.1"
+}

pytorch_adapter.bin

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+oid sha256:a9b8dd0aded280e6c9e61c9f0e81f652abbfe02733b52693929c8c96011ad5a0
+size 44420463

pytorch_model_head.bin

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+size 2370273