add model

README.md

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+---
+tags:
+- bert
+- adapterhub:text-classification
+- adapter-transformers
+---
+
+# Adapter `davanstrien/book-genre-classification` for bert-base-cased
+
+An [adapter](https://adapterhub.ml) for the `bert-base-cased` model that was trained on the [text-classification](https://adapterhub.ml/explore/text-classification/) 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 AutoModelWithHeads
+
+model = AutoModelWithHeads.from_pretrained("bert-base-cased")
+adapter_name = model.load_adapter("davanstrien/book-genre-classification", 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": {
+    "adapter_residual_before_ln": false,
+    "cross_adapter": false,
+    "inv_adapter": null,
+    "inv_adapter_reduction_factor": null,
+    "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,
+    "reduction_factor": 16,
+    "residual_before_ln": true
+  },
+  "hidden_size": 768,
+  "model_class": "BertModelWithHeads",
+  "model_name": "bert-base-cased",
+  "model_type": "bert",
+  "name": "contemporary-genre"
+}

head_config.json

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+{
+  "config": {
+    "activation_function": "tanh",
+    "bias": true,
+    "head_type": "classification",
+    "label2id": {
+      "Arts & Photography": 12,
+      "Biographies & Memoirs": 22,
+      "Business & Money": 20,
+      "Calendars": 0,
+      "Children's Books": 30,
+      "Christian Books & Bibles": 26,
+      "Comics & Graphic Novels": 1,
+      "Computers & Technology": 16,
+      "Cookbooks, Food & Wine": 10,
+      "Crafts, Hobbies & Home": 11,
+      "Education & Teaching": 13,
+      "Engineering & Transportation": 9,
+      "Gay & Lesbian": 8,
+      "Health, Fitness & Dieting": 19,
+      "History": 23,
+      "Humor & Entertainment": 6,
+      "Law": 21,
+      "Literature & Fiction": 7,
+      "Medical Books": 17,
+      "Mystery, Thriller & Suspense": 3,
+      "Parenting & Relationships": 14,
+      "Politics & Social Sciences": 24,
+      "Reference": 25,
+      "Religion & Spirituality": 27,
+      "Romance": 5,
+      "Science & Math": 18,
+      "Science Fiction & Fantasy": 4,
+      "Self-Help": 15,
+      "Sports & Outdoors": 28,
+      "Teen & Young Adult": 29,
+      "Test Preparation": 2,
+      "Travel": 31
+    },
+    "layers": 2,
+    "num_labels": 32,
+    "use_pooler": false
+  },
+  "hidden_size": 768,
+  "model_class": "BertModelWithHeads",
+  "model_name": "bert-base-cased",
+  "model_type": "bert",
+  "name": "contemporary-genre"
+}

pytorch_adapter.bin

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+size 3594645

pytorch_model_head.bin

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+size 2462239