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--- |
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license: apache-2.0 |
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base_model: google/mobilebert-uncased |
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tags: |
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- dataset tools |
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- books |
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- book |
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- genre |
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metrics: |
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- f1 |
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widget: |
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- text: The Quantum Chip |
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example_title: Science Fiction & Fantasy |
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- text: One Dollar's Journey |
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example_title: Business & Finance |
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- text: Timmy The Talking Tree |
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example_title: idk fiction |
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- text: The Cursed Canvas |
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example_title: Arts & Design |
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- text: Hoops and Hegel |
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example_title: Philosophy & Religion |
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- text: Overview of Streams in North Dakota |
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example_title: Nature |
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- text: Advanced Topology |
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example_title: Non-fiction/Math |
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- text: Cooking Up Love |
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example_title: Food & Cooking |
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- text: Dr. Doolittle's Extraplanatary Commute |
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example_title: Science & Technology |
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language: |
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- en |
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--- |
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# mobilebert-uncased-title2genre |
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) for multi-label classification (18 labels). |
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## Model description |
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This classifies one or more **genre** labels in a **multi-label** setting for a given book **title**. |
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The 'standard' way of interpreting the predictions is that the predicted labels for a given example are **only the ones with a greater than 50% probability.** |
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## Details |
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### Labels |
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There are 18 labels, these are already integrated into the `config.json` and should be output by the model: |
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```json |
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"id2label": { |
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"0": "History & Politics", |
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"1": "Health & Medicine", |
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"2": "Mystery & Thriller", |
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"3": "Arts & Design", |
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"4": "Self-Help & Wellness", |
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"5": "Sports & Recreation", |
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"6": "Non-Fiction", |
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"7": "Science Fiction & Fantasy", |
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"8": "Countries & Geography", |
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"9": "Other", |
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"10": "Nature & Environment", |
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"11": "Business & Finance", |
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"12": "Romance", |
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"13": "Philosophy & Religion", |
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"14": "Literature & Fiction", |
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"15": "Science & Technology", |
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"16": "Children & Young Adult", |
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"17": "Food & Cooking" |
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}, |
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``` |
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### Eval results (validation) |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2658 |
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- F1: 0.5395 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-10 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 10.0 |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 2.0.1+cpu |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
