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