metadata

license: mit
base_model: roberta-large-mnli
tags:
  - book
  - genre
  - book title
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
pipeline_tag: text-classification

roberta-large-mnli for title-genre classification

This model is a fine-tuned version of roberta-large-mnli on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2758
F1: 0.5464

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.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-10
lr_scheduler_type: linear
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.3096	1.0	62	0.2862	0.3707
0.2863	2.0	124	0.2804	0.4422
0.2618	3.0	186	0.2773	0.4989
0.2432	4.0	248	0.2764	0.5223
0.2241	5.0	310	0.2758	0.5464

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231001+cu121
Datasets 2.14.5
Tokenizers 0.13.3