---
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](https://huggingface.co/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