---
license: mit
base_model: roberta-base
tags:
  - genre
  - books
  - multi-label
  - dataset tools
metrics:
  - f1
widget:
  - text: >-
      Meet Gertrude, a penguin detective who can't stand the cold. When a shrimp
      cocktail goes missing from the Iceberg Lounge, it's up to her to solve the
      mystery, wearing her collection of custom-made tropical turtlenecks.
    example_title: Tropical Turtlenecks
  - text: >-
      Professor Wobblebottom, a notorious forgetful scientist, invents a time
      machine but forgets how to use it. Now he is randomly popping into
      significant historical events, ruining everything. The future of the past
      is in the balance.
    example_title: When I Forgot The Time
  - text: >-
      In a world where hugs are currency and your social credit score is
      determined by your knack for dad jokes, John, a man who is allergic to
      laughter, has to navigate his way without becoming broke—or
      broken-hearted.
    example_title: Laugh Now, Pay Later
  - text: >-
      Emily, a vegan vampire, is faced with an ethical dilemma when she falls
      head over heels for a human butcher named Bob. Will she bite the forbidden
      fruit or stick to her plant-based blood substitutes?
    example_title: Love at First Bite... Or Not
  - text: >-
      Steve, a sentient self-driving car, wants to be a Broadway star. His dream
      seems unreachable until he meets Sally, a GPS system with the voice of an
      angel and ambitions of her own.
    example_title: Broadway or Bust
  - text: >-
      Dr. Fredrick Tensor, a socially awkward computer scientist, is on a quest
      to perfect AI companionship. However, his models keep outputting
      cringe-worthy, melodramatic waifus that scare away even the most die-hard
      fans of AI romance. Frustrated and lonely, Fredrick must debug his love
      life and algorithms before it's too late.
    example_title: Love.exe Has Stopped Working
language:
  - en
pipeline_tag: text-classification
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# BEE-spoke-data/roberta-base-description2genre

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2130
- F1: 0.6717

## Model description

This classifies one or more **genre** labels in a **multi-label** setting for a given book **description**.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-10
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- num_epochs: 6.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3118        | 1.0   | 62   | 0.2885          | 0.3362 |
| 0.2676        | 2.0   | 124  | 0.2511          | 0.4882 |
| 0.2325        | 3.0   | 186  | 0.2272          | 0.6093 |
| 0.2127        | 4.0   | 248  | 0.2181          | 0.6591 |
| 0.1978        | 5.0   | 310  | 0.2140          | 0.6686 |
| 0.1817        | 6.0   | 372  | 0.2130          | 0.6717 |


### Framework versions

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