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---
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
|