---
license: apache-2.0
base_model: albert-xxlarge-v2
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
---


# albert-xxlarge-v2-description2genre

This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) for multi-label classification with 18 labels.
It achieves the following results on the evaluation set:
- Loss: 0.1905
- F1: 0.7058

## Usage

```python
# pip install -q transformers accelerate optimum
from transformers import pipeline

pipe = pipeline(
    "text-classification", 
    model="BEE-spoke-data/albert-xxlarge-v2-description2genre"
)
pipe.model = pipe.model.to_bettertransformer()

description = "On the Road is a 1957 novel by American writer Jack Kerouac, based on the travels of Kerouac and his friends across the United States. It is considered a defining work of the postwar Beat and Counterculture generations, with its protagonists living life against a backdrop of jazz, poetry, and drug use."  # @param {type:"string"}

result = pipe(description, return_all_scores=True)[0]
print(result)
```

> usage of BetterTransformer (via `optimum`) is optional, but recommended unless you enjoy waiting.

## Model description

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

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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2903        | 0.99  | 123  | 0.2686          | 0.4011 |
| 0.2171        | 2.0   | 247  | 0.2168          | 0.6493 |
| 0.1879        | 3.0   | 371  | 0.1990          | 0.6612 |
| 0.1476        | 4.0   | 495  | 0.1879          | 0.7060 |
| 0.1279        | 4.97  | 615  | 0.1905          | 0.7058 |


### Framework versions

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