albert-xxlarge-v2-description2genre

This model is a fine-tuned version of 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

# 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

Framework versions

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