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:
Usage
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."
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