bertlawbr

This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0495

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 10000
• num_epochs: 20.0
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.12.5
• Pytorch 1.10.1+cu113
• Datasets 1.17.0
• Tokenizers 0.10.3

Citing & Authors

If you use our work, please cite:

@incollection{Viegas_2023,
	doi = {10.1007/978-3-031-36805-9_24},
	url = {https://doi.org/10.1007%2F978-3-031-36805-9_24},
	year = 2023,
	publisher = {Springer Nature Switzerland},
	pages = {349--365},
	author = {Charles F. O. Viegas and Bruno C. Costa and Renato P. Ishii},
	title = {{JurisBERT}: A New Approach that~Converts a~Classification Corpus into~an~{STS} One},
	booktitle = {Computational Science and Its Applications {\textendash} {ICCSA} 2023}
}