abundant-mule-873 – AI Model by ElMad | AlphaNeural AI

You can deploy this model and start earning money today!

ElMad/
abundant-mule-873

like

0

text-classification

generated_from_trainer

FacebookAI/roberta-base

autotrain_compatible

endpoints_compatible

Views

No views yet

abundant-mule-873

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

Loss: 0.1843
Hamming Loss: 0.0497
Zero One Loss: 1.0
Jaccard Score: 1.0
Hamming Loss Optimised: 0.0497
Hamming Loss Threshold: 0.9000
Zero One Loss Optimised: 1.0
Zero One Loss Threshold: 0.9000
Jaccard Score Optimised: 1.0
Jaccard Score Threshold: 0.9000

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: 0.0011128424281972827
train_batch_size: 20
eval_batch_size: 20
seed: 2024
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Hamming Loss	Zero One Loss	Jaccard Score	Hamming Loss Optimised	Hamming Loss Threshold	Zero One Loss Optimised	Zero One Loss Threshold	Jaccard Score Optimised	Jaccard Score Threshold
No log	1.0	160	0.1895	0.0497	1.0	1.0	0.0497	0.9000	1.0	0.9000	1.0	0.9000
No log	2.0	320	0.1859	0.0497	1.0	1.0	0.0497	0.9000	1.0	0.9000	1.0	0.9000
No log	3.0	480	0.1848	0.0497	1.0	1.0	0.0497	0.9000	1.0	0.9000	1.0	0.9000
0.1918	4.0	640	0.1843	0.0497	1.0	1.0	0.0497	0.9000	1.0	0.9000	1.0	0.9000

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu118
Datasets 3.1.0
Tokenizers 0.20.3