victorious-moose-736 – AI Model by ElMad | AlphaNeural AI

You can deploy this model and start earning money today!

ElMad/
victorious-moose-736

like

0

text-classification

generated_from_trainer

answerdotai/ModernBERT-base

autotrain_compatible

endpoints_compatible

Views

No views yet

victorious-moose-736

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

Loss: 0.1689
Hamming Loss: 0.0619
Zero One Loss: 0.4450
Jaccard Score: 0.3857
Hamming Loss Optimised: 0.0592
Hamming Loss Threshold: 0.7299
Zero One Loss Optimised: 0.4387
Zero One Loss Threshold: 0.4119
Jaccard Score Optimised: 0.3464
Jaccard Score Threshold: 0.2462

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: 2.115467719563917e-05
train_batch_size: 32
eval_batch_size: 32
seed: 2024
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.8101446041573426,0.9056914031952074) and epsilon=1e-07 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	100	0.1830	0.0644	0.5162	0.4740	0.0643	0.4982	0.4975	0.3276	0.3889	0.2962
No log	2.0	200	0.1643	0.062	0.4625	0.3920	0.0597	0.6455	0.4587	0.4816	0.3518	0.2755
No log	3.0	300	0.1678	0.0636	0.4550	0.3934	0.0595	0.7017	0.4425	0.3683	0.3441	0.2888
No log	4.0	400	0.1689	0.0619	0.4450	0.3857	0.0592	0.7299	0.4387	0.4119	0.3464	0.2462

Framework versions

Transformers 4.48.0.dev0
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.21.0