honorable-shrew-498 – AI Model by ElMad | AlphaNeural AI

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ElMad/
honorable-shrew-498

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text-classification

generated_from_trainer

answerdotai/ModernBERT-base

autotrain_compatible

endpoints_compatible

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honorable-shrew-498

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: 0.1625
Hamming Loss: 0.055
Zero One Loss: 0.3725
Jaccard Score: 0.3189
Hamming Loss Optimised: 0.0541
Hamming Loss Threshold: 0.7220
Zero One Loss Optimised: 0.3712
Zero One Loss Threshold: 0.4879
Jaccard Score Optimised: 0.2997
Jaccard Score Threshold: 0.2962

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: 8.935925952705373e-05
train_batch_size: 32
eval_batch_size: 32
seed: 2024
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.8778562665922138,0.8720892866629456) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

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	0.1595	0.0571	0.4600	0.4159	0.0559	0.4890	0.4225	0.3826	0.3403	0.2310
No log	2.0	200	0.1476	0.0564	0.3975	0.3373	0.0551	0.5746	0.3875	0.4361	0.2977	0.2865
No log	3.0	300	0.1625	0.055	0.3725	0.3189	0.0541	0.7220	0.3712	0.4879	0.2997	0.2962

Framework versions

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