clean-chimp-516 – AI Model by ElMad | AlphaNeural AI

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ElMad/
clean-chimp-516

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text-classification

generated_from_trainer

answerdotai/ModernBERT-base

autotrain_compatible

endpoints_compatible

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clean-chimp-516

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: 0.1555
Hamming Loss: 0.0573
Zero One Loss: 0.4100
Jaccard Score: 0.3526
Hamming Loss Optimised: 0.0556
Hamming Loss Threshold: 0.5917
Zero One Loss Optimised: 0.4075
Zero One Loss Threshold: 0.5180
Jaccard Score Optimised: 0.3191
Jaccard Score Threshold: 0.2860

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: 3.651418456743375e-05
train_batch_size: 32
eval_batch_size: 32
seed: 2024
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.956179116410945,0.8750477528228764) 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	0.1691	0.0649	0.5188	0.4740	0.064	0.5111	0.4938	0.2835	0.3735	0.2151
No log	2.0	200	0.1540	0.061	0.4313	0.3716	0.0574	0.5944	0.4263	0.4263	0.3226	0.2889
No log	3.0	300	0.1555	0.0573	0.4100	0.3526	0.0556	0.5917	0.4075	0.5180	0.3191	0.2860

Framework versions

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