dashing-worm-457 – AI Model by ElMad | AlphaNeural AI

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ElMad/
dashing-worm-457

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text-classification

generated_from_trainer

distilbert/distilbert-base-cased

autotrain_compatible

endpoints_compatible

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dashing-worm-457

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

Loss: 0.1034
Hamming Loss: 0.0304
Zero One Loss: 0.595
Jaccard Score: 0.5272
Hamming Loss Optimised: 0.03
Hamming Loss Threshold: 0.4790
Zero One Loss Optimised: 0.5430
Zero One Loss Threshold: 0.2889
Jaccard Score Optimised: 0.4332
Jaccard Score Threshold: 0.2108

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: 5.379556445396376e-05
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	0.1543	0.0428	0.8712	0.8502	0.0382	0.2350	0.7625	0.2178	0.6961	0.1808
No log	2.0	320	0.1173	0.0331	0.6713	0.6165	0.0323	0.2835	0.6238	0.2913	0.4819	0.1457
No log	3.0	480	0.1033	0.0294	0.5975	0.5256	0.0284	0.3878	0.5312	0.2564	0.4126	0.1647
0.1519	4.0	640	0.0985	0.0280	0.5813	0.5075	0.0274	0.4056	0.5137	0.2769	0.4014	0.2025

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu118
Datasets 3.1.0
Tokenizers 0.20.3