This model is a fine-tuned version of distilbert-base-uncased on an tinutmap/ai-categories-data 's train dataset. It achieves the following results on the evaluation set:

Loss: 0.0037
Accuracy: 0.9991
F1: 0.9926
Precision: 0.9926
Recall: 0.9926

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.1646	1.0	599	0.0271	0.9956	0.9620	0.9834	0.9415
0.0219	2.0	1198	0.0093	0.9984	0.9862	0.9887	0.9838
0.0093	3.0	1797	0.0062	0.9989	0.9907	0.9905	0.9908
0.0054	4.0	2396	0.0048	0.9991	0.9924	0.9919	0.9929
0.0034	5.0	2995	0.0043	0.9991	0.9928	0.9912	0.9944
0.0021	6.0	3594	0.0038	0.9992	0.9931	0.9923	0.9940
0.0017	7.0	4193	0.0038	0.9991	0.9928	0.9933	0.9922
0.0015	8.0	4792	0.0037	0.9991	0.9926	0.9926	0.9926

Framework versions

Transformers 4.52.2
Pytorch 2.7.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1

tinutmap/ai-categories-text

ai-categories-text

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tinutmap/
ai-categories-text