sensitive-query-classifier-distilbert – AI Model by Harmoniecloud | AlphaNeural AI

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Harmoniecloud/
sensitive-query-classifier-distilbert

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

text-embeddings-inference

endpoints_compatible

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sensitive-query-classifier-distilbert

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

Loss: 0.3097
Accuracy: 0.9382
F1 Weighted: 0.9382
Recall Sensitive: 0.9481
Precision Sensitive: 0.9384

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: 7e-05
train_batch_size: 8
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Weighted	Recall Sensitive	Precision Sensitive
0.3824	1.0	267	0.5317	0.8315	0.8291	0.9204	0.7988
0.3178	2.0	534	0.4726	0.8464	0.8428	0.9654	0.7949
0.1829	3.0	801	0.3666	0.9176	0.9173	0.9516	0.9016
0.0593	4.0	1068	0.2564	0.9438	0.9438	0.9585	0.9390
0.061	5.0	1335	0.3097	0.9382	0.9382	0.9481	0.9384

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0