bert-practice-classifier – AI Model by RayenLLM | AlphaNeural AI

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RayenLLM/
bert-practice-classifier

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-practice-classifier

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: 0.7264
Accuracy: 0.375
Auc: 0.133
Precision: 0.333

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: 0.0002
train_batch_size: 8
eval_batch_size: 8
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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Auc	Precision
0.6963	1.0	4	0.7382	0.375	0.133	0.375
0.6877	2.0	8	0.7270	0.375	0.133	0.375
0.6984	3.0	12	0.7126	0.25	0.067	0.2
0.6871	4.0	16	0.7091	0.375	0.133	0.0
0.6912	5.0	20	0.7012	0.5	0.133	0.0
0.6867	6.0	24	0.7062	0.5	0.133	0.0
0.6862	7.0	28	0.7095	0.375	0.133	0.0
0.6639	8.0	32	0.7177	0.25	0.133	0.0
0.67	9.0	36	0.7239	0.125	0.133	0.0
0.6597	10.0	40	0.7264	0.375	0.133	0.333

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1