finetuned_distilbert_model – AI Model by Manyee101 | AlphaNeural AI

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Manyee101/
finetuned_distilbert_model

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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finetuned_distilbert_model

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4359
Accuracy: 0.9446
Precision: 0.9448
Recall: 0.9446
F1: 0.9446

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.1902	1.0	3750	0.1754	0.9404	0.9414	0.9404	0.9406
0.1335	2.0	7500	0.1706	0.9468	0.9474	0.9468	0.9469
0.0897	3.0	11250	0.2055	0.9438	0.9452	0.9438	0.9437
0.0657	4.0	15000	0.2246	0.9470	0.9470	0.9470	0.9470
0.0507	5.0	18750	0.2857	0.9414	0.9417	0.9414	0.9414
0.033	6.0	22500	0.3249	0.9424	0.9428	0.9424	0.9425
0.0195	7.0	26250	0.3743	0.9413	0.9416	0.9413	0.9414
0.0108	8.0	30000	0.4019	0.9446	0.9448	0.9446	0.9446
0.0085	9.0	33750	0.4098	0.945	0.9452	0.9450	0.9451
0.0061	10.0	37500	0.4359	0.9446	0.9448	0.9446	0.9446

Framework versions

Transformers 4.26.1
Pytorch 2.6.0+cu118
Datasets 3.5.0
Tokenizers 0.13.3