distilbert – AI Model by KasuleTrevor | AlphaNeural AI

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KasuleTrevor/
distilbert

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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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: 0.1400
Accuracy: 0.973
Precision: 0.974
Recall: 0.973
F1: 0.973

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: 0.0001
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
2.7987	1.0	114	2.0513	0.527	0.53	0.527	0.449
1.3683	2.0	228	0.4828	0.955	0.959	0.955	0.955
0.4676	3.0	342	0.2051	0.949	0.936	0.949	0.94
0.2473	4.0	456	0.1503	0.971	0.973	0.971	0.971
0.1912	5.0	570	0.1231	0.973	0.974	0.973	0.973
0.1413	6.0	684	0.1538	0.971	0.972	0.971	0.971
0.1289	7.0	798	0.1197	0.976	0.977	0.976	0.976
0.0951	8.0	912	0.1246	0.978	0.979	0.978	0.978
0.0686	9.0	1026	0.1397	0.973	0.974	0.973	0.973
0.0518	10.0	1140	0.1400	0.973	0.974	0.973	0.973

Framework versions

Transformers 4.43.3
Pytorch 2.1.0+cu118
Datasets 2.20.0
Tokenizers 0.19.1