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