distilbert-training-1

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: 0.0337
• Accuracy: 0.9940
• Precision: 1.0
• Recall: 0.9875
• F1: 0.9937

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	0.25	85	0.0878	0.9819	0.9787	0.9840	0.9813
No log	0.5	170	0.0763	0.9819	1.0	0.9626	0.9809
No log	0.75	255	0.0487	0.9880	0.9841	0.9911	0.9876
0.088	1.0	340	0.0411	0.9931	1.0	0.9857	0.9928
0.088	1.25	425	0.0417	0.9914	0.9964	0.9857	0.9910
0.088	1.5	510	0.0423	0.9923	0.9946	0.9893	0.9920
0.088	1.76	595	0.0404	0.9931	1.0	0.9857	0.9928
0.0325	2.01	680	0.0459	0.9931	1.0	0.9857	0.9928
0.0325	2.26	765	0.0336	0.9940	1.0	0.9875	0.9937
0.0325	2.51	850	0.0358	0.9931	1.0	0.9857	0.9928
0.0325	2.76	935	0.0413	0.9931	1.0	0.9857	0.9928
0.0236	3.01	1020	0.0423	0.9931	1.0	0.9857	0.9928
0.0236	3.26	1105	0.0399	0.9940	1.0	0.9875	0.9937
0.0236	3.51	1190	0.0380	0.9940	1.0	0.9875	0.9937
0.0236	3.76	1275	0.0357	0.9940	1.0	0.9875	0.9937
0.0222	4.01	1360	0.0364	0.9940	1.0	0.9875	0.9937
0.0222	4.26	1445	0.0351	0.9940	1.0	0.9875	0.9937
0.0222	4.51	1530	0.0329	0.9940	1.0	0.9875	0.9937
0.0222	4.76	1615	0.0337	0.9940	1.0	0.9875	0.9937

Framework versions

• Transformers 4.33.1
• Pytorch 2.2.0.dev20230913+cu121
• Tokenizers 0.13.3