distilbert-base-uncased_fold_3_ternary_v1

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: 1.8908
• F1: 0.7879

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	F1
No log	1.0	289	0.5873	0.7636
0.5479	2.0	578	0.5788	0.7697
0.5479	3.0	867	0.6286	0.7770
0.2412	4.0	1156	0.8845	0.7661
0.2412	5.0	1445	0.9894	0.7818
0.1191	6.0	1734	1.0856	0.7842
0.0543	7.0	2023	1.2852	0.7830
0.0543	8.0	2312	1.4295	0.7673
0.0223	9.0	2601	1.4716	0.7806
0.0223	10.0	2890	1.6007	0.7636
0.0122	11.0	3179	1.6744	0.7673
0.0122	12.0	3468	1.6954	0.7685
0.0129	13.0	3757	1.7273	0.7733
0.0057	14.0	4046	1.7114	0.7758
0.0057	15.0	4335	1.7480	0.7733
0.0045	16.0	4624	1.8322	0.7830
0.0045	17.0	4913	1.7448	0.7830
0.0047	18.0	5202	1.8126	0.7782
0.0047	19.0	5491	1.9021	0.7673
0.0018	20.0	5780	1.9011	0.7830
0.0026	21.0	6069	1.8771	0.7806
0.0026	22.0	6358	1.8634	0.7806
0.0012	23.0	6647	1.8926	0.7830
0.0012	24.0	6936	1.8922	0.7855
0.0005	25.0	7225	1.8908	0.7879

Framework versions

• Transformers 4.21.0
• Pytorch 1.12.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1