distilbert-base-uncased_fold_9_binary_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: 1.6965
• F1: 0.8090

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	291	0.4193	0.7989
0.3993	2.0	582	0.4039	0.8026
0.3993	3.0	873	0.5227	0.7995
0.2044	4.0	1164	0.7264	0.8011
0.2044	5.0	1455	0.8497	0.8007
0.0882	6.0	1746	0.9543	0.8055
0.0374	7.0	2037	1.1349	0.7997
0.0374	8.0	2328	1.3175	0.8009
0.0151	9.0	2619	1.3585	0.8030
0.0151	10.0	2910	1.4202	0.8067
0.0068	11.0	3201	1.4364	0.8108
0.0068	12.0	3492	1.4443	0.8088
0.0096	13.0	3783	1.5308	0.8075
0.0031	14.0	4074	1.5061	0.8020
0.0031	15.0	4365	1.5769	0.7980
0.0048	16.0	4656	1.5962	0.8038
0.0048	17.0	4947	1.5383	0.8085
0.0067	18.0	5238	1.5456	0.8158
0.0062	19.0	5529	1.6325	0.8044
0.0062	20.0	5820	1.5430	0.8141
0.0029	21.0	6111	1.6590	0.8117
0.0029	22.0	6402	1.6650	0.8112
0.0017	23.0	6693	1.7016	0.8053
0.0017	24.0	6984	1.6998	0.8090
0.0011	25.0	7275	1.6965	0.8090

Framework versions

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