distilbert-base-uncased_fold_2_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: 1.8833
• F1: 0.7841

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	290	0.4060	0.8070
0.3981	2.0	580	0.4534	0.8072
0.3981	3.0	870	0.5460	0.7961
0.1985	4.0	1160	0.8684	0.7818
0.1985	5.0	1450	0.9009	0.7873
0.0844	6.0	1740	1.1529	0.7825
0.0329	7.0	2030	1.3185	0.7850
0.0329	8.0	2320	1.4110	0.7862
0.0109	9.0	2610	1.4751	0.7784
0.0109	10.0	2900	1.6276	0.7723
0.0071	11.0	3190	1.6779	0.7861
0.0071	12.0	3480	1.6258	0.7850
0.0041	13.0	3770	1.6324	0.7903
0.0109	14.0	4060	1.7563	0.7932
0.0109	15.0	4350	1.6740	0.7906
0.0079	16.0	4640	1.7468	0.7944
0.0079	17.0	4930	1.7095	0.7879
0.0067	18.0	5220	1.7293	0.7912
0.0021	19.0	5510	1.7875	0.7848
0.0021	20.0	5800	1.7462	0.7906
0.0026	21.0	6090	1.8549	0.7815
0.0026	22.0	6380	1.8314	0.7860
0.0021	23.0	6670	1.8577	0.7839
0.0021	24.0	6960	1.8548	0.7883
0.0001	25.0	7250	1.8833	0.7841

Framework versions

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