distilbert-base-uncased_fold_8_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: 1.8474
• F1: 0.8022

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	0.5398	0.7838
0.5509	2.0	578	0.6062	0.7703
0.5509	3.0	867	0.6563	0.7666
0.2366	4.0	1156	0.7688	0.7961
0.2366	5.0	1445	1.0968	0.7690
0.1247	6.0	1734	1.1414	0.7924
0.0482	7.0	2023	1.2159	0.7875
0.0482	8.0	2312	1.2703	0.7887
0.0245	9.0	2601	1.3401	0.7985
0.0245	10.0	2890	1.4645	0.7961
0.0149	11.0	3179	1.5632	0.7801
0.0149	12.0	3468	1.5249	0.7875
0.0124	13.0	3757	1.6263	0.7948
0.0038	14.0	4046	1.8059	0.7764
0.0038	15.0	4335	1.7649	0.7776
0.0061	16.0	4624	1.8293	0.7850
0.0061	17.0	4913	1.8316	0.7887
0.0022	18.0	5202	1.7628	0.7973
0.0022	19.0	5491	1.8763	0.7862
0.002	20.0	5780	1.8409	0.7899
0.0026	21.0	6069	1.8146	0.8022
0.0026	22.0	6358	1.8420	0.7973
0.0008	23.0	6647	1.8683	0.8010
0.0008	24.0	6936	1.8571	0.8010
0.0015	25.0	7225	1.8474	0.8022

Framework versions

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