DIALOGUE_one_

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: 0.2117
• Precision: 0.9762
• Recall: 0.9737
• F1: 0.9736
• Accuracy: 0.9737

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: 3e-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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.9949	0.62	30	0.4697	0.9659	0.9605	0.9603	0.9605
0.3831	1.25	60	0.1338	0.9762	0.9737	0.9736	0.9737
0.1135	1.88	90	0.1407	0.9762	0.9737	0.9736	0.9737
0.0256	2.5	120	0.1359	0.9762	0.9737	0.9736	0.9737
0.0126	3.12	150	0.1449	0.9762	0.9737	0.9736	0.9737
0.0227	3.75	180	0.1552	0.9762	0.9737	0.9736	0.9737
0.0051	4.38	210	0.1573	0.9762	0.9737	0.9736	0.9737
0.0037	5.0	240	0.1594	0.9762	0.9737	0.9736	0.9737
0.003	5.62	270	0.1626	0.9762	0.9737	0.9736	0.9737
0.0024	6.25	300	0.1645	0.9762	0.9737	0.9736	0.9737
0.0021	6.88	330	0.1737	0.9762	0.9737	0.9736	0.9737
0.0018	7.5	360	0.1759	0.9762	0.9737	0.9736	0.9737
0.0015	8.12	390	0.1774	0.9762	0.9737	0.9736	0.9737
0.0014	8.75	420	0.1801	0.9762	0.9737	0.9736	0.9737
0.0013	9.38	450	0.1837	0.9762	0.9737	0.9736	0.9737
0.0011	10.0	480	0.1852	0.9762	0.9737	0.9736	0.9737
0.001	10.62	510	0.1878	0.9762	0.9737	0.9736	0.9737
0.0009	11.25	540	0.1892	0.9762	0.9737	0.9736	0.9737
0.0009	11.88	570	0.1939	0.9762	0.9737	0.9736	0.9737
0.0008	12.5	600	0.1948	0.9762	0.9737	0.9736	0.9737
0.0008	13.12	630	0.1961	0.9762	0.9737	0.9736	0.9737
0.0007	13.75	660	0.1965	0.9762	0.9737	0.9736	0.9737
0.0007	14.38	690	0.1972	0.9762	0.9737	0.9736	0.9737
0.0006	15.0	720	0.1988	0.9762	0.9737	0.9736	0.9737
0.0006	15.62	750	0.1993	0.9762	0.9737	0.9736	0.9737
0.0005	16.25	780	0.2006	0.9762	0.9737	0.9736	0.9737
0.0005	16.88	810	0.2020	0.9762	0.9737	0.9736	0.9737
0.0005	17.5	840	0.2031	0.9762	0.9737	0.9736	0.9737
0.0005	18.12	870	0.2045	0.9762	0.9737	0.9736	0.9737
0.0005	18.75	900	0.2054	0.9762	0.9737	0.9736	0.9737
0.0004	19.38	930	0.2051	0.9762	0.9737	0.9736	0.9737
0.0004	20.0	960	0.2053	0.9762	0.9737	0.9736	0.9737
0.0004	20.62	990	0.2058	0.9762	0.9737	0.9736	0.9737
0.0004	21.25	1020	0.2069	0.9762	0.9737	0.9736	0.9737
0.0004	21.88	1050	0.2076	0.9762	0.9737	0.9736	0.9737
0.0004	22.5	1080	0.2079	0.9762	0.9737	0.9736	0.9737
0.0004	23.12	1110	0.2084	0.9762	0.9737	0.9736	0.9737
0.0004	23.75	1140	0.2092	0.9762	0.9737	0.9736	0.9737
0.0004	24.38	1170	0.2095	0.9762	0.9737	0.9736	0.9737
0.0004	25.0	1200	0.2100	0.9762	0.9737	0.9736	0.9737
0.0004	25.62	1230	0.2104	0.9762	0.9737	0.9736	0.9737
0.0003	26.25	1260	0.2109	0.9762	0.9737	0.9736	0.9737
0.0003	26.88	1290	0.2111	0.9762	0.9737	0.9736	0.9737
0.0003	27.5	1320	0.2113	0.9762	0.9737	0.9736	0.9737
0.0003	28.12	1350	0.2115	0.9762	0.9737	0.9736	0.9737
0.0003	28.75	1380	0.2116	0.9762	0.9737	0.9736	0.9737
0.0003	29.38	1410	0.2117	0.9762	0.9737	0.9736	0.9737
0.0004	30.0	1440	0.2117	0.9762	0.9737	0.9736	0.9737

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0