Metrics

• loss: 1.8402
• accuracy: 0.8085
• precision: 0.7983
• recall: 0.8085
• precision_macro: 0.6608
• recall_macro: 0.6429
• macro_fpr: 0.0935
• weighted_fpr: 0.0732
• weighted_specificity: 0.8548
• macro_specificity: 0.9158
• weighted_sensitivity: 0.8085
• macro_sensitivity: 0.6429
• f1_micro: 0.8085
• f1_macro: 0.6478
• f1_weighted: 0.8018
• runtime: 131.6318
• samples_per_second: 3.4110
• steps_per_second: 0.4330

case-analysis-distilbert-base-cased

This model is a fine-tuned version of distilbert/distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.8402
• Accuracy: 0.8085
• Precision: 0.7983
• Recall: 0.8085
• Precision Macro: 0.6461
• Recall Macro: 0.6218
• Macro Fpr: 0.0984
• Weighted Fpr: 0.0771
• Weighted Specificity: 0.8479
• Macro Specificity: 0.9119
• Weighted Sensitivity: 0.7996
• Macro Sensitivity: 0.6218
• F1 Micro: 0.7996
• F1 Macro: 0.6245
• F1 Weighted: 0.7887

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: 5e-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
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	Precision Macro	Recall Macro	Macro Fpr	Weighted Fpr	Weighted Specificity	Macro Specificity	Weighted Sensitivity	Macro Sensitivity	F1 Micro	F1 Macro	F1 Weighted
No log	1.0	224	0.7001	0.7661	0.7311	0.7661	0.5791	0.5137	0.1330	0.0923	0.7614	0.8819	0.7661	0.5137	0.7661	0.5270	0.7333
No log	2.0	448	0.7388	0.7751	0.7315	0.7751	0.5585	0.5464	0.1208	0.0882	0.7908	0.8915	0.7751	0.5464	0.7751	0.5487	0.7493
0.7066	3.0	672	0.7229	0.8018	0.7605	0.8018	0.5932	0.5708	0.1076	0.0761	0.8090	0.9027	0.8018	0.5708	0.8018	0.5767	0.7760
0.7066	4.0	896	0.8331	0.8062	0.7896	0.8062	0.6675	0.6115	0.1018	0.0742	0.8218	0.9070	0.8062	0.6115	0.8062	0.6301	0.7934
0.3654	5.0	1120	1.2300	0.7684	0.7699	0.7684	0.6085	0.6131	0.1066	0.0913	0.8542	0.9056	0.7684	0.6131	0.7684	0.5896	0.7611
0.3654	6.0	1344	1.0698	0.8129	0.7940	0.8129	0.6864	0.6153	0.0957	0.0712	0.8406	0.9134	0.8129	0.6153	0.8129	0.6300	0.7972
0.2047	7.0	1568	1.3300	0.7884	0.7960	0.7884	0.6412	0.5959	0.1044	0.0821	0.8421	0.9076	0.7884	0.5959	0.7884	0.6141	0.7892
0.2047	8.0	1792	1.3870	0.8107	0.7861	0.8107	0.6467	0.6063	0.0983	0.0722	0.8318	0.9106	0.8107	0.6063	0.8107	0.6163	0.7947
0.0795	9.0	2016	1.5031	0.7951	0.7719	0.7951	0.6275	0.5969	0.1040	0.0791	0.8320	0.9068	0.7951	0.5969	0.7951	0.6036	0.7803
0.0795	10.0	2240	1.6304	0.7728	0.7796	0.7728	0.6171	0.6233	0.1060	0.0892	0.8561	0.9072	0.7728	0.6233	0.7728	0.6196	0.7759
0.0795	11.0	2464	1.6553	0.8040	0.7802	0.8040	0.6405	0.6047	0.1003	0.0751	0.8333	0.9093	0.8040	0.6047	0.8040	0.6097	0.7884
0.0309	12.0	2688	1.6668	0.7996	0.7776	0.7996	0.6247	0.6084	0.0999	0.0771	0.8431	0.9107	0.7996	0.6084	0.7996	0.6073	0.7861
0.0309	13.0	2912	1.7548	0.8040	0.7724	0.8040	0.6059	0.5847	0.1030	0.0751	0.8216	0.9064	0.8040	0.5847	0.8040	0.5912	0.7846
0.0225	14.0	3136	1.6691	0.8107	0.7736	0.8107	0.5965	0.6044	0.0974	0.0722	0.8336	0.9111	0.8107	0.6044	0.8107	0.5998	0.7909
0.0225	15.0	3360	1.8751	0.8040	0.7897	0.8040	0.6516	0.6081	0.1007	0.0751	0.8322	0.9091	0.8040	0.6081	0.8040	0.6251	0.7939
0.0048	16.0	3584	1.8402	0.8085	0.7983	0.8085	0.6608	0.6429	0.0935	0.0732	0.8548	0.9158	0.8085	0.6429	0.8085	0.6478	0.8018
0.0048	17.0	3808	1.9124	0.7951	0.7871	0.7951	0.6331	0.6237	0.1001	0.0791	0.8456	0.9102	0.7951	0.6237	0.7951	0.6250	0.7891
0.0069	18.0	4032	1.8857	0.7973	0.7794	0.7973	0.6268	0.5972	0.1048	0.0781	0.8240	0.9053	0.7973	0.5972	0.7973	0.6062	0.7847
0.0069	19.0	4256	1.9492	0.8062	0.7813	0.8062	0.6467	0.6015	0.1006	0.0742	0.8281	0.9086	0.8062	0.6015	0.8062	0.6107	0.7895
0.0069	20.0	4480	1.8994	0.8085	0.7849	0.8085	0.6417	0.6067	0.0988	0.0732	0.8322	0.9102	0.8085	0.6067	0.8085	0.6144	0.7932
0.0034	21.0	4704	1.9819	0.8040	0.7898	0.8040	0.6748	0.6325	0.0976	0.0751	0.8439	0.9120	0.8040	0.6325	0.8040	0.6429	0.7942
0.0034	22.0	4928	2.0181	0.8062	0.7880	0.8062	0.6736	0.6204	0.0977	0.0742	0.8408	0.9118	0.8062	0.6204	0.8062	0.6293	0.7930
0.0001	23.0	5152	2.0305	0.8062	0.7880	0.8062	0.6736	0.6204	0.0977	0.0742	0.8408	0.9118	0.8062	0.6204	0.8062	0.6293	0.7930
0.0001	24.0	5376	2.0249	0.8040	0.7801	0.8040	0.6448	0.6004	0.1019	0.0751	0.8256	0.9074	0.8040	0.6004	0.8040	0.6092	0.7877
0.0	25.0	5600	2.0139	0.8018	0.7848	0.8018	0.6514	0.6226	0.0984	0.0761	0.8438	0.9114	0.8018	0.6226	0.8018	0.6272	0.7908
0.0	26.0	5824	2.0075	0.8040	0.7868	0.8040	0.6586	0.6281	0.0961	0.0751	0.8487	0.9132	0.8040	0.6281	0.8040	0.6305	0.7926
0.0026	27.0	6048	2.0155	0.8040	0.7868	0.8040	0.6586	0.6281	0.0961	0.0751	0.8487	0.9132	0.8040	0.6281	0.8040	0.6305	0.7926
0.0026	28.0	6272	2.0191	0.8040	0.7865	0.8040	0.6586	0.6237	0.0970	0.0751	0.8463	0.9126	0.8040	0.6237	0.8040	0.6283	0.7923
0.0026	29.0	6496	2.0225	0.8040	0.7865	0.8040	0.6586	0.6237	0.0970	0.0751	0.8463	0.9126	0.8040	0.6237	0.8040	0.6283	0.7923
0.0	30.0	6720	2.0343	0.7996	0.7821	0.7996	0.6461	0.6218	0.0984	0.0771	0.8479	0.9119	0.7996	0.6218	0.7996	0.6245	0.7887

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1