case-analysis-distilbert-base-cased

cite-text-analysis/case-analysis-distilbert-base-cased

case-analysis-distilbert-base-cased is a text classification model from cite-text-analysis. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

- loss: 1.8402 - accuracy: 0.8085 - precision: 0.7983 - recall: 0.8085 - precisionmacro: 0.6608 - recallmacro: 0.6429 - macrofpr: 0.0935 - weightedfpr: 0.0732 - weightedspecificity: 0.8548 - macrospecificity: 0.9158 -…

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: May 10, 2024
Updated: May 10, 2024
SHA: 977f5703

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Base models

distilbert/distilbert-base-cased

Model card

From the Hugging Face model README

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

Task: Text Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: May 10, 2024
Updated: May 10, 2024