Modelpublic

cite-text-analysis/case-analysis-bert-base-uncased

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Model Card

Metrics

—loss: 1.7243
—accuracy: 0.7996
—precision: 0.7969
—recall: 0.7996
—precision_macro: 0.6535
—recall_macro: 0.6526
—macro_fpr: 0.0942
—weighted_fpr: 0.0771
—weighted_specificity: 0.8638
—macro_specificity: 0.9158
—weighted_sensitivity: 0.7996
—macro_sensitivity: 0.6526
—f1_micro: 0.7996
—f1_macro: 0.6529
—f1_weighted: 0.7982
—runtime: 351.9249
—samplespersecond: 1.2760
—stepspersecond: 0.1620

case-analysis-bert-base-uncased

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

—Loss: 1.7243
—Accuracy: 0.7996
—Precision: 0.7969
—Recall: 0.7996
—Precision Macro: 0.6427
—Recall Macro: 0.6184
—Macro Fpr: 0.0946
—Weighted Fpr: 0.0712
—Weighted Specificity: 0.8449
—Macro Specificity: 0.9145
—Weighted Sensitivity: 0.8129
—Macro Sensitivity: 0.6184
—F1 Micro: 0.8129
—F1 Macro: 0.6284
—F1 Weighted: 0.8035

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
—trainbatchsize: 8
—evalbatchsize: 8
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—num_epochs: 30
—mixedprecisiontraining: 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	0.7283	0.7862	0.7487	0.7862	0.5848	0.5572	0.1142	0.0831	0.8036	0.8974	0.7862	0.5572	0.7862	0.5606	0.7597
No log	2.0	448	0.8160	0.7996	0.7603	0.7996	0.5770	0.6065	0.0997	0.0771	0.8417	0.9103	0.7996	0.6065	0.7996	0.5914	0.7794
0.6512	3.0	672	0.8588	0.7906	0.7598	0.7906	0.5770	0.5989	0.1005	0.0811	0.8512	0.9105	0.7906	0.5989	0.7906	0.5840	0.7720
0.6512	4.0	896	1.0821	0.7817	0.7819	0.7817	0.6214	0.6429	0.0996	0.0851	0.8679	0.9124	0.7817	0.6429	0.7817	0.6299	0.7805
0.3466	5.0	1120	1.0612	0.8085	0.7999	0.8085	0.7129	0.6263	0.0948	0.0732	0.8470	0.9139	0.8085	0.6263	0.8085	0.6195	0.7928
0.3466	6.0	1344	1.2559	0.7929	0.7877	0.7929	0.6206	0.6362	0.0951	0.0801	0.8717	0.9161	0.7929	0.6362	0.7929	0.6273	0.7897
0.1715	7.0	1568	1.3701	0.7929	0.7889	0.7929	0.6345	0.6179	0.0991	0.0801	0.8558	0.9122	0.7929	0.6179	0.7929	0.6237	0.7893
0.1715	8.0	1792	1.4005	0.8107	0.8035	0.8107	0.6578	0.6370	0.0922	0.0722	0.8607	0.9179	0.8107	0.6370	0.8107	0.6464	0.8064
0.0636	9.0	2016	1.4737	0.8018	0.7881	0.8018	0.6583	0.6149	0.1026	0.0761	0.8271	0.9072	0.8018	0.6149	0.8018	0.6263	0.7896
0.0636	10.0	2240	1.7569	0.7884	0.7962	0.7884	0.6275	0.6428	0.0960	0.0821	0.8750	0.9158	0.7884	0.6428	0.7884	0.6332	0.7909
0.0636	11.0	2464	1.7141	0.7906	0.7824	0.7906	0.6166	0.6083	0.1035	0.0811	0.8424	0.9083	0.7906	0.6083	0.7906	0.6101	0.7845
0.0159	12.0	2688	1.7144	0.7951	0.7914	0.7951	0.6393	0.6413	0.0969	0.0791	0.8610	0.9140	0.7951	0.6413	0.7951	0.6373	0.7917
0.0159	13.0	2912	1.7243	0.7996	0.7969	0.7996	0.6535	0.6526	0.0942	0.0771	0.8638	0.9158	0.7996	0.6526	0.7996	0.6529	0.7982
0.0043	14.0	3136	1.8551	0.7973	0.7948	0.7973	0.6576	0.6189	0.1041	0.0781	0.8314	0.9072	0.7973	0.6189	0.7973	0.6335	0.7912
0.0043	15.0	3360	1.8841	0.7929	0.7869	0.7929	0.6154	0.6162	0.1008	0.0801	0.8511	0.9110	0.7929	0.6162	0.7929	0.6104	0.7861
0.0029	16.0	3584	2.0853	0.7550	0.7837	0.7550	0.6010	0.6119	0.1100	0.0976	0.8698	0.9062	0.7550	0.6119	0.7550	0.6015	0.7661
0.0029	17.0	3808	1.9722	0.7840	0.7783	0.7840	0.6018	0.5839	0.1076	0.0841	0.8394	0.9059	0.7840	0.5839	0.7840	0.5917	0.7797
0.0071	18.0	4032	1.8735	0.7996	0.7783	0.7996	0.6086	0.5917	0.1053	0.0771	0.8193	0.9047	0.7996	0.5917	0.7996	0.5960	0.7840
0.0071	19.0	4256	1.8294	0.8018	0.7840	0.8018	0.6114	0.5943	0.1025	0.0761	0.8308	0.9082	0.8018	0.5943	0.8018	0.6001	0.7895
0.0071	20.0	4480	1.8578	0.7973	0.7939	0.7973	0.6367	0.6232	0.0990	0.0781	0.8497	0.9118	0.7973	0.6232	0.7973	0.6285	0.7942
0.0049	21.0	4704	1.8770	0.7973	0.7939	0.7973	0.6367	0.6232	0.0990	0.0781	0.8497	0.9118	0.7973	0.6232	0.7973	0.6285	0.7942
0.0049	22.0	4928	1.8932	0.7951	0.7876	0.7951	0.6219	0.6119	0.1007	0.0791	0.8461	0.9103	0.7951	0.6119	0.7951	0.6155	0.7900
0.0015	23.0	5152	1.9834	0.7996	0.7965	0.7996	0.6441	0.6389	0.0960	0.0771	0.8599	0.9149	0.7996	0.6389	0.7996	0.6403	0.7971
0.0015	24.0	5376	1.9926	0.8018	0.7984	0.8018	0.6468	0.6399	0.0952	0.0761	0.8603	0.9155	0.8018	0.6399	0.8018	0.6422	0.7991
0.0001	25.0	5600	1.9771	0.7973	0.7790	0.7973	0.6025	0.6024	0.1011	0.0781	0.8420	0.9098	0.7973	0.6024	0.7973	0.6017	0.7871
0.0001	26.0	5824	1.9871	0.7951	0.7770	0.7951	0.5996	0.6015	0.1020	0.0791	0.8416	0.9092	0.7951	0.6015	0.7951	0.5997	0.7850
0.0	27.0	6048	1.8756	0.8129	0.7961	0.8129	0.6440	0.6200	0.0939	0.0712	0.8462	0.9148	0.8129	0.6200	0.8129	0.6293	0.8029
0.0	28.0	6272	1.8473	0.8151	0.7998	0.8151	0.6463	0.6194	0.0937	0.0703	0.8453	0.9151	0.8151	0.6194	0.8151	0.6305	0.8056
0.0	29.0	6496	1.8525	0.8129	0.7975	0.8129	0.6427	0.6184	0.0946	0.0712	0.8449	0.9145	0.8129	0.6184	0.8129	0.6284	0.8035
0.0001	30.0	6720	1.8540	0.8129	0.7975	0.8129	0.6427	0.6184	0.0946	0.0712	0.8449	0.9145	0.8129	0.6184	0.8129	0.6284	0.8035

Framework versions

—Transformers 4.40.1
—Pytorch 2.2.1+cu121
—Datasets 2.19.1
—Tokenizers 0.19.1