Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0043	0.36	50	0.0399	0.9955	0.9953	0.9954	0.9951
0.0001	0.72	100	0.0226	0.9964	0.9961	0.9962	0.9961
0.0193	1.09	150	0.0668	0.9900	0.9893	0.9905	0.9884
0.0555	1.45	200	0.0504	0.9927	0.9927	0.9934	0.9922
0.0465	1.81	250	0.0017	0.9991	0.9990	0.9990	0.9991
0.048	2.17	300	0.0348	0.9936	0.9934	0.9937	0.9932
0.0513	2.54	350	0.0699	0.9873	0.9870	0.9878	0.9865
0.0213	2.9	400	0.0495	0.9927	0.9926	0.9925	0.9928
0.0427	3.26	450	0.0587	0.9936	0.9933	0.9939	0.9928
0.0097	3.62	500	0.0236	0.9964	0.9961	0.9963	0.9959
0.0001	3.99	550	0.0279	0.9964	0.9962	0.9964	0.9959
0.0001	4.35	600	0.0259	0.9973	0.9972	0.9975	0.9968
0.0	4.71	650	0.0260	0.9973	0.9972	0.9975	0.9968
0.0091	5.07	700	0.0216	0.9964	0.9962	0.9964	0.9959
0.0014	5.43	750	0.0268	0.9973	0.9972	0.9971	0.9972
0.0	5.8	800	0.0383	0.9955	0.9952	0.9957	0.9947
0.0	6.16	850	0.0362	0.9964	0.9962	0.9966	0.9958
0.0003	6.52	900	0.0956	0.9909	0.9904	0.9900	0.9910
0.0247	6.88	950	0.0285	0.9973	0.9972	0.9975	0.9968
0.0003	7.25	1000	0.0333	0.9964	0.9962	0.9967	0.9958
0.0001	7.61	1050	0.0334	0.9964	0.9962	0.9967	0.9958
0.0003	7.97	1100	0.0285	0.9973	0.9972	0.9971	0.9972
0.0001	8.33	1150	0.0294	0.9964	0.9962	0.9962	0.9962
0.0	8.7	1200	0.0298	0.9964	0.9962	0.9962	0.9962
0.0045	9.06	1250	0.0376	0.9955	0.9953	0.9954	0.9951
0.0004	9.42	1300	0.0450	0.9946	0.9943	0.9943	0.9942
0.0322	9.78	1350	0.0492	0.9936	0.9932	0.9939	0.9926
0.003	10.14	1400	0.0110	0.9991	0.9991	0.9992	0.9989
0.0001	10.51	1450	0.0112	0.9991	0.9991	0.9992	0.9989
0.0001	10.87	1500	0.0124	0.9982	0.9981	0.9981	0.9980
0.0	11.23	1550	0.0112	0.9982	0.9981	0.9981	0.9980
0.0	11.59	1600	0.0111	0.9991	0.9991	0.9992	0.9989
0.0	11.96	1650	0.0110	0.9991	0.9991	0.9992	0.9989
0.0	12.32	1700	0.0110	0.9991	0.9991	0.9992	0.9989
0.0	12.68	1750	0.0109	0.9991	0.9991	0.9992	0.9989
0.0	13.04	1800	0.0109	0.9991	0.9990	0.9991	0.9989
0.0	13.41	1850	0.0109	0.9991	0.9990	0.9991	0.9989
0.0	13.77	1900	0.0109	0.9991	0.9990	0.9991	0.9989
0.0	14.13	1950	0.0109	0.9991	0.9990	0.9991	0.9989
0.0	14.49	2000	0.0109	0.9991	0.9990	0.9991	0.9989
0.0	14.86	2050	0.0109	0.9991	0.9990	0.9991	0.9989
0.0	15.22	2100	0.0109	0.9991	0.9990	0.9991	0.9989
0.0	15.58	2150	0.0110	0.9991	0.9990	0.9991	0.9989
0.0	15.94	2200	0.0110	0.9991	0.9990	0.9991	0.9989
0.0	16.3	2250	0.0110	0.9991	0.9990	0.9991	0.9989
0.0	16.67	2300	0.0111	0.9991	0.9990	0.9991	0.9989
0.0	17.03	2350	0.0111	0.9991	0.9990	0.9991	0.9989
0.0	17.39	2400	0.0111	0.9991	0.9990	0.9991	0.9989
0.0	17.75	2450	0.0112	0.9991	0.9990	0.9991	0.9989
0.0	18.12	2500	0.0112	0.9991	0.9990	0.9991	0.9989
0.0	18.48	2550	0.0112	0.9991	0.9990	0.9991	0.9989
0.0099	18.84	2600	0.0175	0.9973	0.9973	0.9973	0.9973
0.0	19.2	2650	0.0133	0.9982	0.9981	0.9983	0.9979
0.0	19.57	2700	0.0135	0.9982	0.9981	0.9983	0.9979
0.0	19.93	2750	0.0135	0.9982	0.9981	0.9983	0.9979
0.0	20.29	2800	0.0135	0.9982	0.9981	0.9983	0.9979
0.0	20.65	2850	0.0132	0.9982	0.9981	0.9983	0.9979
0.0	21.01	2900	0.0133	0.9982	0.9981	0.9983	0.9979
0.0	21.38	2950	0.0133	0.9982	0.9981	0.9983	0.9979
0.0	21.74	3000	0.0124	0.9982	0.9981	0.9981	0.9980
0.0	22.1	3050	0.0125	0.9982	0.9981	0.9981	0.9980
0.0	22.46	3100	0.0125	0.9982	0.9981	0.9981	0.9980
0.0	22.83	3150	0.0125	0.9982	0.9981	0.9981	0.9980
0.0	23.19	3200	0.0125	0.9982	0.9981	0.9981	0.9980
0.0	23.55	3250	0.0126	0.9982	0.9981	0.9981	0.9980
0.0	23.91	3300	0.0126	0.9982	0.9981	0.9981	0.9980
0.0	24.28	3350	0.0126	0.9982	0.9981	0.9981	0.9980
0.0	24.64	3400	0.0126	0.9982	0.9981	0.9981	0.9980
0.0	25.0	3450	0.0126	0.9982	0.9981	0.9981	0.9980
0.0	25.36	3500	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	25.72	3550	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	26.09	3600	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	26.45	3650	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	26.81	3700	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	27.17	3750	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	27.54	3800	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	27.9	3850	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	28.26	3900	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	28.62	3950	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	28.99	4000	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	29.35	4050	0.0127	0.9982	0.9981	0.9981	0.9980
0.0	29.71	4100	0.0127	0.9982	0.9981	0.9981	0.9980

Framework versions

Transformers 4.37.2
Pytorch 2.3.0+cu121
Tokenizers 0.15.2

oumaymaMb/Bert_Text_Classification_v4

Bert_Text_Classification_v4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

oumaymaMb/
Bert_Text_Classification_v4