Bert_Text_Classification_v4

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

• Loss: 0.0376
• Accuracy: 0.9964
• F1: 0.9963
• Precision: 0.9963
• Recall: 0.9963

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: 16
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 30

Training results

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