MM03

This model is a fine-tuned version of prajjwal1/bert-tiny on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3682
• Accuracy: 0.85
• F1: 0.9189

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: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.0	50	0.6923	0.53	0.3672
No log	0.01	100	0.6938	0.47	0.3005
No log	0.01	150	0.6914	0.53	0.3672
No log	0.01	200	0.6934	0.45	0.3486
No log	0.02	250	0.6900	0.53	0.3672
No log	0.02	300	0.6910	0.53	0.3672
No log	0.03	350	0.6910	0.57	0.5387
No log	0.03	400	0.6898	0.53	0.3672
No log	0.03	450	0.6943	0.47	0.3005
0.6943	0.04	500	0.7011	0.47	0.3005
0.6943	0.04	550	0.6920	0.56	0.56
0.6943	0.04	600	0.6917	0.59	0.5765
0.6943	0.05	650	0.6883	0.56	0.5252
0.6943	0.05	700	0.6938	0.49	0.4896
0.6943	0.06	750	0.6903	0.54	0.4655
0.6943	0.06	800	0.6923	0.47	0.4686
0.6943	0.06	850	0.6904	0.5	0.4992
0.6943	0.07	900	0.6835	0.6	0.5684
0.6943	0.07	950	0.6912	0.43	0.3752
0.6881	0.07	1000	0.6634	0.61	0.5974
0.6881	0.08	1050	0.6575	0.6	0.5935
0.6881	0.08	1100	0.6431	0.68	0.6803
0.6881	0.08	1150	0.6386	0.67	0.6691
0.6881	0.09	1200	0.6290	0.63	0.6279
0.6881	0.09	1250	0.6355	0.66	0.66
0.6881	0.1	1300	0.6592	0.61	0.6078
0.6881	0.1	1350	0.6443	0.66	0.6595
0.6881	0.1	1400	0.6520	0.62	0.6194
0.6881	0.11	1450	0.6636	0.58	0.5680
0.6621	0.11	1500	0.6508	0.59	0.5844
0.6621	0.11	1550	0.6373	0.61	0.6097
0.6621	0.12	1600	0.6372	0.63	0.6280
0.6621	0.12	1650	0.6499	0.6	0.5912
0.6621	0.12	1700	0.6501	0.61	0.6047
0.6621	0.13	1750	0.6390	0.62	0.6139
0.6621	0.13	1800	0.6358	0.62	0.6139
0.6621	0.14	1850	0.6245	0.65	0.6468
0.6621	0.14	1900	0.6207	0.66	0.6575
0.6621	0.14	1950	0.6185	0.69	0.6898
0.6317	0.15	2000	0.6366	0.62	0.6139
0.6317	0.15	2050	0.6155	0.64	0.6374
0.6317	0.15	2100	0.6062	0.69	0.6902
0.6317	0.16	2150	0.6040	0.66	0.6575
0.6317	0.16	2200	0.5912	0.73	0.7298
0.6317	0.17	2250	0.5861	0.72	0.7195
0.6317	0.17	2300	0.5732	0.73	0.7302
0.6317	0.17	2350	0.5695	0.74	0.7402
0.6317	0.18	2400	0.5514	0.74	0.74
0.6317	0.18	2450	0.5468	0.74	0.74
0.6194	0.18	2500	0.5528	0.71	0.7101
0.6194	0.19	2550	0.5562	0.68	0.6803
0.6194	0.19	2600	0.5624	0.69	0.6903
0.6194	0.19	2650	0.5505	0.72	0.72
0.6194	0.2	2700	0.5429	0.74	0.7389
0.6194	0.2	2750	0.5576	0.68	0.6803
0.6194	0.21	2800	0.5424	0.71	0.7098
0.6194	0.21	2850	0.5194	0.76	0.7590
0.6194	0.21	2900	0.5321	0.75	0.7493
0.6194	0.22	2950	0.5306	0.75	0.7493
0.596	0.22	3000	0.5366	0.75	0.7477
0.596	0.22	3050	0.5124	0.77	0.7679
0.596	0.23	3100	0.5152	0.75	0.7465
0.596	0.23	3150	0.5302	0.76	0.7573
0.596	0.23	3200	0.5289	0.76	0.7582
0.596	0.24	3250	0.5111	0.76	0.7573
0.596	0.24	3300	0.5204	0.75	0.7477
0.596	0.25	3350	0.5145	0.75	0.7477
0.596	0.25	3400	0.5139	0.76	0.7561
0.596	0.25	3450	0.4971	0.76	0.7561
0.5746	0.26	3500	0.5048	0.74	0.7381
0.5746	0.26	3550	0.5224	0.76	0.7590
0.5746	0.26	3600	0.5077	0.76	0.76
0.5746	0.27	3650	0.4803	0.79	0.7899
0.5746	0.27	3700	0.4753	0.78	0.7802
0.5746	0.28	3750	0.4684	0.79	0.7901
0.5746	0.28	3800	0.4853	0.76	0.76
0.5746	0.28	3850	0.5012	0.76	0.7602
0.5746	0.29	3900	0.4905	0.77	0.7698
0.5746	0.29	3950	0.4731	0.79	0.7899
0.5246	0.29	4000	0.4616	0.8	0.7992
0.5246	0.3	4050	0.4875	0.79	0.7902
0.5246	0.3	4100	0.4560	0.78	0.7791
0.5246	0.3	4150	0.4696	0.77	0.7694
0.5246	0.31	4200	0.4457	0.78	0.7802
0.5246	0.31	4250	0.4743	0.76	0.7596

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.0
• Tokenizers 0.15.0