bert-large-uncased_stereoset_classifieronly

This model is a fine-tuned version of bert-large-uncased on the stereoset dataset. It achieves the following results on the evaluation set:

• Loss: 0.6856
• Accuracy: 0.5479
• Tp: 0.3579
• Tn: 0.1900
• Fp: 0.3242
• Fn: 0.1279

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Tp	Tn	Fp	Fn
0.6938	0.43	20	0.6891	0.5573	0.2630	0.2943	0.2198	0.2229
0.703	0.85	40	0.6942	0.5455	0.3901	0.1554	0.3587	0.0958
0.6929	1.28	60	0.6882	0.5510	0.3069	0.2441	0.2700	0.1790
0.7044	1.7	80	0.6895	0.5471	0.3603	0.1868	0.3273	0.1256
0.6947	2.13	100	0.6874	0.5463	0.3006	0.2457	0.2684	0.1852
0.7115	2.55	120	0.6910	0.5479	0.3768	0.1711	0.3430	0.1091
0.7016	2.98	140	0.6879	0.5518	0.3430	0.2088	0.3053	0.1429
0.6952	3.4	160	0.6918	0.5361	0.3980	0.1381	0.3760	0.0879
0.6959	3.83	180	0.6910	0.5463	0.3878	0.1586	0.3556	0.0981
0.6924	4.26	200	0.6906	0.5471	0.3830	0.1641	0.3501	0.1028
0.6943	4.68	220	0.6877	0.5487	0.3195	0.2292	0.2849	0.1664
0.7052	5.11	240	0.6879	0.5644	0.2473	0.3171	0.1970	0.2386
0.6881	5.53	260	0.6889	0.5479	0.3791	0.1688	0.3454	0.1068
0.6971	5.96	280	0.6882	0.5463	0.3752	0.1711	0.3430	0.1107
0.6781	6.38	300	0.6930	0.5330	0.4035	0.1295	0.3846	0.0824
0.6992	6.81	320	0.6875	0.5495	0.3579	0.1915	0.3226	0.1279
0.6954	7.23	340	0.6868	0.5557	0.3195	0.2363	0.2779	0.1664
0.6949	7.66	360	0.6877	0.5479	0.3556	0.1923	0.3218	0.1303
0.6946	8.09	380	0.6899	0.5471	0.3878	0.1593	0.3548	0.0981
0.6877	8.51	400	0.6862	0.5542	0.3218	0.2323	0.2818	0.1641
0.6994	8.94	420	0.6890	0.5479	0.3823	0.1656	0.3485	0.1036
0.7061	9.36	440	0.6867	0.5620	0.2347	0.3273	0.1868	0.2512
0.6945	9.79	460	0.6893	0.5479	0.3878	0.1601	0.3540	0.0981
0.7078	10.21	480	0.6908	0.5353	0.3972	0.1381	0.3760	0.0887
0.6911	10.64	500	0.6858	0.5502	0.3108	0.2394	0.2747	0.1750
0.684	11.06	520	0.6875	0.5502	0.3768	0.1735	0.3407	0.1091
0.6925	11.49	540	0.6906	0.5369	0.3972	0.1397	0.3744	0.0887
0.7104	11.91	560	0.6856	0.5597	0.2527	0.3069	0.2072	0.2331
0.6919	12.34	580	0.6857	0.5479	0.3391	0.2088	0.3053	0.1468
0.6873	12.77	600	0.6903	0.5338	0.3987	0.1350	0.3791	0.0871
0.6915	13.19	620	0.6862	0.5471	0.3540	0.1931	0.3210	0.1319
0.6921	13.62	640	0.6859	0.5518	0.3485	0.2033	0.3108	0.1374
0.7092	14.04	660	0.6888	0.5479	0.3807	0.1672	0.3469	0.1052
0.6874	14.47	680	0.6851	0.5518	0.3210	0.2308	0.2834	0.1648
0.682	14.89	700	0.6877	0.5510	0.3744	0.1766	0.3375	0.1115
0.6953	15.32	720	0.6853	0.5526	0.3273	0.2253	0.2889	0.1586
0.7056	15.74	740	0.6882	0.5487	0.3885	0.1601	0.3540	0.0973
0.6776	16.17	760	0.6875	0.5471	0.3783	0.1688	0.3454	0.1075
0.6862	16.6	780	0.6863	0.5510	0.3642	0.1868	0.3273	0.1217
0.6827	17.02	800	0.6868	0.5510	0.3705	0.1805	0.3336	0.1154
0.7161	17.45	820	0.6878	0.5502	0.3791	0.1711	0.3430	0.1068
0.6991	17.87	840	0.6852	0.5487	0.3359	0.2127	0.3014	0.1499
0.6836	18.3	860	0.6876	0.5487	0.3830	0.1656	0.3485	0.1028
0.7023	18.72	880	0.6862	0.5487	0.3595	0.1892	0.3250	0.1264
0.6939	19.15	900	0.6854	0.5495	0.3485	0.2009	0.3132	0.1374
0.6883	19.57	920	0.6860	0.5479	0.3587	0.1892	0.3250	0.1272
0.6872	20.0	940	0.6866	0.5518	0.3697	0.1821	0.3320	0.1162
0.685	20.43	960	0.6861	0.5487	0.3595	0.1892	0.3250	0.1264
0.6771	20.85	980	0.6853	0.5510	0.3477	0.2033	0.3108	0.1381
0.6904	21.28	1000	0.6859	0.5487	0.3564	0.1923	0.3218	0.1295
0.6925	21.7	1020	0.6848	0.5518	0.3132	0.2386	0.2755	0.1727
0.6982	22.13	1040	0.6856	0.5463	0.3532	0.1931	0.3210	0.1327
0.7015	22.55	1060	0.6859	0.5479	0.3587	0.1892	0.3250	0.1272
0.6851	22.98	1080	0.6860	0.5518	0.3650	0.1868	0.3273	0.1209
0.6875	23.4	1100	0.6856	0.5463	0.3532	0.1931	0.3210	0.1327
0.7035	23.83	1120	0.6851	0.5510	0.3454	0.2057	0.3085	0.1405
0.699	24.26	1140	0.6846	0.5534	0.3281	0.2253	0.2889	0.1578
0.6954	24.68	1160	0.6851	0.5495	0.3485	0.2009	0.3132	0.1374
0.6881	25.11	1180	0.6851	0.5510	0.3485	0.2025	0.3116	0.1374
0.6931	25.53	1200	0.6862	0.5487	0.3666	0.1821	0.3320	0.1193
0.6967	25.96	1220	0.6868	0.5487	0.3752	0.1735	0.3407	0.1107
0.6826	26.38	1240	0.6863	0.5502	0.3689	0.1813	0.3328	0.1170
0.6927	26.81	1260	0.6857	0.5487	0.3587	0.1900	0.3242	0.1272
0.692	27.23	1280	0.6853	0.5471	0.3524	0.1947	0.3195	0.1334
0.6936	27.66	1300	0.6856	0.5479	0.3579	0.1900	0.3242	0.1279
0.6871	28.09	1320	0.6856	0.5487	0.3579	0.1907	0.3234	0.1279
0.6956	28.51	1340	0.6857	0.5487	0.3595	0.1892	0.3250	0.1264
0.6788	28.94	1360	0.6859	0.5479	0.3611	0.1868	0.3273	0.1248
0.6933	29.36	1380	0.6856	0.5479	0.3579	0.1900	0.3242	0.1279
0.6909	29.79	1400	0.6856	0.5479	0.3579	0.1900	0.3242	0.1279

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1
• Datasets 2.10.1
• Tokenizers 0.13.2