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asun17904/
multiberts-seed_2-step_2000k_winobias_classifieronly

Name: multiberts-seed_2-step_2000k_winobias_classifieronly
Availability: InStock

generated_from_trainer

apache-2.0

autotrain_compatible

endpoints_compatible

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multiberts-seed_2-step_2000k_winobias_classifieronly

This model is a fine-tuned version of google/multiberts-seed_2-step_2000k on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6937
Accuracy: 0.4943
Tp: 0.1629
Tn: 0.3314
Fp: 0.1686
Fn: 0.3371

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Tp	Tn	Fp	Fn
0.7201	0.8	20	0.7046	0.5044	0.0884	0.4160	0.0840	0.4116
0.7141	1.6	40	0.6997	0.5025	0.2001	0.3024	0.1976	0.2999
0.7113	2.4	60	0.6979	0.5063	0.2936	0.2128	0.2872	0.2064
0.7073	3.2	80	0.6967	0.5038	0.1225	0.3813	0.1187	0.3775
0.6922	4.0	100	0.6950	0.5019	0.1616	0.3403	0.1597	0.3384
0.7025	4.8	120	0.6953	0.5076	0.1313	0.3763	0.1237	0.3687
0.7029	5.6	140	0.6947	0.5019	0.2986	0.2033	0.2967	0.2014
0.6974	6.4	160	0.6952	0.5038	0.1092	0.3946	0.1054	0.3908
0.6992	7.2	180	0.6948	0.5088	0.1275	0.3813	0.1187	0.3725
0.6944	8.0	200	0.6939	0.4956	0.2557	0.2399	0.2601	0.2443
0.6953	8.8	220	0.6940	0.4912	0.1824	0.3087	0.1913	0.3176
0.6994	9.6	240	0.6942	0.4949	0.1503	0.3447	0.1553	0.3497
0.6955	10.4	260	0.6939	0.4949	0.2405	0.2544	0.2456	0.2595
0.6993	11.2	280	0.6942	0.5006	0.1446	0.3561	0.1439	0.3554
0.6925	12.0	300	0.6940	0.4975	0.1616	0.3359	0.1641	0.3384
0.6985	12.8	320	0.6938	0.4905	0.2008	0.2898	0.2102	0.2992
0.7014	13.6	340	0.6951	0.5051	0.0821	0.4230	0.0770	0.4179
0.6947	14.4	360	0.6939	0.4912	0.3150	0.1761	0.3239	0.1850
0.698	15.2	380	0.6940	0.5006	0.1654	0.3352	0.1648	0.3346
0.6912	16.0	400	0.6946	0.5032	0.1073	0.3958	0.1042	0.3927
0.6929	16.8	420	0.6946	0.5	0.1035	0.3965	0.1035	0.3965
0.6946	17.6	440	0.6938	0.4994	0.1951	0.3043	0.1957	0.3049
0.6955	18.4	460	0.6937	0.4962	0.2481	0.2481	0.2519	0.2519
0.7	19.2	480	0.6938	0.4994	0.1894	0.3100	0.1900	0.3106
0.6947	20.0	500	0.6938	0.4994	0.2008	0.2986	0.2014	0.2992
0.6978	20.8	520	0.6937	0.4937	0.2462	0.2475	0.2525	0.2538
0.7004	21.6	540	0.6937	0.4962	0.2677	0.2285	0.2715	0.2323
0.6977	22.4	560	0.6937	0.4975	0.2620	0.2355	0.2645	0.2380
0.6933	23.2	580	0.6937	0.4968	0.2342	0.2626	0.2374	0.2658
0.6991	24.0	600	0.6938	0.5	0.1824	0.3176	0.1824	0.3176
0.6961	24.8	620	0.6937	0.4987	0.2140	0.2847	0.2153	0.2860
0.7054	25.6	640	0.6944	0.5032	0.1029	0.4003	0.0997	0.3971
0.6991	26.4	660	0.6942	0.4943	0.1181	0.3763	0.1237	0.3819
0.702	27.2	680	0.6943	0.5013	0.1004	0.4009	0.0991	0.3996
0.6968	28.0	700	0.6941	0.4918	0.1206	0.3712	0.1288	0.3794
0.6939	28.8	720	0.6941	0.4912	0.1136	0.3775	0.1225	0.3864
0.6956	29.6	740	0.6936	0.5019	0.2361	0.2658	0.2342	0.2639
0.6956	30.4	760	0.6936	0.4968	0.2172	0.2797	0.2203	0.2828
0.6987	31.2	780	0.6937	0.4924	0.1679	0.3245	0.1755	0.3321
0.6935	32.0	800	0.6936	0.4912	0.1970	0.2942	0.2058	0.3030
0.6967	32.8	820	0.6936	0.4937	0.1913	0.3024	0.1976	0.3087
0.6982	33.6	840	0.6941	0.4968	0.1288	0.3681	0.1319	0.3712
0.7007	34.4	860	0.6937	0.4949	0.1824	0.3125	0.1875	0.3176
0.7	35.2	880	0.6936	0.4949	0.2197	0.2753	0.2247	0.2803
0.6904	36.0	900	0.6935	0.5025	0.2431	0.2595	0.2405	0.2569
0.6945	36.8	920	0.6936	0.4937	0.2096	0.2841	0.2159	0.2904
0.7025	37.6	940	0.6935	0.5019	0.2519	0.25	0.25	0.2481
0.6969	38.4	960	0.6935	0.4994	0.2235	0.2759	0.2241	0.2765
0.7038	39.2	980	0.6936	0.4949	0.1818	0.3131	0.1869	0.3182
0.698	40.0	1000	0.6937	0.4931	0.1736	0.3194	0.1806	0.3264
0.6973	40.8	1020	0.6938	0.5013	0.1540	0.3472	0.1528	0.3460
0.6964	41.6	1040	0.6939	0.5032	0.1408	0.3624	0.1376	0.3592
0.6999	42.4	1060	0.6939	0.5	0.1370	0.3630	0.1370	0.3630
0.7002	43.2	1080	0.6939	0.5006	0.1376	0.3630	0.1370	0.3624
0.6939	44.0	1100	0.6940	0.4956	0.1225	0.3731	0.1269	0.3775
0.6984	44.8	1120	0.6939	0.4994	0.1338	0.3655	0.1345	0.3662
0.6946	45.6	1140	0.6939	0.5019	0.1395	0.3624	0.1376	0.3605
0.6972	46.4	1160	0.6937	0.4962	0.1616	0.3346	0.1654	0.3384
0.694	47.2	1180	0.6937	0.4905	0.1679	0.3226	0.1774	0.3321
0.6974	48.0	1200	0.6937	0.4886	0.1648	0.3239	0.1761	0.3352
0.6956	48.8	1220	0.6937	0.4893	0.1648	0.3245	0.1755	0.3352
0.7032	49.6	1240	0.6937	0.4943	0.1629	0.3314	0.1686	0.3371

Framework versions

Transformers 4.26.1
Pytorch 1.13.1
Datasets 2.10.1
Tokenizers 0.13.2

asun17904/multiberts-seed_2-step_2000k_winobias_classifieronly

multiberts-seed_2-step_2000k_winobias_classifieronly

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

asun17904/
multiberts-seed_2-step_2000k_winobias_classifieronly