Gayathri142214002/t5_qg_2 - AI Model Zoo

t5_qg_2

This model is a fine-tuned version of Gayathri142214002/t5_qg_1 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3823

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: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss
0.8817	0.03	10	0.8651
0.8338	0.06	20	0.8145
0.9069	0.09	30	0.7292
0.8168	0.12	40	0.6823
0.6639	0.15	50	0.6587
0.6897	0.18	60	0.6266
0.6563	0.21	70	0.6206
0.7409	0.24	80	0.5953
0.7765	0.27	90	0.5762
0.6625	0.3	100	0.5683
0.5672	0.33	110	0.5687
0.5073	0.36	120	0.5722
0.5656	0.39	130	0.5583
0.5908	0.42	140	0.5357
0.6982	0.45	150	0.5139
0.5616	0.48	160	0.4979
0.6185	0.51	170	0.4837
0.5247	0.54	180	0.4845
0.5561	0.57	190	0.4848
0.5342	0.6	200	0.4802
0.4285	0.63	210	0.4893
0.4691	0.66	220	0.4944
0.483	0.69	230	0.4913
0.607	0.72	240	0.4877
0.5613	0.75	250	0.4692
0.5271	0.78	260	0.4566
0.4606	0.81	270	0.4692
0.52	0.84	280	0.4777
0.4936	0.87	290	0.4673
0.5076	0.9	300	0.4637
0.4497	0.93	310	0.4702
0.3881	0.96	320	0.4821
0.4673	0.99	330	0.4641
0.4199	1.02	340	0.4639
0.4399	1.05	350	0.4751
0.4949	1.08	360	0.4808
0.4312	1.11	370	0.4795
0.4369	1.14	380	0.4809
0.4733	1.17	390	0.4661
0.4259	1.2	400	0.4490
0.4345	1.23	410	0.4440
0.4075	1.26	420	0.4519
0.4423	1.29	430	0.4503
0.3973	1.32	440	0.4466
0.3671	1.35	450	0.4481
0.4627	1.38	460	0.4431
0.435	1.41	470	0.4454
0.4073	1.44	480	0.4470
0.4309	1.47	490	0.4398
0.4392	1.5	500	0.4333
0.4372	1.53	510	0.4270
0.4842	1.56	520	0.4329
0.4509	1.59	530	0.4343
0.4836	1.62	540	0.4324
0.3892	1.65	550	0.4187
0.3473	1.68	560	0.4163
0.4382	1.71	570	0.4161
0.365	1.74	580	0.4102
0.4001	1.77	590	0.4122
0.3835	1.8	600	0.4162
0.432	1.83	610	0.4147
0.4152	1.86	620	0.4254
0.4328	1.89	630	0.4319
0.4156	1.92	640	0.4210
0.3748	1.95	650	0.4182
0.449	1.98	660	0.4155
0.4083	2.01	670	0.4145
0.3137	2.04	680	0.4187
0.3838	2.07	690	0.4220
0.4279	2.1	700	0.4105
0.3715	2.13	710	0.4113
0.3207	2.16	720	0.4169
0.3755	2.19	730	0.4207
0.3726	2.22	740	0.4280
0.4083	2.25	750	0.4389
0.3895	2.28	760	0.4378
0.3834	2.31	770	0.4327
0.3968	2.34	780	0.4292
0.3871	2.37	790	0.4223
0.4057	2.4	800	0.4232
0.3633	2.43	810	0.4161
0.3998	2.46	820	0.4107
0.3789	2.49	830	0.4080
0.4204	2.52	840	0.4126
0.401	2.55	850	0.4094
0.4049	2.58	860	0.3987
0.4129	2.61	870	0.3941
0.3414	2.64	880	0.3951
0.4505	2.67	890	0.3942
0.407	2.7	900	0.3985
0.3854	2.73	910	0.4011
0.4164	2.76	920	0.4025
0.4032	2.79	930	0.4005
0.3638	2.82	940	0.4002
0.3799	2.85	950	0.4008
0.4283	2.89	960	0.3955
0.3696	2.92	970	0.3947
0.3766	2.95	980	0.3959
0.3956	2.98	990	0.3950
0.3816	3.01	1000	0.3950
0.3108	3.04	1010	0.3934
0.3578	3.07	1020	0.3970
0.3519	3.1	1030	0.4019
0.3121	3.13	1040	0.4065
0.3845	3.16	1050	0.4081
0.342	3.19	1060	0.4075
0.3573	3.22	1070	0.4093
0.3673	3.25	1080	0.4089
0.3596	3.28	1090	0.4084
0.3474	3.31	1100	0.4089
0.3966	3.34	1110	0.4072
0.3813	3.37	1120	0.4046
0.3557	3.4	1130	0.4067
0.4239	3.43	1140	0.4052
0.3938	3.46	1150	0.3973
0.3686	3.49	1160	0.3980
0.3763	3.52	1170	0.3954
0.3754	3.55	1180	0.3936
0.3656	3.58	1190	0.3937
0.3383	3.61	1200	0.3942
0.3984	3.64	1210	0.3974
0.3814	3.67	1220	0.3995
0.3309	3.7	1230	0.3980
0.347	3.73	1240	0.3971
0.3727	3.76	1250	0.3958
0.381	3.79	1260	0.3950
0.3824	3.82	1270	0.3946
0.3971	3.85	1280	0.3941
0.3634	3.88	1290	0.3950
0.3501	3.91	1300	0.3939
0.327	3.94	1310	0.3927
0.3313	3.97	1320	0.3978
0.378	4.0	1330	0.4029
0.3376	4.03	1340	0.4008
0.3636	4.06	1350	0.3977
0.3064	4.09	1360	0.3976
0.4012	4.12	1370	0.3981
0.2932	4.15	1380	0.3991
0.3805	4.18	1390	0.3996
0.3438	4.21	1400	0.3996
0.3413	4.24	1410	0.4001
0.365	4.27	1420	0.3981
0.3769	4.3	1430	0.3960
0.3552	4.33	1440	0.3940
0.3328	4.36	1450	0.3937
0.3719	4.39	1460	0.3929
0.3607	4.42	1470	0.3933
0.3117	4.45	1480	0.3942
0.3522	4.48	1490	0.3964
0.375	4.51	1500	0.3979
0.3337	4.54	1510	0.3985
0.3549	4.57	1520	0.3959
0.4021	4.6	1530	0.3928
0.3217	4.63	1540	0.3895
0.3533	4.66	1550	0.3881
0.3564	4.69	1560	0.3882
0.3281	4.72	1570	0.3891
0.3474	4.75	1580	0.3897
0.3447	4.78	1590	0.3910
0.318	4.81	1600	0.3928
0.3414	4.84	1610	0.3942
0.3311	4.87	1620	0.3940
0.2932	4.9	1630	0.3921
0.3456	4.93	1640	0.3886
0.3462	4.96	1650	0.3851
0.3599	4.99	1660	0.3847
0.2819	5.02	1670	0.3864
0.3026	5.05	1680	0.3899
0.3272	5.08	1690	0.3909
0.3466	5.11	1700	0.3911
0.3279	5.14	1710	0.3912
0.3317	5.17	1720	0.3908
0.3523	5.2	1730	0.3906
0.3044	5.23	1740	0.3911
0.3361	5.26	1750	0.3902
0.3303	5.29	1760	0.3891
0.351	5.32	1770	0.3887
0.3093	5.35	1780	0.3904
0.3773	5.38	1790	0.3900
0.3302	5.41	1800	0.3901
0.303	5.44	1810	0.3911
0.3131	5.47	1820	0.3914
0.3483	5.5	1830	0.3907
0.3046	5.53	1840	0.3902
0.3794	5.56	1850	0.3898
0.3228	5.59	1860	0.3892
0.3268	5.62	1870	0.3888
0.3602	5.65	1880	0.3869
0.3655	5.68	1890	0.3854
0.3168	5.71	1900	0.3844
0.3359	5.74	1910	0.3840
0.314	5.77	1920	0.3841
0.3428	5.8	1930	0.3843
0.3504	5.83	1940	0.3835
0.3342	5.86	1950	0.3831
0.3474	5.89	1960	0.3828
0.3453	5.92	1970	0.3821
0.2968	5.95	1980	0.3829
0.3402	5.98	1990	0.3826
0.3401	6.01	2000	0.3819
0.3282	6.04	2010	0.3817
0.3265	6.07	2020	0.3821
0.3265	6.1	2030	0.3836
0.3189	6.13	2040	0.3841
0.2829	6.16	2050	0.3847
0.341	6.19	2060	0.3855
0.307	6.22	2070	0.3858
0.3037	6.25	2080	0.3853
0.2908	6.28	2090	0.3853
0.3204	6.31	2100	0.3856
0.3103	6.34	2110	0.3855
0.3359	6.37	2120	0.3851
0.3156	6.4	2130	0.3842
0.3207	6.43	2140	0.3837
0.2814	6.46	2150	0.3833
0.2908	6.49	2160	0.3833
0.3119	6.52	2170	0.3836
0.3272	6.55	2180	0.3839
0.3309	6.58	2190	0.3835
0.3141	6.61	2200	0.3832
0.3082	6.64	2210	0.3831
0.3661	6.67	2220	0.3829
0.3256	6.7	2230	0.3828
0.2938	6.73	2240	0.3828
0.3268	6.76	2250	0.3827
0.34	6.79	2260	0.3827
0.3444	6.82	2270	0.3825
0.3251	6.85	2280	0.3823
0.277	6.88	2290	0.3824
0.2945	6.91	2300	0.3824
0.3313	6.94	2310	0.3824
0.3536	6.97	2320	0.3823

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3