Training Loss	Epoch	Step	Validation Loss	Rouge2 Precision	Rouge2 Recall	Rouge2 Fmeasure
1.8404	0.75	500	1.5005	0.4265	0.2786	0.3273
1.6858	1.51	1000	1.4216	0.4318	0.2946	0.3404
1.6071	2.26	1500	1.3777	0.4472	0.3148	0.3598
1.5551	3.02	2000	1.3360	0.4406	0.3168	0.3586
1.5116	3.77	2500	1.3128	0.4523	0.3234	0.3671
1.4837	4.52	3000	1.2937	0.4477	0.3215	0.3645
1.4513	5.28	3500	1.2766	0.4511	0.3262	0.3689
1.4336	6.03	4000	1.2626	0.4548	0.3283	0.3718
1.4149	6.79	4500	1.2449	0.4495	0.3274	0.3687
1.3977	7.54	5000	1.2349	0.4507	0.3305	0.3712
1.3763	8.3	5500	1.2239	0.4519	0.3266	0.3688
1.371	9.05	6000	1.2171	0.4546	0.3305	0.3727
1.3501	9.8	6500	1.2080	0.4575	0.3329	0.3755
1.3443	10.56	7000	1.2017	0.4576	0.3314	0.3742
1.326	11.31	7500	1.1926	0.4578	0.333	0.3757
1.3231	12.07	8000	1.1866	0.4606	0.3357	0.3782
1.3089	12.82	8500	1.1816	0.4591	0.3338	0.3765
1.3007	13.57	9000	1.1764	0.4589	0.3361	0.3777
1.2943	14.33	9500	1.1717	0.4641	0.3382	0.3811
1.2854	15.08	10000	1.1655	0.4617	0.3378	0.38
1.2777	15.84	10500	1.1612	0.464	0.3401	0.3823
1.2684	16.59	11000	1.1581	0.4608	0.3367	0.3789
1.2612	17.35	11500	1.1554	0.4623	0.3402	0.3818
1.2625	18.1	12000	1.1497	0.4613	0.3381	0.3802
1.2529	18.85	12500	1.1465	0.4671	0.3419	0.3848
1.2461	19.61	13000	1.1431	0.4646	0.3399	0.3824
1.2415	20.36	13500	1.1419	0.4659	0.341	0.3835
1.2375	21.12	14000	1.1377	0.4693	0.3447	0.3873
1.2315	21.87	14500	1.1353	0.4672	0.3433	0.3855
1.2263	22.62	15000	1.1333	0.467	0.3433	0.3854
1.2214	23.38	15500	1.1305	0.4682	0.3446	0.3869
1.2202	24.13	16000	1.1291	0.4703	0.3465	0.3888
1.2155	24.89	16500	1.1270	0.472	0.348	0.3903
1.2064	25.64	17000	1.1261	0.4724	0.3479	0.3905
1.2173	26.4	17500	1.1236	0.4734	0.3485	0.3912
1.1994	27.15	18000	1.1220	0.4739	0.3486	0.3915
1.2018	27.9	18500	1.1217	0.4747	0.3489	0.3921
1.2045	28.66	19000	1.1194	0.4735	0.3488	0.3916
1.1949	29.41	19500	1.1182	0.4732	0.3484	0.3911
1.19	30.17	20000	1.1166	0.4724	0.3479	0.3904
1.1932	30.92	20500	1.1164	0.4753	0.3494	0.3924
1.1952	31.67	21000	1.1147	0.4733	0.3485	0.3911
1.1922	32.43	21500	1.1146	0.475	0.3494	0.3923
1.1889	33.18	22000	1.1132	0.4765	0.3499	0.3933
1.1836	33.94	22500	1.1131	0.4768	0.351	0.3939
1.191	34.69	23000	1.1127	0.4755	0.3495	0.3926
1.1811	35.44	23500	1.1113	0.4748	0.349	0.3919
1.1864	36.2	24000	1.1107	0.4751	0.3494	0.3921
1.1789	36.95	24500	1.1103	0.4756	0.3499	0.3927
1.1819	37.71	25000	1.1101	0.4758	0.35	0.3932
1.1862	38.46	25500	1.1099	0.4755	0.3497	0.3926
1.1764	39.22	26000	1.1101	0.4759	0.3498	0.3928
1.1819	39.97	26500	1.1101	0.4758	0.3498	0.3927

Framework versions

Transformers 4.12.5
Pytorch 1.10.0+cu111
Datasets 1.15.1
Tokenizers 0.10.3

gayanin/t5-small-mlm-pubmed-35

t5-small-mlm-pubmed-35

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gayanin/
t5-small-mlm-pubmed-35