Training Loss	Epoch	Step	Validation Loss	Bleu Score
No log	1.0	95	13.2410	0.0069
No log	2.0	190	7.6317	0.0134
No log	3.0	285	6.0919	0.0058
No log	4.0	380	5.4922	0.0021
No log	5.0	475	4.7151	0.0009
12.0698	6.0	570	4.1412	0.0003
12.0698	7.0	665	3.6398	0.0003
12.0698	8.0	760	3.2643	0.0009
12.0698	9.0	855	3.0544	0.0013
12.0698	10.0	950	2.8015	0.0043
4.696	11.0	1045	2.5552	0.0789
4.696	12.0	1140	2.3535	0.1036
4.696	13.0	1235	2.2132	0.0050
4.696	14.0	1330	2.1084	0.1333
4.696	15.0	1425	2.0117	0.2972
3.1348	16.0	1520	1.9333	0.2481
3.1348	17.0	1615	1.8395	0.4149
3.1348	18.0	1710	1.7661	0.5439
3.1348	19.0	1805	1.7101	0.6001
3.1348	20.0	1900	1.6562	0.6219
3.1348	21.0	1995	1.6073	0.5865
2.4276	22.0	2090	1.5773	0.5683
2.4276	23.0	2185	1.5478	0.5408
2.4276	24.0	2280	1.5190	0.5749
2.4276	25.0	2375	1.4927	0.5818
2.4276	26.0	2470	1.4671	0.5673
2.076	27.0	2565	1.4499	0.5616
2.076	28.0	2660	1.4275	0.6041
2.076	29.0	2755	1.4096	0.5764
2.076	30.0	2850	1.3983	0.5862
2.076	31.0	2945	1.3812	0.5982
1.8828	32.0	3040	1.3679	0.5927
1.8828	33.0	3135	1.3548	0.5916
1.8828	34.0	3230	1.3461	0.5769
1.8828	35.0	3325	1.3353	0.5871
1.8828	36.0	3420	1.3293	0.5687
1.7602	37.0	3515	1.3195	0.5689
1.7602	38.0	3610	1.3109	0.5949
1.7602	39.0	3705	1.3049	0.5619
1.7602	40.0	3800	1.2953	0.5872
1.7602	41.0	3895	1.2907	0.6014
1.7602	42.0	3990	1.2831	0.5917
1.6652	43.0	4085	1.2757	0.5718
1.6652	44.0	4180	1.2692	0.5707
1.6652	45.0	4275	1.2642	0.5758
1.6652	46.0	4370	1.2619	0.6012
1.6652	47.0	4465	1.2527	0.5749
1.6009	48.0	4560	1.2496	0.5722
1.6009	49.0	4655	1.2447	0.5633
1.6009	50.0	4750	1.2411	0.5615
1.6009	51.0	4845	1.2356	0.5691
1.6009	52.0	4940	1.2322	0.5636
1.5481	53.0	5035	1.2285	0.5724
1.5481	54.0	5130	1.2255	0.5771
1.5481	55.0	5225	1.2201	0.5827
1.5481	56.0	5320	1.2181	0.5928
1.5481	57.0	5415	1.2152	0.5599
1.5082	58.0	5510	1.2123	0.5779
1.5082	59.0	5605	1.2083	0.5609
1.5082	60.0	5700	1.2070	0.5654
1.5082	61.0	5795	1.2036	0.5566
1.5082	62.0	5890	1.2011	0.5569
1.5082	63.0	5985	1.1993	0.5567
1.4799	64.0	6080	1.1958	0.5619
1.4799	65.0	6175	1.1950	0.5691
1.4799	66.0	6270	1.1914	0.5572
1.4799	67.0	6365	1.1879	0.5635
1.4799	68.0	6460	1.1866	0.5654
1.4475	69.0	6555	1.1850	0.5575
1.4475	70.0	6650	1.1833	0.5507
1.4475	71.0	6745	1.1820	0.5493
1.4475	72.0	6840	1.1786	0.5525
1.4475	73.0	6935	1.1789	0.5615
1.4233	74.0	7030	1.1770	0.5603
1.4233	75.0	7125	1.1749	0.5699
1.4233	76.0	7220	1.1754	0.5730
1.4233	77.0	7315	1.1735	0.5798
1.4233	78.0	7410	1.1716	0.5771
1.4101	79.0	7505	1.1699	0.5800
1.4101	80.0	7600	1.1675	0.5736
1.4101	81.0	7695	1.1661	0.5845
1.4101	82.0	7790	1.1659	0.5974
1.4101	83.0	7885	1.1664	0.5825
1.4101	84.0	7980	1.1647	0.5871
1.3965	85.0	8075	1.1639	0.5772
1.3965	86.0	8170	1.1628	0.5826
1.3965	87.0	8265	1.1615	0.5960
1.3965	88.0	8360	1.1616	0.5908
1.3965	89.0	8455	1.1613	0.5775
1.3835	90.0	8550	1.1604	0.5917
1.3835	91.0	8645	1.1597	0.5732
1.3835	92.0	8740	1.1594	0.5767
1.3835	93.0	8835	1.1584	0.5719
1.3835	94.0	8930	1.1581	0.5700
1.3766	95.0	9025	1.1583	0.5845
1.3766	96.0	9120	1.1578	0.5808
1.3766	97.0	9215	1.1578	0.5889
1.3766	98.0	9310	1.1577	0.5851
1.3766	99.0	9405	1.1578	0.5923
1.3726	100.0	9500	1.1577	0.5923

Framework versions

Transformers 4.18.0
Pytorch 1.11.0+cu113
Datasets 2.1.0
Tokenizers 0.12.1

chainyo/t5-base-sede-txt2sql

t5-base-sede-txt2sql

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

chainyo/
t5-base-sede-txt2sql