Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
No log	1.0	36	3.4870	0.0863	17.9878
No log	2.0	72	3.0772	0.2333	17.622
No log	3.0	108	2.7865	0.2752	16.6829
No log	4.0	144	2.5878	0.8782	17.9024
No log	5.0	180	2.4639	1.584	17.1463
No log	6.0	216	2.3559	0.9321	16.8049
No log	7.0	252	2.2704	1.0018	17.3902
No log	8.0	288	2.1956	1.2549	17.0732
No log	9.0	324	2.1307	0.9709	17.4268
No log	10.0	360	2.0866	0.7563	17.5
No log	11.0	396	2.0392	0.707	17.2439
No log	12.0	432	1.9920	0.8647	16.9756
No log	13.0	468	1.9630	0.8724	17.8171
2.7137	14.0	504	1.9244	1.0593	17.4146
2.7137	15.0	540	1.9010	1.6823	17.061
2.7137	16.0	576	1.8711	1.6452	16.5732
2.7137	17.0	612	1.8475	1.6622	16.8659
2.7137	18.0	648	1.8265	2.2968	16.7195
2.7137	19.0	684	1.8056	2.2125	16.6098
2.7137	20.0	720	1.7962	2.3889	16.3049
2.7137	21.0	756	1.7778	2.341	16.3537
2.7137	22.0	792	1.7626	2.3187	16.1341
2.7137	23.0	828	1.7450	2.5281	16.0732
2.7137	24.0	864	1.7357	2.6768	15.9268
2.7137	25.0	900	1.7177	2.3932	15.9146
2.7137	26.0	936	1.7126	2.611	15.8537
2.7137	27.0	972	1.7088	2.2829	15.622
1.9301	28.0	1008	1.6868	2.4441	15.8293
1.9301	29.0	1044	1.6707	2.5402	16.0976
1.9301	30.0	1080	1.6790	2.0723	15.561
1.9301	31.0	1116	1.6600	1.4278	15.9146
1.9301	32.0	1152	1.6661	1.4274	15.7317
1.9301	33.0	1188	1.6474	1.4484	15.6463
1.9301	34.0	1224	1.6484	1.5172	15.7805
1.9301	35.0	1260	1.6389	1.5497	15.7561
1.9301	36.0	1296	1.6384	1.52	15.6341
1.9301	37.0	1332	1.6304	1.4572	15.8293
1.9301	38.0	1368	1.6163	1.4786	16.1341
1.9301	39.0	1404	1.6116	1.5765	15.9634
1.9301	40.0	1440	1.6020	1.5902	16.0244
1.9301	41.0	1476	1.6064	1.6992	15.8659
1.6368	42.0	1512	1.5949	1.5409	16.0
1.6368	43.0	1548	1.5811	1.4916	16.2439
1.6368	44.0	1584	1.5849	1.6047	16.2683
1.6368	45.0	1620	1.5843	1.521	15.7073
1.6368	46.0	1656	1.5805	1.7424	15.9878
1.6368	47.0	1692	1.5791	1.6066	15.9268
1.6368	48.0	1728	1.5734	1.602	15.7195
1.6368	49.0	1764	1.5649	1.5817	15.939
1.6368	50.0	1800	1.5654	1.6469	15.8293
1.6368	51.0	1836	1.5587	1.7048	15.6463
1.6368	52.0	1872	1.5553	1.5203	15.8415
1.6368	53.0	1908	1.5500	1.5646	15.6951
1.6368	54.0	1944	1.5532	1.5003	15.7195
1.6368	55.0	1980	1.5344	1.5359	16.1098
1.4554	56.0	2016	1.5370	1.6052	15.6951
1.4554	57.0	2052	1.5394	1.5299	15.9146
1.4554	58.0	2088	1.5399	1.6024	15.6829
1.4554	59.0	2124	1.5403	1.6342	15.6829
1.4554	60.0	2160	1.5361	1.609	15.7195
1.4554	61.0	2196	1.5308	1.6753	15.878
1.4554	62.0	2232	1.5211	1.6381	16.0976
1.4554	63.0	2268	1.5242	1.7172	15.622
1.4554	64.0	2304	1.5215	1.6888	15.9024
1.4554	65.0	2340	1.5146	1.6619	16.0
1.4554	66.0	2376	1.5173	1.7203	15.8537
1.4554	67.0	2412	1.5235	1.7363	15.7317
1.4554	68.0	2448	1.5125	1.7295	16.0366
1.4554	69.0	2484	1.5141	1.7005	15.8902
1.3341	70.0	2520	1.5162	1.8302	15.7927
1.3341	71.0	2556	1.5129	1.8278	15.9024
1.3341	72.0	2592	1.5123	1.7764	15.6829
1.3341	73.0	2628	1.5046	1.7259	15.9634
1.3341	74.0	2664	1.5069	1.6517	15.9024
1.3341	75.0	2700	1.5026	1.7334	15.9024
1.3341	76.0	2736	1.4923	1.7531	15.9268
1.3341	77.0	2772	1.4956	1.7338	15.7561
1.3341	78.0	2808	1.4996	1.6956	15.7805
1.3341	79.0	2844	1.5010	1.7299	15.9268
1.3341	80.0	2880	1.5012	1.7097	15.9024
1.3341	81.0	2916	1.5032	1.7689	15.8902
1.3341	82.0	2952	1.5025	1.7353	15.939
1.3341	83.0	2988	1.5004	1.7472	15.9512
1.2568	84.0	3024	1.4989	1.7171	15.9756
1.2568	85.0	3060	1.5015	1.7704	15.9024
1.2568	86.0	3096	1.5017	1.7838	15.9024
1.2568	87.0	3132	1.5022	1.7562	16.0366
1.2568	88.0	3168	1.5004	1.7633	16.0366
1.2568	89.0	3204	1.4995	1.7633	15.9756
1.2568	90.0	3240	1.5038	1.766	15.8537
1.2568	91.0	3276	1.5001	1.7764	16.0
1.2568	92.0	3312	1.5010	1.7707	15.878
1.2568	93.0	3348	1.4996	1.7633	15.9268
1.2568	94.0	3384	1.5011	1.7453	15.8171
1.2568	95.0	3420	1.5014	1.7385	15.7927
1.2568	96.0	3456	1.4996	1.7253	15.7927
1.2568	97.0	3492	1.4988	1.7459	15.8049
1.2103	98.0	3528	1.4978	1.7461	15.8171
1.2103	99.0	3564	1.4986	1.7461	15.8293
1.2103	100.0	3600	1.4986	1.7461	15.8171

Framework versions

Transformers 4.26.1
Pytorch 1.13.0
Datasets 2.10.1
Tokenizers 0.13.2

mekjr1/t5-base-finetuned-es-to-pua

t5-base-finetuned-es-to-pua

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mekjr1/
t5-base-finetuned-es-to-pua