Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
7.8225	1.0	6	7.8823	0.1442	19.0
6.4593	2.0	12	6.2595	0.0	19.0
5.7362	3.0	18	5.8728	0.5829	19.0
5.1022	4.0	24	6.0663	0.5829	19.0
5.1499	5.0	30	6.1787	0.5829	19.0
4.4478	6.0	36	6.1807	0.2295	19.0
4.6633	7.0	42	5.8996	0.5829	19.0
4.6893	8.0	48	6.0757	0.0	19.0
5.3314	9.0	54	5.8488	0.5829	19.0
4.9772	10.0	60	5.8862	0.5829	19.0
4.6109	11.0	66	6.0246	0.6041	19.0
4.133	12.0	72	5.9013	0.5829	19.0
4.9029	13.0	78	6.0611	0.6006	19.0
3.9306	14.0	84	5.8331	0.5938	19.0
4.2939	15.0	90	6.0608	0.202	19.0
3.7392	16.0	96	5.9132	0.4958	19.0
4.0965	17.0	102	6.0289	0.6011	19.0
4.8056	18.0	108	5.8952	0.6233	19.0
4.9226	19.0	114	6.1260	0.2865	19.0
3.463	20.0	120	6.0577	0.5829	19.0
3.6935	21.0	126	5.9865	0.6482	19.0
4.8423	22.0	132	6.1672	0.5938	19.0
4.1419	23.0	138	5.9532	0.0	19.0
4.114	24.0	144	5.9337	0.1363	19.0
3.687	25.0	150	5.9786	0.0	19.0
4.4531	26.0	156	6.2074	0.1645	8.0
3.7463	27.0	162	6.1692	0.0	19.0
4.1026	28.0	168	6.0478	0.0	19.0
3.8369	29.0	174	6.0615	0.0	19.0
3.7155	30.0	180	6.1976	0.6323	19.0
3.8799	31.0	186	6.2384	0.0	19.0
4.2195	32.0	192	6.1328	0.5829	19.0
5.1049	33.0	198	5.9780	0.5829	19.0
4.1496	34.0	204	6.0294	0.6233	19.0
3.8001	35.0	210	6.1042	0.1346	19.0
3.4327	36.0	216	5.8325	0.1023	19.0
4.1074	37.0	222	6.1190	0.611	19.0
3.84	38.0	228	6.4321	0.2321	19.0
3.7483	39.0	234	6.2523	0.2795	19.0
3.9157	40.0	240	6.2355	0.4213	19.0
3.3449	41.0	246	6.1757	0.611	19.0
3.5886	42.0	252	6.0657	0.5938	19.0
3.5048	43.0	258	6.0277	0.5829	19.0
3.7519	44.0	264	6.5569	0.681	19.0
3.7334	45.0	270	6.0739	0.5938	19.0
3.8206	46.0	276	6.0092	0.6401	19.0
3.5061	47.0	282	6.0719	0.6488	19.0
3.4392	48.0	288	6.0652	0.59	19.0
3.6158	49.0	294	6.0207	0.5829	19.0
3.1909	50.0	300	6.2023	0.1442	19.0
3.2138	51.0	306	6.1003	0.6233	19.0
4.0992	52.0	312	6.2286	0.5896	19.0
3.4983	53.0	318	6.2911	0.6006	19.0
3.0111	54.0	324	6.4124	0.3004	11.0
3.251	55.0	330	5.9168	0.7833	14.0
3.2281	56.0	336	6.0207	0.3379	19.0
3.6692	57.0	342	6.1399	0.6395	19.0
2.8706	58.0	348	6.4675	0.2317	19.0
3.7137	59.0	354	6.1596	0.0	19.0
3.6537	60.0	360	6.2131	0.5938	19.0
3.2023	61.0	366	6.2877	0.6323	19.0
2.3914	62.0	372	6.5001	0.6323	19.0
2.8612	63.0	378	6.5683	0.7084	19.0
3.1646	64.0	384	6.7003	0.2039	9.0
3.1234	65.0	390	6.1225	0.4851	11.0
3.0967	66.0	396	6.2517	0.5896	19.0
2.5832	67.0	402	6.3071	0.5896	19.0
3.2709	68.0	408	6.5033	0.6482	19.0
3.2511	69.0	414	6.4329	0.6395	19.0
2.7053	70.0	420	6.5449	0.6323	19.0
3.4684	71.0	426	6.9512	0.2914	19.0
2.7875	72.0	432	6.6579	0.6006	19.0
2.5674	73.0	438	6.4629	0.6395	19.0
2.3457	74.0	444	6.6680	0.7084	19.0
2.339	75.0	450	6.7313	0.7333	19.0
3.4058	76.0	456	6.7786	0.3105	16.0
2.5678	77.0	462	6.6553	0.5896	19.0
2.9506	78.0	468	6.9532	0.3379	19.0
2.2285	79.0	474	7.1575	0.191	6.0
2.5635	80.0	480	7.1580	0.2837	15.0
1.8763	81.0	486	7.0203	0.5896	19.0
3.3663	82.0	492	6.7150	0.5896	19.0
2.1434	83.0	498	6.5911	0.5896	19.0
2.6678	84.0	504	6.7084	0.5829	19.0
3.7082	85.0	510	6.7475	0.4447	13.0
3.3436	86.0	516	6.6436	0.2223	19.0
2.3866	87.0	522	6.6915	0.5896	19.0
2.0647	88.0	528	7.0000	0.5896	19.0
2.7861	89.0	534	7.1116	0.1346	19.0
2.5331	90.0	540	7.0207	0.0	19.0
2.3609	91.0	546	7.0159	0.2837	12.0
2.5884	92.0	552	6.9928	0.1628	19.0
2.2198	93.0	558	7.0179	0.6885	19.0
2.4258	94.0	564	7.0429	0.5938	19.0
1.9681	95.0	570	7.0348	1.3808	19.0
2.2643	96.0	576	7.0513	1.1802	19.0
2.1551	97.0	582	7.0741	1.1558	19.0
2.1624	98.0	588	7.1079	1.1558	19.0
2.6342	99.0	594	7.1447	1.1558	19.0
1.1566	100.0	600	7.1529	1.1802	19.0

Framework versions

Transformers 4.35.2
Pytorch 1.13.1+cu117
Datasets 2.17.0
Tokenizers 0.15.2

kmok1/cs_mT5_0.01_100_v0.2

cs_mT5_0.01_100_v0.2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

kmok1/
cs_mT5_0.01_100_v0.2