Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
2.684	1.0	6	8.4517	0.0956	61.6667
1.978	2.0	12	8.4546	0.0985	61.8095
2.8654	3.0	18	8.4538	0.0961	62.4286
2.8165	4.0	24	8.4550	0.0991	63.1905
2.6606	5.0	30	8.4556	0.0956	61.0476
3.1159	6.0	36	8.4525	0.0964	60.5238
1.813	7.0	42	8.4524	0.0961	59.8095
2.9637	8.0	48	8.4520	0.0961	59.8095
2.1663	9.0	54	8.4526	0.0918	59.5714
2.475	10.0	60	8.4516	0.0916	59.381
2.5769	11.0	66	8.4493	0.0927	60.1905
2.414	12.0	72	8.4485	0.0927	60.1905
2.5985	13.0	78	8.4500	0.0946	60.1905
2.6263	14.0	84	8.4527	0.1003	61.0
2.2439	15.0	90	8.4533	0.0774	69.0952
1.9865	16.0	96	8.4542	0.0769	69.5238
2.2472	17.0	102	8.4540	0.0766	69.7619
2.5489	18.0	108	8.4534	0.0782	70.3333
1.9181	19.0	114	8.4527	0.0789	70.5714
2.0332	20.0	120	8.4505	0.0785	70.7619
1.9397	21.0	126	8.4488	0.0784	70.9048
2.788	22.0	132	8.4480	0.0772	71.9524
2.4842	23.0	138	8.4473	0.0778	71.6667
2.3397	24.0	144	8.4459	0.0975	62.6667
2.3303	25.0	150	8.4448	0.1314	71.9048
2.6417	26.0	156	8.4436	0.1311	71.9524
2.0759	27.0	162	8.4446	0.128	71.9524
2.0973	28.0	168	8.4450	0.1659	62.1905
2.9593	29.0	174	8.4455	0.1285	71.4762
3.0086	30.0	180	8.4442	0.1624	61.8571
2.684	31.0	186	8.4431	0.162	62.0952
2.7015	32.0	192	8.4442	0.162	62.0952
4.6745	33.0	198	8.4431	0.1624	62.9048
2.1913	34.0	204	8.4427	0.1607	63.0
2.1685	35.0	210	8.4443	0.1671	61.4286
2.3458	36.0	216	8.4458	0.1346	69.6667
2.0533	37.0	222	8.4456	0.132	70.1905
3.1101	38.0	228	8.4442	0.1335	69.8095
2.2737	39.0	234	8.4447	0.0787	70.7619
2.4838	40.0	240	8.4476	0.0784	70.1905
1.9048	41.0	246	8.4487	0.0801	70.4762
2.825	42.0	252	8.4495	0.0668	79.4286
1.7811	43.0	258	8.4521	0.0639	78.2381
2.1382	44.0	264	8.4545	0.0639	78.1429
2.2783	45.0	270	8.4553	0.0636	78.5714
2.1117	46.0	276	8.4558	0.0636	78.5714
2.0165	47.0	282	8.4563	0.0638	78.4762
2.2424	48.0	288	8.4568	0.0639	78.3333
2.7404	49.0	294	8.4564	0.0627	79.5714
3.3443	50.0	300	8.4560	0.0617	78.4762
2.7281	51.0	306	8.4551	0.0617	78.4762
2.9189	52.0	312	8.4520	0.0757	70.7143
2.3192	53.0	318	8.4512	0.0754	70.7619
2.3737	54.0	324	8.4505	0.0604	78.4286
2.4041	55.0	330	8.4490	0.0606	78.0952
4.5412	56.0	336	8.4478	0.0618	78.0952
2.399	57.0	342	8.4469	0.0617	78.2381
1.8226	58.0	348	8.4467	0.062	77.9048
2.3362	59.0	354	8.4463	0.0612	77.4762
2.4263	60.0	360	8.4450	0.0612	77.4762
2.7929	61.0	366	8.4439	0.0617	78.2381
3.2633	62.0	372	8.4434	0.0615	78.3333
2.3451	63.0	378	8.4436	0.0607	77.9048
2.8337	64.0	384	8.4429	0.061	77.4762
2.7405	65.0	390	8.4430	0.0607	77.9048
2.8955	66.0	396	8.4420	0.0614	78.6667
2.3475	67.0	402	8.4408	0.061	79.0952
2.0904	68.0	408	8.4383	0.0608	79.1905
2.4816	69.0	414	8.4367	0.0607	79.3333
2.3696	70.0	420	8.4365	0.0607	79.3333
2.7587	71.0	426	8.4364	0.0616	79.5714
2.0684	72.0	432	8.4369	0.0617	79.4762
2.5021	73.0	438	8.4375	0.0617	79.4762
1.4037	74.0	444	8.4362	0.0759	71.0476
2.1197	75.0	450	8.4357	0.0763	70.7619
2.2019	76.0	456	8.4378	0.0612	78.8571
1.8674	77.0	462	8.4402	0.062	77.7619
4.6628	78.0	468	8.4415	0.0769	69.3333
2.5704	79.0	474	8.4420	0.0769	69.3333
1.8771	80.0	480	8.4422	0.0772	69.1905
1.9444	81.0	486	8.4437	0.078	70.5238
2.0133	82.0	492	8.4443	0.0771	71.1429
2.8815	83.0	498	8.4445	0.0757	70.4286
3.0573	84.0	504	8.4455	0.0621	77.7143
2.011	85.0	510	8.4469	0.0621	77.7143
1.8176	86.0	516	8.4488	0.0621	77.7143
1.505	87.0	522	8.4512	0.0621	77.7143
5.016	88.0	528	8.4542	0.0622	77.5714
4.8956	89.0	534	8.4565	0.0625	77.1905
2.3939	90.0	540	8.4578	0.0625	77.1905
1.8629	91.0	546	8.4589	0.0622	77.5714
2.7315	92.0	552	8.4599	0.0617	78.1429
2.6185	93.0	558	8.4605	0.0618	78.1429
2.2754	94.0	564	8.4598	0.0617	78.2381
1.9322	95.0	570	8.4582	0.0616	78.381
2.1725	96.0	576	8.4583	0.0621	78.9524
2.603	97.0	582	8.4576	0.0619	79.1905
2.543	98.0	588	8.4569	0.0619	79.1905
2.4981	99.0	594	8.4563	0.0618	79.2857
1.8449	100.0	600	8.4561	0.063	80.0952
3.063	101.0	606	8.4559	0.0618	79.2857
1.7031	102.0	612	8.4564	0.0622	77.7143
2.6749	103.0	618	8.4563	0.0623	77.5714
2.5504	104.0	624	8.4558	0.0781	69.4286
1.785	105.0	630	8.4559	0.0791	69.4286
2.3876	106.0	636	8.4560	0.0753	70.5238
1.9649	107.0	642	8.4556	0.0613	78.4762
2.5544	108.0	648	8.4571	0.0617	78.3333
2.3048	109.0	654	8.4578	0.0619	77.9524
3.2234	110.0	660	8.4595	0.0618	77.9524
2.5271	111.0	666	8.4600	0.0619	77.7619
2.1592	112.0	672	8.4599	0.0621	77.8571
2.1582	113.0	678	8.4600	0.0618	77.9524
5.1356	114.0	684	8.4596	0.0622	77.6667
3.1661	115.0	690	8.4594	0.0622	77.7619
2.1159	116.0	696	8.4597	0.0617	78.2381
2.1355	117.0	702	8.4602	0.0612	78.7143
2.5071	118.0	708	8.4606	0.0631	79.9524
2.5419	119.0	714	8.4608	0.0631	80.0476
2.1749	120.0	720	8.4616	0.0617	79.381
2.1737	121.0	726	8.4622	0.0631	80.0476
2.2413	122.0	732	8.4623	0.0633	79.8095
2.2636	123.0	738	8.4624	0.0636	79.4762
2.9731	124.0	744	8.4624	0.0636	79.4762
2.6207	125.0	750	8.4621	0.0636	79.4762
2.6231	126.0	756	8.4602	0.0636	79.4762
2.4161	127.0	762	8.4605	0.0637	79.381
2.9764	128.0	768	8.4613	0.0762	70.9524
2.41	129.0	774	8.4618	0.0761	71.0476
2.1357	130.0	780	8.4620	0.0762	70.7143
3.211	131.0	786	8.4621	0.0762	70.7143
1.8992	132.0	792	8.4623	0.0633	79.7143
2.9689	133.0	798	8.4621	0.0631	79.9524
2.4456	134.0	804	8.4619	0.0629	80.0476
1.9567	135.0	810	8.4620	0.063	79.8571
4.3724	136.0	816	8.4619	0.0626	79.2381
2.2729	137.0	822	8.4623	0.0626	79.2381
2.2375	138.0	828	8.4620	0.0625	78.2381
2.0507	139.0	834	8.4617	0.0625	78.2381
3.2081	140.0	840	8.4621	0.1072	78.0952
3.0478	141.0	846	8.4629	0.1072	78.0952
1.6707	142.0	852	8.4628	0.1042	77.5238
2.7035	143.0	858	8.4626	0.1042	77.5238
2.0088	144.0	864	8.4627	0.1042	77.5238
2.2061	145.0	870	8.4619	0.1042	77.5238
2.9719	146.0	876	8.4597	0.1055	76.7143
1.7429	147.0	882	8.4591	0.1335	69.0952
2.0689	148.0	888	8.4590	0.1094	77.7143
3.0878	149.0	894	8.4593	0.1094	77.7143
2.3762	150.0	900	8.4593	0.1083	78.381
1.9409	151.0	906	8.4591	0.1083	78.381
2.472	152.0	912	8.4590	0.1328	70.1905
2.1888	153.0	918	8.4590	0.1341	69.619
2.8783	154.0	924	8.4582	0.1341	69.619
2.4719	155.0	930	8.4582	0.1318	68.9524
2.4873	156.0	936	8.4579	0.1318	68.9524
2.202	157.0	942	8.4576	0.1318	68.9524
2.4128	158.0	948	8.4577	0.1318	68.9524
1.6922	159.0	954	8.4577	0.1318	68.9524
2.5719	160.0	960	8.4582	0.1318	68.9524
1.8392	161.0	966	8.4581	0.1318	68.9524
2.1349	162.0	972	8.4581	0.1318	68.9524
2.0836	163.0	978	8.4586	0.1318	68.9524
2.5173	164.0	984	8.4590	0.1318	68.9524
1.9422	165.0	990	8.4591	0.1318	68.9524
2.4949	166.0	996	8.4591	0.1318	68.9524
2.6692	167.0	1002	8.4586	0.1318	68.9524
1.5472	168.0	1008	8.4588	0.1318	68.9524
5.0693	169.0	1014	8.4589	0.1318	68.9524
2.6937	170.0	1020	8.4593	0.1318	68.9524
5.0729	171.0	1026	8.4596	0.1306	69.5238
2.645	172.0	1032	8.4599	0.1306	69.5238
1.671	173.0	1038	8.4600	0.1306	69.5238
2.329	174.0	1044	8.4600	0.1306	69.5238
2.2443	175.0	1050	8.4597	0.1306	69.5238
2.0599	176.0	1056	8.4594	0.1306	69.5238
2.0761	177.0	1062	8.4598	0.1639	60.7619
2.3301	178.0	1068	8.4595	0.1306	69.5238
2.8817	179.0	1074	8.4595	0.1306	69.5238
2.3847	180.0	1080	8.4588	0.1312	69.5238
2.7967	181.0	1086	8.4586	0.1312	69.5238
1.6165	182.0	1092	8.4590	0.1308	69.6667
3.2699	183.0	1098	8.4585	0.1308	69.6667
2.1596	184.0	1104	8.4587	0.1308	69.6667
4.383	185.0	1110	8.4587	0.1308	69.6667
2.5019	186.0	1116	8.4587	0.1308	69.6667
2.1497	187.0	1122	8.4587	0.1308	69.6667
2.7942	188.0	1128	8.4594	0.1342	69.7619
2.5737	189.0	1134	8.4595	0.1342	69.7619
2.7013	190.0	1140	8.4597	0.1342	69.7619
4.7672	191.0	1146	8.4598	0.1342	69.7619
4.723	192.0	1152	8.4598	0.1342	69.7619
2.2355	193.0	1158	8.4598	0.1342	69.7619
1.7872	194.0	1164	8.4599	0.1342	69.7619
2.0794	195.0	1170	8.4600	0.1342	69.7619
1.6962	196.0	1176	8.4601	0.1342	69.7619
2.2855	197.0	1182	8.4602	0.1342	69.7619
2.8048	198.0	1188	8.4603	0.1346	69.619
1.8135	199.0	1194	8.4603	0.1346	69.619
2.395	200.0	1200	8.4603	0.1346	69.619

Framework versions

Transformers 4.35.2
Pytorch 1.13.1+cu117
Datasets 2.16.1
Tokenizers 0.15.0

kmok1/cs_m2m_0.00001_200_v0.2

cs_m2m_0.00001_200_v0.2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

kmok1/
cs_m2m_0.00001_200_v0.2