Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
5.0853	1.0	6	6.9325	0.0	5.0
4.3538	2.0	12	7.0396	0.1923	7.5714
4.6426	3.0	18	7.0321	0.1563	42.1429
5.1737	4.0	24	7.0390	0.0335	103.5238
3.9214	5.0	30	7.0585	0.0	5.0
4.7309	6.0	36	7.1597	0.1313	7.7619
4.3458	7.0	42	7.1875	0.0	5.0
4.1409	8.0	48	7.1934	0.308	18.1429
3.8187	9.0	54	7.1696	0.0	5.0
3.9459	10.0	60	7.1153	0.0	5.0
4.3563	11.0	66	7.2286	0.3581	8.619
4.4193	12.0	72	7.3526	0.0	5.0
4.4508	13.0	78	7.4000	0.0	5.0
4.115	14.0	84	7.4140	0.0	5.0
4.1807	15.0	90	7.4866	0.0	5.0
3.8422	16.0	96	7.6149	0.3839	9.0
4.1567	17.0	102	7.5413	0.2035	8.8095
4.3236	18.0	108	7.5256	0.2104	9.0
4.3343	19.0	114	7.5449	0.149	8.4286
4.3139	20.0	120	7.4758	0.0	5.0
3.1706	21.0	126	7.5896	0.0274	130.9048
3.0241	22.0	132	7.8300	0.2142	7.9524
4.5364	23.0	138	7.8698	0.0515	5.2857
5.4824	24.0	144	7.8732	0.0364	192.0952
3.8072	25.0	150	7.7993	0.0	5.0
3.9879	26.0	156	7.7222	0.0746	200.0
4.0397	27.0	162	7.6906	0.0436	146.0476
3.7429	28.0	168	7.7814	0.0	6.8095
3.7498	29.0	174	7.8873	0.2861	8.0
4.1991	30.0	180	8.0400	0.3032	13.5714
5.4424	31.0	186	7.9368	0.2537	15.1905
3.6523	32.0	192	7.8529	0.3288	7.1905
5.5908	33.0	198	7.8531	0.087	5.8571
3.8218	34.0	204	7.7538	0.2073	7.8571
3.8408	35.0	210	7.6796	0.1027	7.381
3.2347	36.0	216	7.8281	0.1662	8.9524
4.0158	37.0	222	7.8108	0.1907	23.9524
4.2395	38.0	228	7.7778	0.4592	19.4286
3.1863	39.0	234	7.8962	0.3148	16.1429
3.5706	40.0	240	8.2310	0.2962	33.7619
3.8174	41.0	246	8.0290	0.2864	14.1429
3.6144	42.0	252	7.9235	0.2737	11.8095
3.914	43.0	258	7.9920	0.286	15.5714
3.9245	44.0	264	7.9770	0.1251	35.8571
3.223	45.0	270	8.1701	0.1428	32.1429
3.5751	46.0	276	8.2573	0.2497	19.9048
3.7939	47.0	282	8.2825	0.0571	110.9524
3.8968	48.0	288	8.4263	0.0702	200.0
2.2186	49.0	294	8.3673	0.2356	107.5714
3.1794	50.0	300	8.2041	0.2142	38.5238
3.3098	51.0	306	8.2863	0.0349	113.3333
3.7869	52.0	312	8.3350	0.0655	95.2857
3.7239	53.0	318	8.2509	0.025	179.7143
3.5206	54.0	324	8.2301	0.074	75.9524
3.2225	55.0	330	8.1540	0.0242	173.5238
2.6646	56.0	336	8.1574	0.3081	91.2381
3.3487	57.0	342	8.1095	0.0597	115.6667
3.2801	58.0	348	8.1534	0.1796	39.8095
2.7653	59.0	354	8.2800	0.0423	82.0476
3.3158	60.0	360	8.2560	0.0437	116.4762
2.5549	61.0	366	8.2070	0.0348	164.2857
2.9411	62.0	372	8.2850	0.3249	12.381
2.965	63.0	378	8.3497	0.0352	117.1429
3.4553	64.0	384	8.3532	0.0739	145.9524
3.1656	65.0	390	8.3229	0.1993	102.5714
3.3285	66.0	396	8.3454	0.2297	46.9524
2.7365	67.0	402	8.4989	0.2246	39.381
3.1372	68.0	408	8.4935	0.0444	115.2381
2.3018	69.0	414	8.4543	0.0552	113.8571
2.5972	70.0	420	8.4092	0.245	15.3333
5.2476	71.0	426	8.3573	0.2629	32.0476
2.4894	72.0	432	8.3228	0.2863	42.5238
3.9303	73.0	438	8.3295	0.5382	36.7619
3.8135	74.0	444	8.3803	0.2421	41.8095
2.36	75.0	450	8.4558	0.1325	58.381
2.7095	76.0	456	8.5280	0.2592	68.9524
2.0011	77.0	462	8.4020	0.2997	58.2381
1.9209	78.0	468	8.4449	0.1838	43.7143
3.3766	79.0	474	8.5564	0.2789	24.9048
3.4283	80.0	480	8.5476	0.264	35.7143
2.8935	81.0	486	8.5057	0.0633	79.8095
2.5961	82.0	492	8.4756	0.0648	92.9524
3.999	83.0	498	8.4273	0.1558	68.4286
3.612	84.0	504	8.3825	0.1379	52.9524
2.5813	85.0	510	8.3289	0.1275	42.0
2.8265	86.0	516	8.3150	0.2806	22.9048
3.1955	87.0	522	8.3218	0.2976	17.4762
2.7654	88.0	528	8.3135	0.2878	35.619
3.7539	89.0	534	8.3157	0.0896	48.4762
1.8882	90.0	540	8.3397	0.0897	57.7619
2.5795	91.0	546	8.3700	0.069	79.1905
1.9473	92.0	552	8.4195	0.1347	152.4762
2.349	93.0	558	8.4513	0.0239	183.619
3.1561	94.0	564	8.4664	0.0234	192.4286
2.9355	95.0	570	8.4679	0.1186	167.8571
2.5661	96.0	576	8.4588	0.1833	110.9524
3.1005	97.0	582	8.4478	0.0432	124.8571
2.7184	98.0	588	8.4399	0.0589	84.9048
2.8431	99.0	594	8.4340	0.1961	103.9524
2.9269	100.0	600	8.4343	0.0488	93.2857

Framework versions

Transformers 4.35.2
Pytorch 1.13.1+cu117
Datasets 2.16.1
Tokenizers 0.15.0

kmok1/cs_m2m_0.001_50_v0.2

cs_m2m_0.001_50_v0.2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

kmok1/
cs_m2m_0.001_50_v0.2