Training Loss	Epoch	Step	Validation Loss	Bleu
0.4816	1.0	1809	0.5935	47.7153
0.3341	2.0	3618	0.6006	50.5885
0.2506	3.0	5427	0.6418	49.6835
0.1782	4.0	7236	0.7034	49.5122
0.1341	5.0	9045	0.7710	49.0027
0.105	6.0	10854	0.8104	49.9420
0.0807	7.0	12663	0.8545	49.5733
0.065	8.0	14472	0.8951	49.2548
0.0527	9.0	16281	0.9306	49.9206
0.0425	10.0	18090	0.9683	48.3271
0.0361	11.0	19899	1.0163	49.0164
0.0316	12.0	21708	1.0402	49.8569
0.0262	13.0	23517	1.0561	49.0445
0.0235	14.0	25326	1.1004	49.7207
0.0219	15.0	27135	1.1100	49.8089
0.0188	16.0	28944	1.1268	50.5988
0.0168	17.0	30753	1.1314	49.6320
0.0156	18.0	32562	1.1476	49.8571
0.0142	19.0	34371	1.1578	50.1789
0.0138	20.0	36180	1.1919	48.9829
0.0121	21.0	37989	1.2091	50.1698
0.0121	22.0	39798	1.2353	49.7882
0.0107	23.0	41607	1.2361	50.2884
0.0102	24.0	43416	1.2433	49.8285
0.0093	25.0	45225	1.2487	50.2542
0.0084	26.0	47034	1.2557	49.7000
0.0087	27.0	48843	1.2869	50.2786
0.0081	28.0	50652	1.2925	50.4841
0.0069	29.0	52461	1.2634	49.8380
0.0074	30.0	54270	1.2974	49.3104
0.0063	31.0	56079	1.3415	49.7660
0.0067	32.0	57888	1.3139	49.2742
0.0059	33.0	59697	1.3681	50.0993
0.0061	34.0	61506	1.3476	50.2811
0.0064	35.0	63315	1.3631	50.2230
0.0058	36.0	65124	1.3328	50.6003
0.0053	37.0	66933	1.3731	49.7225
0.0057	38.0	68742	1.3806	49.8896
0.005	39.0	70551	1.3884	49.5702
0.0047	40.0	72360	1.3745	50.4731
0.0047	41.0	74169	1.3902	49.2447
0.0043	42.0	75978	1.4240	49.8482
0.0047	43.0	77787	1.3911	50.2552
0.0047	44.0	79596	1.3647	49.6567
0.0039	45.0	81405	1.4277	49.6737
0.0041	46.0	83214	1.4442	50.8941
0.0042	47.0	85023	1.4579	49.3731
0.004	48.0	86832	1.4214	50.9695
0.0041	49.0	88641	1.4520	50.0375
0.0036	50.0	90450	1.4542	49.8573
0.0041	51.0	92259	1.4235	49.8182
0.0034	52.0	94068	1.4811	50.0658
0.0036	53.0	95877	1.4689	50.9516
0.0032	54.0	97686	1.4741	50.4925
0.0031	55.0	99495	1.5038	50.6183
0.0033	56.0	101304	1.4595	50.9636
0.0032	57.0	103113	1.4815	50.3140
0.0029	58.0	104922	1.5145	51.0419
0.0032	59.0	106731	1.4924	50.1044
0.0029	60.0	108540	1.5090	50.2749
0.003	61.0	110349	1.5335	50.1858
0.003	62.0	112158	1.5249	49.8183
0.0026	63.0	113967	1.5167	49.4238
0.0024	64.0	115776	1.5502	49.5933
0.0031	65.0	117585	1.5294	49.9286
0.0028	66.0	119394	1.5548	50.0010
0.0027	67.0	121203	1.5403	49.7720
0.0027	68.0	123012	1.5486	50.7311
0.0027	69.0	124821	1.5524	50.6086
0.0032	70.0	126630	1.5356	49.8228
0.0024	71.0	128439	1.5496	50.4141
0.0027	72.0	130248	1.5870	49.7700
0.0024	73.0	132057	1.5617	50.1583
0.0026	74.0	133866	1.5719	50.4506
0.0024	75.0	135675	1.5845	50.0883
0.0023	76.0	137484	1.5840	51.0780
0.0026	77.0	139293	1.5889	50.5186
0.0025	78.0	141102	1.5981	50.1534
0.0021	79.0	142911	1.6026	50.0285
0.0023	80.0	144720	1.5834	50.0926
0.0022	81.0	146529	1.6145	50.0083
0.0025	82.0	148338	1.6056	50.4207
0.0023	83.0	150147	1.5991	50.0276
0.0023	84.0	151956	1.6080	50.0848
0.002	85.0	153765	1.6068	50.1176
0.002	86.0	155574	1.6170	49.7719
0.0019	87.0	157383	1.6356	50.2535
0.0021	88.0	159192	1.6251	50.4238
0.002	89.0	161001	1.6096	50.3078
0.0022	90.0	162810	1.6139	49.9777
0.0021	91.0	164619	1.6286	50.4731
0.0019	92.0	166428	1.6273	50.3567
0.0019	93.0	168237	1.6271	50.3870
0.0018	94.0	170046	1.6310	50.6999
0.0022	95.0	171855	1.6299	50.3114
0.0018	96.0	173664	1.6442	50.3675
0.0017	97.0	175473	1.6455	50.3591
0.0018	98.0	177282	1.6469	50.3269
0.0016	99.0	179091	1.6494	50.5726
0.0018	100.0	180900	1.6504	50.5918

Framework versions

Transformers 4.27.3
Pytorch 2.0.0
Datasets 2.10.1
Tokenizers 0.13.2

hrithikgupt/NLLB-en_tags-hi-HVG-2

NLLB-en_tags-hi-HVG-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hrithikgupt/
NLLB-en_tags-hi-HVG-2