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Shrimalip/
mbart-large-50-many-to-many-mmt-finetuned-hi-to-en

Name: mbart-large-50-many-to-many-mmt-finetuned-hi-to-en
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mbart-large-50-many-to-many-mmt-finetuned-hi-to-en

This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on the None dataset. It achieves the following results on the evaluation set:

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The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Gen Len
2.4071	0.07	500	2.4856	5.49
2.6727	0.13	1000	2.3521	5.4733
2.6438	0.2	1500	2.2782	5.38
2.5672	0.27	2000	2.2432	5.6367
2.5561	0.34	2500	2.0945	5.4733
2.5502	0.4	3000	2.0506	5.4667
2.5326	0.47	3500	2.0163	5.4367
2.4979	0.54	4000	1.9795	5.4267
2.4771	0.61	4500	1.9002	5.28
2.5121	0.67	5000	1.8497	5.5433
2.4568	0.74	5500	1.7447	5.66
2.4211	0.81	6000	1.6948	5.5633
2.4702	0.88	6500	1.5632	5.4267
2.4161	0.94	7000	1.5122	5.5633
2.2322	1.01	7500	1.3694	5.45
1.5244	1.08	8000	1.3296	5.55
1.5234	1.14	8500	1.2637	5.5267
1.5902	1.21	9000	1.2621	5.6133
1.4645	1.28	9500	1.1856	5.55
1.5426	1.35	10000	1.1528	5.6267
1.5414	1.41	10500	1.1533	5.6067
1.4708	1.48	11000	1.1136	5.5533
1.4625	1.55	11500	1.0736	5.54
1.5805	1.62	12000	1.0700	5.5733
1.5295	1.68	12500	1.0171	5.4867
1.5585	1.75	13000	0.9770	5.6133
1.5602	1.82	13500	0.9106	5.5867
1.5361	1.89	14000	0.8823	5.5333
1.5217	1.95	14500	0.8353	5.5167
1.3229	2.02	15000	0.7296	5.4967
0.916	2.09	15500	0.6979	5.56
0.9075	2.15	16000	0.6797	5.5333
0.8575	2.22	16500	0.6372	5.52
0.8702	2.29	17000	0.6288	5.4967
0.8741	2.36	17500	0.6297	5.5033
0.8764	2.42	18000	0.6038	5.5233
0.9152	2.49	18500	0.5853	5.4933
0.8598	2.56	19000	0.5731	5.54
0.8586	2.63	19500	0.5558	5.5533
0.9005	2.69	20000	0.5388	5.5233
0.889	2.76	20500	0.5269	5.5067
0.8772	2.83	21000	0.5301	5.5333
0.8713	2.9	21500	0.5208	5.5567
0.8962	2.96	22000	0.5182	5.5567