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

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

generated_from_trainer

facebook/mbart-large-50-many-to-many-mmt

finetune

endpoints_compatible

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

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:

Loss: 1.7540
Bleu: 9.5451
Gen Len: 6.3699

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
3.2113	0.0	500	2.8535	4.975	6.0562
2.8197	0.01	1000	2.6902	3.2399	6.2449
2.6595	0.01	1500	2.6256	1.2488	6.5596
2.679	0.01	2000	2.5394	4.8497	6.1644
2.5408	0.02	2500	2.4751	2.294	6.3051
2.4689	0.02	3000	2.4052	3.6526	6.3714
2.4299	0.02	3500	2.3642	3.4783	6.4584
2.4303	0.02	4000	2.3545	4.9837	6.343
2.3715	0.03	4500	2.3138	5.3665	6.1981
2.3354	0.03	5000	2.3002	5.8659	6.109
2.3734	0.03	5500	2.2877	3.8468	6.443
2.2968	0.04	6000	2.2385	3.1561	6.4381
2.272	0.04	6500	2.2269	5.7378	6.1155
2.2691	0.04	7000	2.2244	7.8318	6.2325
2.2333	0.05	7500	2.1973	5.4549	6.1463
2.2518	0.05	8000	2.1996	2.3346	6.4954
2.251	0.05	8500	2.1682	4.7228	6.4063
2.252	0.06	9000	2.2211	5.7043	6.3745
2.288	0.06	9500	2.2017	3.7285	6.4893
2.2443	0.06	10000	2.1300	7.0869	6.4448
2.1723	0.06	10500	2.1076	4.3789	6.3482
2.1371	0.07	11000	2.1024	4.2024	6.547
2.1206	0.07	11500	2.1045	7.2662	6.1786
2.1325	0.07	12000	2.0903	3.8746	6.344
2.1315	0.08	12500	2.0817	5.9713	6.4415
2.1285	0.08	13000	2.0637	7.7832	6.1534
2.1223	0.08	13500	2.0522	2.9569	6.313
2.1036	0.09	14000	2.0505	5.0732	6.3308
2.1053	0.09	14500	2.0288	6.2772	6.1638
2.1122	0.09	15000	2.0232	6.8311	6.2005
2.0566	0.1	15500	2.0175	5.8541	6.1907
2.0783	0.1	16000	2.0147	10.0926	6.1531
2.0775	0.1	16500	2.0128	7.6705	6.2624
2.0952	0.1	17000	1.9951	5.5904	6.2104
2.115	0.11	17500	1.9806	8.0092	6.2081
2.0515	0.11	18000	1.9769	5.9444	6.2055
2.0698	0.11	18500	1.9611	8.6585	6.2591
2.0521	0.12	19000	1.9715	9.1678	6.2758
2.0581	0.12	19500	1.9538	7.0038	6.2019
2.0073	0.12	20000	1.9502	7.6102	6.3093
2.0104	0.13	20500	1.9414	7.7584	6.1554
2.0163	0.13	21000	1.9404	5.8758	6.3561
2.03	0.13	21500	1.9294	6.9283	6.1343
1.9915	0.14	22000	1.9159	5.6757	6.2349
2.0158	0.14	22500	1.9234	7.5094	6.1197
1.9616	0.14	23000	1.9170	9.4006	6.2105
1.9954	0.14	23500	1.9008	2.9622	6.2355
2.0116	0.15	24000	1.9026	11.0333	6.0291
1.9742	0.15	24500	1.8973	4.504	6.3386
1.9805	0.15	25000	1.8955	3.8655	6.2335
1.9413	0.16	25500	1.8821	8.9818	6.1769
1.9311	0.16	26000	1.8851	6.7291	6.0846
1.9696	0.16	26500	1.8789	12.1041	6.1274
1.9419	0.17	27000	1.8687	7.2389	6.2407
1.959	0.17	27500	1.8688	7.984	6.3319
1.9449	0.17	28000	1.8655	6.7646	6.2376
1.961	0.18	28500	1.8541	9.8683	6.2369
1.9293	0.18	29000	1.8676	8.2689	6.0779
1.978	0.18	29500	1.8515	5.7599	6.1964
1.9121	0.18	30000	1.8508	7.8691	6.19
1.9566	0.19	30500	1.8350	7.7093	6.1696
1.9279	0.19	31000	1.8455	7.2261	6.2585
1.9717	0.19	31500	1.8374	8.7243	6.2427
1.9215	0.2	32000	1.8239	3.5888	6.242
1.937	0.2	32500	1.8352	3.352	6.2681
1.9103	0.2	33000	1.8260	7.5665	6.2443
1.9363	0.21	33500	1.8122	8.6132	6.1723
1.8938	0.21	34000	1.8175	8.49	6.3157
1.8869	0.21	34500	1.8156	7.8958	6.3069
1.9113	0.22	35000	1.8083	5.658	6.2682
1.9175	0.22	35500	1.8012	9.5439	6.3022
1.9283	0.22	36000	1.8032	9.1064	6.3756
1.9227	0.22	36500	1.7899	8.7293	6.2953
1.9129	0.23	37000	1.7822	4.2586	6.2276
1.8733	0.23	37500	1.7789	9.1095	6.2261
1.8986	0.23	38000	1.7831	4.853	6.2544
1.8655	0.24	38500	1.7762	7.4264	6.3151
1.8996	0.24	39000	1.7648	7.4422	6.3657
1.8771	0.24	39500	1.7690	12.3696	6.1305
1.8719	0.25	40000	1.7565	10.0389	6.2719
1.8993	0.25	40500	1.7481	14.4891	6.2271
1.8756	0.25	41000	1.7516	7.2393	6.2737
1.8195	0.26	41500	1.7534	9.0375	6.2557
1.8384	0.26	42000	1.7540	9.5451	6.3699

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

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

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

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