mbart-large-50-many-to-many-mmt-ICFOSS-Malayalam_English_Translation

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: 1.3733
• Bleu: 28.9041
• Rouge: {'rouge1': 0.6211709615166336, 'rouge2': 0.3817538086155071, 'rougeL': 0.5654819931253774, 'rougeLsum': 0.5656455299372645}
• Chrf: {'score': 56.252579884228325, 'char_order': 6, 'word_order': 0, 'beta': 2}

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: 0.0002
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Rouge	Chrf
1.5329	1.0	4700	1.4284	27.0756	{'rouge1': 0.6054918604734425, 'rouge2': 0.36327221325964765, 'rougeL': 0.5490261054453232, 'rougeLsum': 0.5491186003413475}	{'score': 54.690919979551, 'char_order': 6, 'word_order': 0, 'beta': 2}
1.4295	2.0	9400	1.3924	28.2063	{'rouge1': 0.614973366544844, 'rouge2': 0.373550100507563, 'rougeL': 0.5589026806041284, 'rougeLsum': 0.5589661976445393}	{'score': 55.635529686949894, 'char_order': 6, 'word_order': 0, 'beta': 2}
1.3942	3.0	14100	1.3792	28.5831	{'rouge1': 0.6187502745206666, 'rouge2': 0.37919936984407143, 'rougeL': 0.5626864397042893, 'rougeLsum': 0.5627150169042504}	{'score': 56.019161628219024, 'char_order': 6, 'word_order': 0, 'beta': 2}
1.3795	4.0	18800	1.3759	28.7523	{'rouge1': 0.620515288235373, 'rouge2': 0.38072092563685545, 'rougeL': 0.5644953116677603, 'rougeLsum': 0.5646285495158272}	{'score': 56.162861197192925, 'char_order': 6, 'word_order': 0, 'beta': 2}
1.3723	5.0	23500	1.3735	28.8675	{'rouge1': 0.6225302294049915, 'rouge2': 0.382440202243451, 'rougeL': 0.5664785907343486, 'rougeLsum': 0.5666347228887372}	{'score': 56.30835530151895, 'char_order': 6, 'word_order': 0, 'beta': 2}
1.3684	6.0	28200	1.3731	28.8915	{'rouge1': 0.6214787732761883, 'rouge2': 0.3815472818692578, 'rougeL': 0.5656767538045446, 'rougeLsum': 0.5657190870277087}	{'score': 56.251600472693866, 'char_order': 6, 'word_order': 0, 'beta': 2}
1.3685	7.0	32900	1.3732	28.8953	{'rouge1': 0.6216361131555139, 'rouge2': 0.3821354228713412, 'rougeL': 0.5655300849639422, 'rougeLsum': 0.565595149126267}	{'score': 56.26874870012928, 'char_order': 6, 'word_order': 0, 'beta': 2}
1.3678	8.0	37600	1.3733	28.9041	{'rouge1': 0.6211709615166336, 'rouge2': 0.3817538086155071, 'rougeL': 0.5654819931253774, 'rougeLsum': 0.5656455299372645}	{'score': 56.252579884228325, 'char_order': 6, 'word_order': 0, 'beta': 2}

Framework versions

• PEFT 0.10.0
• Transformers 4.39.3
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.0