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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
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