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metadata
license: mit
base_model: facebook/m2m100_418M
tags:
  - generated_from_trainer
metrics:
  - bleu
model-index:
  - name: m2m100_418M-finetuned-en-to-hi
    results: []

m2m100_418M-finetuned-en-to-hi

This model is a fine-tuned version of facebook/m2m100_418M on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0453
  • Bleu: 17.4993
  • Gen Len: 6.7284

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: 48
  • eval_batch_size: 48
  • 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
2.4274 0.16 500 2.1152 4.4935 6.8813
2.1915 0.33 1000 1.9722 5.8486 6.9727
2.1187 0.49 1500 1.8575 5.5802 6.9993
2.0151 0.66 2000 1.7686 8.8892 6.8233
1.9709 0.82 2500 1.6948 8.4082 6.8809
1.9376 0.99 3000 1.6341 10.0801 6.85
1.761 1.15 3500 1.5788 8.1916 6.8816
1.7269 1.32 4000 1.5380 10.2779 6.9447
1.7231 1.48 4500 1.4946 6.9244 6.9402
1.6925 1.65 5000 1.4456 13.7246 6.9018
1.6658 1.81 5500 1.4146 9.1181 6.9104
1.6673 1.98 6000 1.3727 8.6535 6.8682
1.5165 2.14 6500 1.3441 14.8146 6.9804
1.5111 2.31 7000 1.3101 11.192 6.92
1.4889 2.47 7500 1.2814 11.8364 6.9509
1.4903 2.64 8000 1.2510 16.8035 6.9316
1.4871 2.8 8500 1.2298 14.5766 6.9053
1.4854 2.97 9000 1.2051 14.2822 6.8438
1.3719 3.13 9500 1.1758 16.1779 6.8918
1.3481 3.3 10000 1.1612 20.1789 6.8138
1.3585 3.46 10500 1.1410 15.6937 6.8613
1.35 3.63 11000 1.1261 20.0808 6.832
1.3557 3.79 11500 1.1069 19.588 6.8242
1.3329 3.96 12000 1.0924 19.9913 6.796
1.2792 4.12 12500 1.0791 18.8275 6.7616
1.2568 4.29 13000 1.0701 16.7189 6.7676
1.2558 4.45 13500 1.0605 18.7687 6.7464
1.2533 4.62 14000 1.0541 19.1818 6.7693
1.2559 4.78 14500 1.0475 19.0462 6.738
1.2513 4.95 15000 1.0453 17.4993 6.7284

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0