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TURNA_spell_correction_general_turkish

Text2Text Generation
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metadata
license: other
tags:
  - generated_from_trainer
base_model: boun-tabi-LMG/TURNA
metrics:
  - rouge
  - bleu
model-index:
  - name: TURNA_spell_correction_general_turkish
    results: []

TURNA_spell_correction_general_turkish

This model is a fine-tuned version of boun-tabi-LMG/TURNA on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1739
  • Rouge1: 0.6731
  • Rouge2: 0.3509
  • Rougel: 0.6734
  • Rougelsum: 0.6734
  • Bleu: 0.0
  • Precisions: [0.724791169451074, 0.875, 0.9080188679245284, 0.0]
  • Brevity Penalty: 0.9975
  • Length Ratio: 0.9975
  • Translation Length: 6704
  • Reference Length: 6721
  • Meteor: 0.4269
  • Score: 27.9423
  • Num Edits: 1878
  • Ref Length: 6721.0

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: 5e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Bleu Precisions Brevity Penalty Length Ratio Translation Length Reference Length Meteor Score Num Edits Ref Length
No log 0.3334 1407 0.3554 0.5067 0.2640 0.5068 0.5068 0.0 [0.5543951692734113, 0.681206764027671, 0.620919881305638, 0.0] 1.0 1.0039 20204 20126 0.3130 45.8213 9222 20126.0
No log 0.6668 2814 0.2851 0.5589 0.2938 0.5590 0.5591 0.0 [0.612387263939409, 0.7749062931544684, 0.7614457831325301, 0.0] 0.9972 0.9972 20069 20126 0.3509 39.5906 7968 20126.0
0.5458 1.0002 4221 0.2465 0.5908 0.3104 0.5914 0.5912 0.0 [0.6474730323611666, 0.8186703821656051, 0.8253452477660439, 0.0] 0.9949 0.9949 20024 20126 0.3739 36.0330 7252 20126.0
0.5458 1.3336 5628 0.2302 0.6130 0.3223 0.6133 0.6135 0.0 [0.6655887766777773, 0.8296412468143501, 0.8471337579617835, 0.0] 0.9988 0.9988 20101 20126 0.3876 34.1002 6863 20126.0
0.5458 1.6671 7035 0.2101 0.6242 0.3262 0.6241 0.6246 0.0 [0.6759966072943172, 0.8334324806662701, 0.8369652945924132, 0.0] 0.9959 0.9959 20043 20126 0.3936 33.0965 6661 20126.0
0.1935 2.0005 8442 0.1931 0.6498 0.3415 0.6501 0.6502 0.0 [0.6969411063660741, 0.8487738419618529, 0.8557993730407524, 0.0] 1.0 1.0006 20138 20126 0.4097 30.8457 6208 20126.0
0.1935 2.3339 9849 0.1939 0.6500 0.3443 0.6502 0.6502 0.0 [0.7011294094233544, 0.8635026475779565, 0.8777602523659306, 0.0] 0.9987 0.9987 20099 20126 0.4120 30.3886 6116 20126.0
0.1935 2.6673 11256 0.1864 0.6615 0.3467 0.6615 0.6621 0.0 [0.7094416546512206, 0.8591824760414629, 0.8735271013354281, 0.0] 0.9994 0.9994 20113 20126 0.4178 29.5439 5946 20126.0
0.1135 3.0007 12663 0.1746 0.6729 0.3488 0.6734 0.6735 0.0 [0.7206584444002387, 0.8674628034455756, 0.8738244514106583, 0.0] 0.9991 0.9991 20108 20126 0.4253 28.3961 5715 20126.0
0.1135 3.3341 14070 0.1846 0.6750 0.3540 0.6753 0.6753 0.0 [0.7217659648598875, 0.8744843842074249, 0.8928571428571429, 0.0] 0.9983 0.9983 20091 20126 0.4260 28.2918 5694 20126.0
0.1135 3.6675 15477 0.1806 0.6827 0.3566 0.6830 0.6829 0.0 [0.7288599283724632, 0.8759796238244514, 0.8909090909090909, 0.0] 0.9989 0.9989 20104 20126 0.4308 27.5465 5544 20126.0
0.0672 4.0009 16884 0.1758 0.6872 0.3617 0.6873 0.6874 0.0 [0.7345353122820998, 0.8853879480110279, 0.9000793021411578, 0.0] 0.9976 0.9976 20078 20126 0.4341 26.9701 5428 20126.0

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1