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dv347/grammar-classifier

sourceHugging Facemitupdated 7mo agoView on Hugging Face
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grammar-classifier

This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 4.0967
  • Exact Match: 0.0
  • Micro F1: 0.3075
  • Macro F1: 0.0334
  • Hamming Accuracy: 0.8806
  • Avg Pred Positives: 34.0
  • Avg Gold Positives: 13.5736

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
  • trainbatchsize: 32
  • evalbatchsize: 64
  • seed: 42
  • gradientaccumulationsteps: 2
  • totaltrainbatch_size: 64
  • optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lrschedulertype: cosine
  • lrschedulerwarmup_steps: 0.2
  • num_epochs: 10

Training results

Training LossEpochStepValidation LossExact MatchMicro F1Macro F1Hamming AccuracyAvg Pred PositivesAvg Gold Positives
0.29300.51640.56630.00.31390.02880.904125.013.5736
0.19281.03280.27200.00.43920.02560.946013.013.5736
0.07511.54920.05590.00.52440.02340.96288.013.5736
33.35992.065613.52650.00.29310.02260.911421.013.5736
19.08592.582010.58640.00.22140.03020.837644.013.5736
8.61453.09844.09670.00.30750.03340.880634.013.5736

Framework versions

  • Transformers 5.2.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.5.0
  • Tokenizers 0.22.2