CoolFace
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HamidBekam/g-patentsberta-e2e

sourceHugging Faceupdated 7mo agoView on Hugging Face
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Model Card

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g-patentsberta-e2e

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

  • Loss: 0.4002
  • Accuracy: 0.8255
  • Precision: 0.2789
  • Recall: 0.8247
  • F1: 0.4168

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: 16
  • evalbatchsize: 64
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lrschedulertype: linear
  • lrschedulerwarmup_steps: 0.06
  • num_epochs: 1.0
  • mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation LossAccuracyPrecisionRecallF1
0.45340.186620000.43170.79880.79850.78990.7942
0.42120.373140000.43590.80430.85730.72180.7837
0.40950.559760000.41600.81570.80040.83250.8161
0.39920.746380000.40390.82100.82550.80610.8157
0.38280.9328100000.39130.82410.81790.82600.8219

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.9.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2