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felipe-nextly/text-classification-medical

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
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Model Card

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text-classification-medical

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

  • —Loss: 0.0394
  • —Accuracy: 1.0
  • —F1: 1.0

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: 2e-05
  • —trainbatchsize: 16
  • —evalbatchsize: 16
  • —seed: 42
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —num_epochs: 10

Training results

Training LossEpochStepValidation LossAccuracyF1
No log1.0130.64090.57140.7273
No log2.0260.53850.82970.8397
No log3.0390.33460.92860.9293
No log4.0520.19790.97800.9781
No log5.0650.13210.99450.9945
No log6.0780.09321.01.0
No log7.0910.06541.01.0
No log8.01040.05081.01.0
No log9.01170.04201.01.0
No log10.01300.03941.01.0

Framework versions

  • —Transformers 4.36.2
  • —Pytorch 2.1.0+cu121
  • —Datasets 2.15.0
  • —Tokenizers 0.15.0