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ribesstefano/RuleBert-v0.5-k1

sourceHugging Facemitupdated 3y agoView on Hugging Face
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ribesstefano/RuleBert-v0.5-k1

This model is a fine-tuned version of papluca/xlm-roberta-base-language-detection on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.3067
  • —F1: 0.4861
  • —Roc Auc: 0.6679
  • —Accuracy: 0.0

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-06
  • —trainbatchsize: 2
  • —evalbatchsize: 64
  • —seed: 42
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —training_steps: 8000
  • —mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation LossF1Roc AucAccuracy
0.41510.062500.36580.48610.66790.0
0.36370.135000.32090.48610.66790.0
0.33680.197500.30930.48610.66790.0
0.33350.2510000.30810.48610.66790.0
0.35240.3112500.30630.48610.66790.0
0.36190.3815000.30670.48610.66790.0

Framework versions

  • —Transformers 4.36.2
  • —Pytorch 2.1.0+cu121
  • —Datasets 2.16.1
  • —Tokenizers 0.15.0