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DACSG/modelo

sourceHugging Facemitupdated 4mo agoView on Hugging Face
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modelo

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

  • Loss: 0.5213
  • Accuracy: 0.8766
  • F1 Macro: 0.8405
  • Precision: 0.8444
  • Recall: 0.8435

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: 8
  • evalbatchsize: 8
  • 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
  • num_epochs: 4
  • mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation LossAccuracyF1 MacroPrecisionRecall
0.82201.05840.59880.79690.75300.76670.7523
0.50982.011680.43230.87830.83240.86330.8104
0.43983.017520.55610.87400.83780.83700.8427
0.32604.023360.52130.87660.84050.84440.8435

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2