mnavas / roberta-finetuned-solvencia-v1

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Total runs: 1
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7-day runs: 0
30-day runs: -2
Model's Last Updated: April 12 2023
text-classification

Introduction of roberta-finetuned-solvencia-v1

Model Details of roberta-finetuned-solvencia-v1

roberta-finetuned-solvencia-v1

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: 0.4141
  • Accuracy: 0.8919
  • F1: 0.8919
  • Precision: 0.8919
  • Recall: 0.8919
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
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
Training results
Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 333 0.3781 0.8544 0.8544 0.8544 0.8544
0.4429 2.0 666 0.3295 0.8679 0.8679 0.8679 0.8679
0.4429 3.0 999 0.3664 0.8784 0.8784 0.8784 0.8784
0.3512 4.0 1332 0.4602 0.8649 0.8649 0.8649 0.8649
0.2975 5.0 1665 0.4721 0.8889 0.8889 0.8889 0.8889
0.2975 6.0 1998 0.4141 0.8919 0.8919 0.8919 0.8919
0.2499 7.0 2331 0.4054 0.8889 0.8889 0.8889 0.8889
0.2132 8.0 2664 0.4878 0.8829 0.8829 0.8829 0.8829
0.2132 9.0 2997 0.4867 0.8904 0.8904 0.8904 0.8904
0.1812 10.0 3330 0.5339 0.8889 0.8889 0.8889 0.8889
Framework versions
  • Transformers 4.16.2
  • Pytorch 1.9.1
  • Datasets 1.18.4
  • Tokenizers 0.11.6

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