lilt-ruroberta / README.md
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metadata
tags:
  - generated_from_trainer
model-index:
  - name: lilt-ruroberta
    results: []

lilt-ruroberta

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4919
  • Comment: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}
  • Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
  • Labname: {'precision': 0.5833333333333334, 'recall': 0.6666666666666666, 'f1': 0.6222222222222222, 'number': 21}
  • Laboratory: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
  • Measure: {'precision': 0.5833333333333334, 'recall': 0.7777777777777778, 'f1': 0.6666666666666666, 'number': 9}
  • Ref Value: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
  • Result: {'precision': 0.25, 'recall': 0.25, 'f1': 0.25, 'number': 12}
  • Overall Precision: 0.4528
  • Overall Recall: 0.4
  • Overall F1: 0.4248
  • Overall Accuracy: 0.8698

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-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
  • training_steps: 25
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Comment Date Labname Laboratory Measure Ref Value Result Overall Precision Overall Recall Overall F1 Overall Accuracy
2.4398 5.0 5 1.5928 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.5850
1.4788 10.0 10 1.1857 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.6512
0.9806 15.0 15 0.8188 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.21875, 'recall': 0.3333333333333333, 'f1': 0.2641509433962264, 'number': 21} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} {'precision': 0.5, 'recall': 0.1111111111111111, 'f1': 0.1818181818181818, 'number': 9} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.1667 0.1333 0.1481 0.7660
0.6358 20.0 20 0.5763 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.41935483870967744, 'recall': 0.6190476190476191, 'f1': 0.5, 'number': 21} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} {'precision': 0.7, 'recall': 0.7777777777777778, 'f1': 0.7368421052631577, 'number': 9} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} {'precision': 0.42857142857142855, 'recall': 0.25, 'f1': 0.3157894736842105, 'number': 12} 0.4182 0.3833 0.4 0.8675
0.4712 25.0 25 0.4919 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.5833333333333334, 'recall': 0.6666666666666666, 'f1': 0.6222222222222222, 'number': 21} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} {'precision': 0.5833333333333334, 'recall': 0.7777777777777778, 'f1': 0.6666666666666666, 'number': 9} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} {'precision': 0.25, 'recall': 0.25, 'f1': 0.25, 'number': 12} 0.4528 0.4 0.4248 0.8698

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.12.1
  • Datasets 2.8.0
  • Tokenizers 0.13.2