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

lilt-ruroberta

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2043
  • Comment: {'precision': 1.0, 'recall': 0.9444444444444444, 'f1': 0.9714285714285714, 'number': 18}
  • Date: {'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 8}
  • Labname: {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5}
  • Laboratory: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
  • Measure: {'precision': 1.0, 'recall': 0.9230769230769231, 'f1': 0.9600000000000001, 'number': 13}
  • Ref Value: {'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'number': 14}
  • Result: {'precision': 1.0, 'recall': 0.9285714285714286, 'f1': 0.962962962962963, 'number': 14}
  • Overall Precision: 0.9296
  • Overall Recall: 0.8919
  • Overall F1: 0.9103
  • Overall Accuracy: 0.9563

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
1.2584 5.0 5 0.9810 {'precision': 1.0, 'recall': 0.05555555555555555, 'f1': 0.10526315789473684, 'number': 18} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} {'precision': 0.6666666666666666, 'recall': 0.3076923076923077, 'f1': 0.42105263157894735, 'number': 13} {'precision': 0.5714285714285714, 'recall': 0.2857142857142857, 'f1': 0.38095238095238093, 'number': 14} {'precision': 0.4482758620689655, 'recall': 0.9285714285714286, 'f1': 0.6046511627906977, 'number': 14} 0.44 0.2973 0.3548 0.7125
0.6614 10.0 10 0.5382 {'precision': 0.8947368421052632, 'recall': 0.9444444444444444, 'f1': 0.918918918918919, 'number': 18} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} {'precision': 0.8333333333333334, 'recall': 0.38461538461538464, 'f1': 0.5263157894736842, 'number': 13} {'precision': 0.8125, 'recall': 0.9285714285714286, 'f1': 0.8666666666666666, 'number': 14} {'precision': 1.0, 'recall': 0.7857142857142857, 'f1': 0.88, 'number': 14} 0.8475 0.6757 0.7519 0.9
0.3955 15.0 15 0.3360 {'precision': 0.8947368421052632, 'recall': 0.9444444444444444, 'f1': 0.918918918918919, 'number': 18} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} {'precision': 0.8333333333333334, 'recall': 0.38461538461538464, 'f1': 0.5263157894736842, 'number': 13} {'precision': 0.8125, 'recall': 0.9285714285714286, 'f1': 0.8666666666666666, 'number': 14} {'precision': 1.0, 'recall': 0.7857142857142857, 'f1': 0.88, 'number': 14} 0.8475 0.6757 0.7519 0.9
0.2654 20.0 20 0.2405 {'precision': 1.0, 'recall': 0.8888888888888888, 'f1': 0.9411764705882353, 'number': 18} {'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 8} {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} {'precision': 1.0, 'recall': 0.9230769230769231, 'f1': 0.9600000000000001, 'number': 13} {'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'number': 14} {'precision': 0.9285714285714286, 'recall': 0.9285714285714286, 'f1': 0.9285714285714286, 'number': 14} 0.9155 0.8784 0.8966 0.95
0.2125 25.0 25 0.2043 {'precision': 1.0, 'recall': 0.9444444444444444, 'f1': 0.9714285714285714, 'number': 18} {'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 8} {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} {'precision': 1.0, 'recall': 0.9230769230769231, 'f1': 0.9600000000000001, 'number': 13} {'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'number': 14} {'precision': 1.0, 'recall': 0.9285714285714286, 'f1': 0.962962962962963, 'number': 14} 0.9296 0.8919 0.9103 0.9563

Framework versions

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