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End of training

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README.md CHANGED
@@ -14,18 +14,18 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.7493
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- - Comment: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
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- - Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}
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- - Labname: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18}
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- - Laboratory: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
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- - Measure: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
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- - Ref Value: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}
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- - Result: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
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- - Overall Precision: 0.0
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- - Overall Recall: 0.0
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- - Overall F1: 0.0
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- - Overall Accuracy: 0.375
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  ## Model description
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@@ -45,28 +45,23 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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- - train_batch_size: 1
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- - eval_batch_size: 1
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - training_steps: 10
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Comment | Date | Labname | Laboratory | Measure | Ref Value | Result | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 2.6158 | 0.5 | 1 | 2.6467 | {'precision': 0.06666666666666667, 'recall': 0.375, 'f1': 0.11320754716981134, 'number': 8} | {'precision': 0.13333333333333333, 'recall': 0.17391304347826086, 'f1': 0.15094339622641512, 'number': 23} | {'precision': 0.16666666666666666, 'recall': 0.1111111111111111, 'f1': 0.13333333333333333, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0657 | 0.1324 | 0.0878 | 0.0375 |
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- | 2.6704 | 1.0 | 2 | 2.6467 | {'precision': 0.06666666666666667, 'recall': 0.375, 'f1': 0.11320754716981134, 'number': 8} | {'precision': 0.13333333333333333, 'recall': 0.17391304347826086, 'f1': 0.15094339622641512, 'number': 23} | {'precision': 0.16666666666666666, 'recall': 0.1111111111111111, 'f1': 0.13333333333333333, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0657 | 0.1324 | 0.0878 | 0.0375 |
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- | 2.6164 | 1.5 | 3 | 2.6467 | {'precision': 0.06666666666666667, 'recall': 0.375, 'f1': 0.11320754716981134, 'number': 8} | {'precision': 0.13333333333333333, 'recall': 0.17391304347826086, 'f1': 0.15094339622641512, 'number': 23} | {'precision': 0.16666666666666666, 'recall': 0.1111111111111111, 'f1': 0.13333333333333333, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0657 | 0.1324 | 0.0878 | 0.0375 |
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- | 2.6707 | 2.0 | 4 | 2.2168 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0 | 0.0 | 0.0 | 0.375 |
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- | 1.8689 | 2.5 | 5 | 2.1469 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0 | 0.0 | 0.0 | 0.375 |
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- | 2.1588 | 3.0 | 6 | 1.9684 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0 | 0.0 | 0.0 | 0.375 |
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- | 1.0594 | 3.5 | 7 | 2.0123 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0 | 0.0 | 0.0 | 0.375 |
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- | 2.0705 | 4.0 | 8 | 1.8896 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0 | 0.0 | 0.0 | 0.375 |
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- | 1.9698 | 4.5 | 9 | 1.7493 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0 | 0.0 | 0.0 | 0.375 |
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- | 0.8502 | 5.0 | 10 | 1.6972 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.0 | 0.0 | 0.0 | 0.375 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2043
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+ - Comment: {'precision': 1.0, 'recall': 0.9444444444444444, 'f1': 0.9714285714285714, 'number': 18}
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+ - Date: {'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 8}
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+ - Labname: {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5}
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+ - Laboratory: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
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+ - Measure: {'precision': 1.0, 'recall': 0.9230769230769231, 'f1': 0.9600000000000001, 'number': 13}
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+ - Ref Value: {'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'number': 14}
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+ - Result: {'precision': 1.0, 'recall': 0.9285714285714286, 'f1': 0.962962962962963, 'number': 14}
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+ - Overall Precision: 0.9296
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+ - Overall Recall: 0.8919
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+ - Overall F1: 0.9103
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+ - Overall Accuracy: 0.9563
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - training_steps: 25
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Comment | Date | Labname | Laboratory | Measure | Ref Value | Result | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
 
 
 
 
 
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  ### Framework versions
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