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lilt-en-funsd-custom

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End of training

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  1. README.md +14 -60
  2. logs/events.out.tfevents.1685460269.MSI.3990.0 +2 -2
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README.md CHANGED
@@ -16,13 +16,13 @@ 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 mydata dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0000
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- - In: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}
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- - Ear: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}
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- - Overall Precision: 1.0
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- - Overall Recall: 1.0
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- - Overall F1: 1.0
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- - Overall Accuracy: 1.0
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  ## Model description
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@@ -47,63 +47,17 @@ The following hyperparameters were used during training:
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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: 2500
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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 | In | Ear | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:------:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 0.0001 | 25.0 | 50 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 50.0 | 100 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 75.0 | 150 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 100.0 | 200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 125.0 | 250 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 150.0 | 300 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 175.0 | 350 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 200.0 | 400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 225.0 | 450 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 250.0 | 500 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 275.0 | 550 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 300.0 | 600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 325.0 | 650 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 350.0 | 700 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 375.0 | 750 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 400.0 | 800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 425.0 | 850 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 450.0 | 900 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 475.0 | 950 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 500.0 | 1000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 525.0 | 1050 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 550.0 | 1100 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 575.0 | 1150 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 600.0 | 1200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 625.0 | 1250 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 650.0 | 1300 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 675.0 | 1350 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 700.0 | 1400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 725.0 | 1450 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 750.0 | 1500 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 775.0 | 1550 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 800.0 | 1600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 825.0 | 1650 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 850.0 | 1700 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 875.0 | 1750 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 900.0 | 1800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 925.0 | 1850 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 950.0 | 1900 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 975.0 | 1950 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1000.0 | 2000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1025.0 | 2050 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1050.0 | 2100 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1075.0 | 2150 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1100.0 | 2200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1125.0 | 2250 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1150.0 | 2300 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1175.0 | 2350 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1200.0 | 2400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1225.0 | 2450 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1250.0 | 2500 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | 1.0 | 1.0 | 1.0 | 1.0 |
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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 mydata dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0023
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+ - In: {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}
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+ - Ear: {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2}
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+ - Overall Precision: 0.6
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+ - Overall Recall: 0.75
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+ - Overall F1: 0.6667
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+ - Overall Accuracy: 0.9984
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  ## Model description
28
 
 
47
  - seed: 42
48
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
49
  - lr_scheduler_type: linear
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+ - training_steps: 200
51
  - mixed_precision_training: Native AMP
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53
  ### Training results
54
 
55
+ | Training Loss | Epoch | Step | Validation Loss | In | Ear | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.1628 | 25.0 | 50 | 0.0023 | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | 0.6 | 0.75 | 0.6667 | 0.9984 |
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+ | 0.0002 | 50.0 | 100 | 0.0015 | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | 0.6 | 0.75 | 0.6667 | 0.9984 |
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+ | 0.0001 | 75.0 | 150 | 0.0020 | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | 0.6 | 0.75 | 0.6667 | 0.9984 |
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+ | 0.0 | 100.0 | 200 | 0.0020 | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | 0.6 | 0.75 | 0.6667 | 0.9984 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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@@ -1,3 +1,3 @@
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