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quethrozar
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lilt-en-funsd-custom

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lilt
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quethrozar commited on
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End of training

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  1. README.md +13 -13
  2. logs/events.out.tfevents.1685069459.MSI.5457.11 +2 -2
  3. logs/events.out.tfevents.1685069580.MSI.5457.13 +3 -0
README.md CHANGED
@@ -16,9 +16,9 @@ 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: 0.0085
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- - In: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6}
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- - Ear: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6}
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  - Overall Precision: 1.0
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  - Overall Recall: 1.0
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  - Overall F1: 1.0
@@ -54,16 +54,16 @@ The following hyperparameters were used during training:
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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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- | 0.7649 | 3.33 | 10 | 0.0085 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.005 | 6.67 | 20 | 0.0004 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0004 | 10.0 | 30 | 0.0001 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0002 | 13.33 | 40 | 0.0001 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0001 | 16.67 | 50 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0001 | 20.0 | 60 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0001 | 23.33 | 70 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0001 | 26.67 | 80 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0001 | 30.0 | 90 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0001 | 33.33 | 100 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 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: 0.0193
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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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  | Training Loss | Epoch | Step | Validation Loss | In | Ear | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.6122 | 5.0 | 10 | 0.0193 | {'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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+ | 0.0028 | 10.0 | 20 | 0.0002 | {'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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+ | 0.0003 | 15.0 | 30 | 0.0001 | {'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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+ | 0.0001 | 20.0 | 40 | 0.0001 | {'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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+ | 0.0001 | 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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+ | 0.0001 | 30.0 | 60 | 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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+ | 0.0001 | 35.0 | 70 | 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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+ | 0.0001 | 40.0 | 80 | 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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+ | 0.0001 | 45.0 | 90 | 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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+ | 0.0001 | 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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  ### Framework versions
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