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Monster23
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layoutlm-funsd

Token Classification
Transformers
TensorBoard
Safetensors
layoutlm
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  1. README.md +15 -22
  2. logs/events.out.tfevents.1701939863.ysw.113000.0 +2 -2
  3. logs/events.out.tfevents.1701941436.ysw.125732.0 +3 -0
  4. logs/events.out.tfevents.1701941678.ysw.127616.0 +3 -0
  5. logs/events.out.tfevents.1701942067.ysw.130599.0 +3 -0
  6. logs/events.out.tfevents.1701942326.ysw.132428.0 +3 -0
  7. model.safetensors +1 -1
  8. training_args.bin +1 -1
README.md CHANGED
@@ -14,13 +14,16 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0668
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  - Number-a: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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  - Number-q: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
 
 
 
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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: 0.9848
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  ## Model description
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@@ -40,33 +43,23 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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- - train_batch_size: 16
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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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- - num_epochs: 15
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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 | Number-a | Number-q | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 1.1627 | 1.0 | 1 | 1.1422 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.2713 |
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- | 1.1655 | 2.0 | 2 | 1.1422 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.2713 |
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- | 1.1695 | 3.0 | 3 | 1.1422 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.2713 |
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- | 1.1661 | 4.0 | 4 | 0.8227 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.8093 |
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- | 0.8478 | 5.0 | 5 | 0.5718 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9744 |
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- | 0.5975 | 6.0 | 6 | 0.3821 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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- | 0.4052 | 7.0 | 7 | 0.2537 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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- | 0.2676 | 8.0 | 8 | 0.1673 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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- | 0.1775 | 9.0 | 9 | 0.1173 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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- | 0.1266 | 10.0 | 10 | 0.0942 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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- | 0.1017 | 11.0 | 11 | 0.0842 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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- | 0.0891 | 12.0 | 12 | 0.0786 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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- | 0.0845 | 13.0 | 13 | 0.0741 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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- | 0.0788 | 14.0 | 14 | 0.0702 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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- | 0.0763 | 15.0 | 15 | 0.0668 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.0 | 0.0 | 0.0 | 0.9848 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2568
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  - Number-a: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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  - Number-q: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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+ - Of-destination: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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+ - Of-loading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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+ - Tin: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}
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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: 0.9118
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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+ - train_batch_size: 20
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+ - eval_batch_size: 10
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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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+ - num_epochs: 5
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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 | Number-a | Number-q | Of-destination | Of-loading | Tin | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.8024 | 1.0 | 1 | 1.8275 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.013513513513513514, 'recall': 0.75, 'f1': 0.026548672566371685, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26} | 0.0051 | 0.0714 | 0.0095 | 0.1338 |
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+ | 1.8012 | 2.0 | 2 | 1.8275 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.013513513513513514, 'recall': 0.75, 'f1': 0.026548672566371685, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26} | 0.0051 | 0.0714 | 0.0095 | 0.1338 |
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+ | 1.7979 | 3.0 | 3 | 1.8275 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.013513513513513514, 'recall': 0.75, 'f1': 0.026548672566371685, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26} | 0.0051 | 0.0714 | 0.0095 | 0.1338 |
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+ | 1.8022 | 4.0 | 4 | 1.4916 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26} | 0.0 | 0.0 | 0.0 | 0.6594 |
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+ | 1.4811 | 5.0 | 5 | 1.2568 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26} | 0.0 | 0.0 | 0.0 | 0.9118 |
 
 
 
 
 
 
 
 
 
 
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