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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: layoutlm-synthchecking-padding |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlm-synthchecking-padding |
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This model is a fine-tuned version of [microsoft/layoutlm-large-uncased](https://huggingface.co/microsoft/layoutlm-large-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0005 |
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- Ank Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} |
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- Ank Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} |
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- Ayee Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} |
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- Ayee Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} |
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- Icr: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} |
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- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 2 |
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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: 8 |
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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 | Ank Address | Ank Name | Ayee Address | Ayee Name | Icr | Mount | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 1.3656 | 1.0 | 30 | 0.8294 | {'precision': 0.17721518987341772, 'recall': 0.4666666666666667, 'f1': 0.25688073394495414, 'number': 30} | {'precision': 0.23076923076923078, 'recall': 0.1, 'f1': 0.13953488372093023, 'number': 30} | {'precision': 0.011235955056179775, 'recall': 0.03333333333333333, 'f1': 0.01680672268907563, 'number': 30} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 0.2989 | 0.4333 | 0.3537 | 0.7804 | |
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| 0.418 | 2.0 | 60 | 0.0552 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 0.9666666666666667, 'recall': 0.9666666666666667, 'f1': 0.9666666666666667, 'number': 30} | {'precision': 0.9666666666666667, 'recall': 0.9666666666666667, 'f1': 0.9666666666666667, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 0.9889 | 0.9889 | 0.9889 | 0.9984 | |
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| 0.033 | 3.0 | 90 | 0.0022 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0056 | 4.0 | 120 | 0.0010 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0032 | 5.0 | 150 | 0.0007 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 6.0 | 180 | 0.0006 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0028 | 7.0 | 210 | 0.0005 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0022 | 8.0 | 240 | 0.0005 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.27.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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