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End of training

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README.md ADDED
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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-synth3
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+ results: []
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+ ---
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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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+
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+ # layoutlm-synth3
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+
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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: 0.0021
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+ - Ank Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
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+ - Ank Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
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+ - Ayee Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
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+ - Ayee Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
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+ - Icr: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
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+ - Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 8
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+ - eval_batch_size: 6
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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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+
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+ ### Training results
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+
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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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+ | 0.9365 | 1.0 | 20 | 0.1057 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 0.9487179487179487, 'recall': 0.9487179487179487, 'f1': 0.9487179487179487, 'number': 39} | {'precision': 0.9487179487179487, 'recall': 0.9487179487179487, 'f1': 0.9487179487179487, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 0.9829 | 0.9829 | 0.9829 | 0.9976 |
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+ | 0.0449 | 2.0 | 40 | 0.0058 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0075 | 3.0 | 60 | 0.0028 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.005 | 4.0 | 80 | 0.0022 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0042 | 5.0 | 100 | 0.0021 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0 | 1.0 | 1.0 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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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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