--- tags: - generated_from_trainer model-index: - name: layoutlm-synthchecking-padding results: [] --- # layoutlm-synthchecking-padding This model is a fine-tuned version of [microsoft/layoutlm-large-uncased](https://huggingface.co/microsoft/layoutlm-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0005 - Ank Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} - Ank Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} - Ayee Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} - Ayee Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} - Icr: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} - Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} - Overall Precision: 1.0 - Overall Recall: 1.0 - Overall F1: 1.0 - Overall Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Ank Address | Ank Name | Ayee Address | Ayee Name | Icr | Mount | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2