End of training

README.md

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+---
+tags:
+- generated_from_trainer
+model-index:
+- name: layoutlm-synthchecking-padding
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# 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

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preprocessor_config.json

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special_tokens_map.json

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+}