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--- |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: layoutlm-funsd-tf |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# layoutlm-funsd-tf |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.2451 |
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- Validation Loss: 0.7339 |
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- Train Overall Precision: 0.7247 |
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- Train Overall Recall: 0.8058 |
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- Train Overall F1: 0.7631 |
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- Train Overall Accuracy: 0.7976 |
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- Epoch: 7 |
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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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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} |
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- training_precision: mixed_float16 |
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### Training results |
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| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch | |
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|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:| |
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| 1.6758 | 1.4035 | 0.2734 | 0.3191 | 0.2945 | 0.5113 | 0 | |
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| 1.1350 | 0.8802 | 0.5626 | 0.6538 | 0.6048 | 0.7313 | 1 | |
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| 0.7417 | 0.6927 | 0.6604 | 0.7602 | 0.7068 | 0.7805 | 2 | |
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| 0.5568 | 0.6715 | 0.7039 | 0.7501 | 0.7263 | 0.7823 | 3 | |
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| 0.4493 | 0.6464 | 0.7073 | 0.7782 | 0.7410 | 0.7980 | 4 | |
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| 0.3732 | 0.6112 | 0.7108 | 0.7858 | 0.7464 | 0.8182 | 5 | |
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| 0.2949 | 0.6429 | 0.7123 | 0.7988 | 0.7531 | 0.8070 | 6 | |
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| 0.2451 | 0.7339 | 0.7247 | 0.8058 | 0.7631 | 0.7976 | 7 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- TensorFlow 2.9.2 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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