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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: 0.3061 |
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- Validation Loss: 0.6626 |
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- Train Overall Precision: 0.7205 |
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- Train Overall Recall: 0.7863 |
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- Train Overall F1: 0.7519 |
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- Train Overall Accuracy: 0.8006 |
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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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| 1.6700 | 1.3895 | 0.2812 | 0.2880 | 0.2846 | 0.5333 | 0 | |
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| 1.1465 | 0.8828 | 0.5819 | 0.6433 | 0.6111 | 0.7324 | 1 | |
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| 0.7878 | 0.7302 | 0.6497 | 0.7240 | 0.6849 | 0.7791 | 2 | |
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| 0.6195 | 0.6630 | 0.6844 | 0.7692 | 0.7243 | 0.7949 | 3 | |
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| 0.5031 | 0.6265 | 0.6929 | 0.7903 | 0.7384 | 0.8112 | 4 | |
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| 0.4202 | 0.6244 | 0.7329 | 0.7863 | 0.7587 | 0.8124 | 5 | |
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| 0.3479 | 0.6233 | 0.7275 | 0.7702 | 0.7482 | 0.8127 | 6 | |
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| 0.3061 | 0.6626 | 0.7205 | 0.7863 | 0.7519 | 0.8006 | 7 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- TensorFlow 2.9.0 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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