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--- |
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license: mit |
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base_model: microsoft/layoutlm-base-uncased |
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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: 0.2473 |
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- Validation Loss: 0.6721 |
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- Train Overall Precision: 0.7140 |
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- Train Overall Recall: 0.8043 |
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- Train Overall F1: 0.7565 |
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- Train Overall Accuracy: 0.8076 |
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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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| 1.7381 | 1.4132 | 0.2537 | 0.2935 | 0.2722 | 0.5348 | 0 | |
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| 1.2080 | 0.9561 | 0.5655 | 0.5981 | 0.5813 | 0.7060 | 1 | |
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| 0.8008 | 0.7159 | 0.6477 | 0.7371 | 0.6895 | 0.7727 | 2 | |
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| 0.6021 | 0.6365 | 0.6928 | 0.7772 | 0.7326 | 0.7984 | 3 | |
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| 0.4729 | 0.6242 | 0.6833 | 0.7923 | 0.7337 | 0.8067 | 4 | |
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| 0.3818 | 0.6040 | 0.7164 | 0.7832 | 0.7483 | 0.8131 | 5 | |
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| 0.3024 | 0.6533 | 0.7177 | 0.8048 | 0.7588 | 0.8074 | 6 | |
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| 0.2473 | 0.6721 | 0.7140 | 0.8043 | 0.7565 | 0.8076 | 7 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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