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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: 0.2280 |
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- Validation Loss: 0.6532 |
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- Train Overall Precision: 0.7218 |
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- Train Overall Recall: 0.7878 |
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- Train Overall F1: 0.7534 |
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- Train Overall Accuracy: 0.8144 |
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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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| 1.6940 | 1.4151 | 0.2686 | 0.2785 | 0.2735 | 0.5128 | 0 | |
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| 1.1731 | 0.8665 | 0.5771 | 0.6101 | 0.5932 | 0.7267 | 1 | |
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| 0.7612 | 0.6849 | 0.6362 | 0.7336 | 0.6814 | 0.7784 | 2 | |
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| 0.5630 | 0.6265 | 0.6748 | 0.7592 | 0.7145 | 0.8017 | 3 | |
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| 0.4441 | 0.6256 | 0.6935 | 0.7767 | 0.7328 | 0.8036 | 4 | |
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| 0.3641 | 0.6402 | 0.7115 | 0.7772 | 0.7429 | 0.7940 | 5 | |
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| 0.2781 | 0.6248 | 0.7176 | 0.7868 | 0.7506 | 0.8141 | 6 | |
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| 0.2280 | 0.6532 | 0.7218 | 0.7878 | 0.7534 | 0.8144 | 7 | |
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### Framework versions |
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- Transformers 4.22.2 |
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- TensorFlow 2.10.0 |
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- Datasets 2.5.2 |
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- Tokenizers 0.12.1 |
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