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--- |
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license: mit |
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tags: |
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- generated_from_keras_callback |
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base_model: microsoft/layoutlm-base-uncased |
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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: 0.6688 |
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- Validation Loss: 1.0205 |
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- Train Overall Precision: 0.4820 |
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- Train Overall Recall: 0.5640 |
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- Train Overall F1: 0.5198 |
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- Train Overall Accuracy: 0.6662 |
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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: float32 |
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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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| 1.7446 | 1.5046 | 0.1515 | 0.1912 | 0.1690 | 0.3736 | 0 | |
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| 1.4357 | 1.3277 | 0.2590 | 0.4295 | 0.3231 | 0.4314 | 1 | |
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| 1.2564 | 1.2248 | 0.3028 | 0.4907 | 0.3745 | 0.4767 | 2 | |
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| 1.1340 | 1.1044 | 0.3433 | 0.4802 | 0.4003 | 0.5769 | 3 | |
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| 1.0126 | 1.0670 | 0.4183 | 0.5519 | 0.4759 | 0.5820 | 4 | |
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| 0.8976 | 1.0148 | 0.3816 | 0.5273 | 0.4428 | 0.6186 | 5 | |
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| 0.7552 | 1.0872 | 0.4571 | 0.5449 | 0.4971 | 0.6268 | 6 | |
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| 0.6688 | 1.0205 | 0.4820 | 0.5640 | 0.5198 | 0.6662 | 7 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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