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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: 0.2920 |
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- Validation Loss: 0.6882 |
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- Train Overall Precision: 0.7061 |
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- Train Overall Recall: 0.7943 |
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- Train Overall F1: 0.7476 |
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- Train Overall Accuracy: 0.7966 |
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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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| 1.7368 | 1.4201 | 0.2646 | 0.3051 | 0.2834 | 0.5163 | 0 | |
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| 1.2040 | 0.9290 | 0.5253 | 0.6051 | 0.5624 | 0.7138 | 1 | |
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| 0.8330 | 0.8307 | 0.5912 | 0.7010 | 0.6414 | 0.7294 | 2 | |
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| 0.6119 | 0.6724 | 0.6667 | 0.7697 | 0.7145 | 0.7902 | 3 | |
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| 0.4706 | 0.6231 | 0.6905 | 0.7883 | 0.7362 | 0.8068 | 4 | |
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| 0.3759 | 0.6366 | 0.7203 | 0.7933 | 0.7550 | 0.8077 | 5 | |
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| 0.3043 | 0.7168 | 0.6989 | 0.7953 | 0.7440 | 0.7937 | 6 | |
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| 0.2920 | 0.6882 | 0.7061 | 0.7943 | 0.7476 | 0.7966 | 7 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- TensorFlow 2.12.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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