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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: 0.2477 |
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- Validation Loss: 0.7106 |
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- Train Overall Precision: 0.7360 |
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- Train Overall Recall: 0.7918 |
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- Train Overall F1: 0.7629 |
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- Train Overall Accuracy: 0.7983 |
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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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| 1.7300 | 1.4338 | 0.1989 | 0.2313 | 0.2139 | 0.5265 | 0 | |
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| 1.1984 | 0.8999 | 0.5775 | 0.5795 | 0.5785 | 0.7162 | 1 | |
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| 0.7785 | 0.7393 | 0.6713 | 0.7040 | 0.6872 | 0.7634 | 2 | |
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| 0.5666 | 0.6416 | 0.7113 | 0.7617 | 0.7356 | 0.7961 | 3 | |
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| 0.4447 | 0.6454 | 0.7165 | 0.7737 | 0.7440 | 0.8003 | 4 | |
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| 0.3647 | 0.6500 | 0.7353 | 0.7832 | 0.7585 | 0.8091 | 5 | |
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| 0.3044 | 0.6587 | 0.7266 | 0.8053 | 0.7639 | 0.8119 | 6 | |
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| 0.2477 | 0.7106 | 0.7360 | 0.7918 | 0.7629 | 0.7983 | 7 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- TensorFlow 2.9.2 |
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- Datasets 2.6.0 |
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- Tokenizers 0.13.1 |
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