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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: vfbsilva/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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# vfbsilva/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: 0.7170 |
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- Validation Loss: 1.1978 |
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- Train Overall Precision: 0.4534 |
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- Train Overall Recall: 0.5660 |
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- Train Overall F1: 0.5035 |
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- Train Overall Accuracy: 0.6193 |
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- Epoch: 6 |
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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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| 1.6558 | 1.4251 | 0.2107 | 0.3332 | 0.2581 | 0.4092 | 0 | |
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| 1.3215 | 1.2372 | 0.3024 | 0.4696 | 0.3679 | 0.4742 | 1 | |
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| 1.1454 | 1.2395 | 0.3329 | 0.4782 | 0.3925 | 0.4779 | 2 | |
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| 1.0421 | 1.1025 | 0.3771 | 0.5263 | 0.4394 | 0.6005 | 3 | |
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| 0.9349 | 1.1771 | 0.3860 | 0.5610 | 0.4574 | 0.5416 | 4 | |
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| 0.8505 | 1.0244 | 0.4657 | 0.5685 | 0.5120 | 0.6265 | 5 | |
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| 0.7170 | 1.1978 | 0.4534 | 0.5660 | 0.5035 | 0.6193 | 6 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- TensorFlow 2.10.0 |
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- Datasets 2.16.1 |
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- Tokenizers 0.13.2 |
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