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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: Aanshula/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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# Aanshula/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: 0.3182 |
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- Validation Loss: 0.6807 |
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- Train Overall Precision: 0.7172 |
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- Train Overall Recall: 0.7878 |
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- Train Overall F1: 0.7508 |
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- Train Overall Accuracy: 0.7864 |
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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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| 1.7000 | 1.4167 | 0.2445 | 0.2107 | 0.2264 | 0.4831 | 0 | |
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| 1.1656 | 0.8677 | 0.5749 | 0.6257 | 0.5992 | 0.7251 | 1 | |
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| 0.7704 | 0.7254 | 0.6356 | 0.7160 | 0.6734 | 0.7637 | 2 | |
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| 0.5758 | 0.6690 | 0.6851 | 0.7476 | 0.7150 | 0.7857 | 3 | |
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| 0.4526 | 0.6096 | 0.7085 | 0.7757 | 0.7406 | 0.8046 | 4 | |
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| 0.3614 | 0.6834 | 0.7118 | 0.7657 | 0.7377 | 0.7872 | 5 | |
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| 0.3182 | 0.6807 | 0.7172 | 0.7878 | 0.7508 | 0.7864 | 6 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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