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---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: layoutlm-funsd-tf
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd-tf
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5535
- Validation Loss: 1.2985
- Train Overall Precision: 0.4855
- Train Overall Recall: 0.5705
- Train Overall F1: 0.5246
- Train Overall Accuracy: 0.6026
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.6900 | 1.4644 | 0.1772 | 0.1922 | 0.1844 | 0.4144 | 0 |
| 1.3402 | 1.2155 | 0.2920 | 0.4566 | 0.3562 | 0.4865 | 1 |
| 1.1357 | 1.1454 | 0.3449 | 0.4952 | 0.4066 | 0.5503 | 2 |
| 1.0088 | 1.1098 | 0.3871 | 0.5198 | 0.4438 | 0.5737 | 3 |
| 0.8868 | 1.0295 | 0.4061 | 0.5569 | 0.4697 | 0.6159 | 4 |
| 0.7747 | 1.1882 | 0.4507 | 0.5625 | 0.5004 | 0.6062 | 5 |
| 0.6577 | 1.0658 | 0.4760 | 0.5625 | 0.5156 | 0.6305 | 6 |
| 0.5535 | 1.2985 | 0.4855 | 0.5705 | 0.5246 | 0.6026 | 7 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.15.2
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