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---
tags:
- generated_from_keras_callback
model-index:
- name: layoutlm-funsd-sequence-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-sequence-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: 0.2348
- Validation Loss: 0.6737
- Train Overall Precision: 0.7356
- Train Overall Recall: 0.7998
- Train Overall F1: 0.7663
- Train Overall Accuracy: 0.8220
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.7150 | 1.4139 | 0.2373 | 0.2860 | 0.2594 | 0.4954 | 0 |
| 1.1803 | 0.9205 | 0.5676 | 0.6322 | 0.5981 | 0.7008 | 1 |
| 0.7884 | 0.7100 | 0.6202 | 0.7250 | 0.6685 | 0.7735 | 2 |
| 0.5877 | 0.6476 | 0.6689 | 0.7662 | 0.7142 | 0.7942 | 3 |
| 0.4490 | 0.6179 | 0.7133 | 0.8078 | 0.7576 | 0.8066 | 4 |
| 0.3746 | 0.6305 | 0.7176 | 0.7878 | 0.7510 | 0.8129 | 5 |
| 0.3082 | 0.6924 | 0.7163 | 0.8018 | 0.7566 | 0.7937 | 6 |
| 0.2348 | 0.6737 | 0.7356 | 0.7998 | 0.7663 | 0.8220 | 7 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
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