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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: 0.2471
- Validation Loss: 0.6741
- Train Overall Precision: 0.7311
- Train Overall Recall: 0.7858
- Train Overall F1: 0.7574
- Train Overall Accuracy: 0.8104
- 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 |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.7162 | 1.4302 | 0.2735 | 0.3026 | 0.2873 | 0.4851 | 0 |
| 1.1601 | 0.8705 | 0.5728 | 0.6708 | 0.6180 | 0.7254 | 1 |
| 0.7538 | 0.7479 | 0.6533 | 0.7055 | 0.6784 | 0.7572 | 2 |
| 0.5704 | 0.6795 | 0.6686 | 0.7582 | 0.7106 | 0.7936 | 3 |
| 0.4379 | 0.6239 | 0.7022 | 0.7762 | 0.7374 | 0.8062 | 4 |
| 0.3470 | 0.6538 | 0.7226 | 0.7842 | 0.7522 | 0.7986 | 5 |
| 0.2908 | 0.6827 | 0.7033 | 0.7777 | 0.7386 | 0.7971 | 6 |
| 0.2471 | 0.6741 | 0.7311 | 0.7858 | 0.7574 | 0.8104 | 7 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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