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---
license: mit
tags:
- generated_from_keras_callback
base_model: microsoft/layoutlm-base-uncased
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: 0.6138
- Validation Loss: 1.0719
- Train Overall Precision: 0.4799
- Train Overall Recall: 0.5926
- Train Overall F1: 0.5303
- Train Overall Accuracy: 0.6254
- 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 |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.6621 | 1.4563 | 0.1960 | 0.3126 | 0.2410 | 0.4075 | 0 |
| 1.3485 | 1.2456 | 0.2943 | 0.4867 | 0.3668 | 0.4838 | 1 |
| 1.1546 | 1.1675 | 0.3643 | 0.5334 | 0.4329 | 0.5295 | 2 |
| 1.0163 | 1.0992 | 0.4061 | 0.5514 | 0.4678 | 0.5842 | 3 |
| 0.8892 | 1.0781 | 0.4346 | 0.5605 | 0.4896 | 0.5989 | 4 |
| 0.7755 | 1.1893 | 0.4589 | 0.5735 | 0.5098 | 0.5913 | 5 |
| 0.6631 | 1.0950 | 0.4863 | 0.5595 | 0.5203 | 0.6265 | 6 |
| 0.6138 | 1.0719 | 0.4799 | 0.5926 | 0.5303 | 0.6254 | 7 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.15.2
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