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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: 0.5937
- Validation Loss: 1.1902
- Train Overall Precision: 0.4751
- Train Overall Recall: 0.5850
- Train Overall F1: 0.5244
- Train Overall Accuracy: 0.6201
- 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 |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.6536 | 1.4851 | 0.1737 | 0.3176 | 0.2245 | 0.4025 | 0 |
| 1.3258 | 1.2951 | 0.2957 | 0.4325 | 0.3513 | 0.4737 | 1 |
| 1.1768 | 1.1266 | 0.3614 | 0.4892 | 0.4157 | 0.5489 | 2 |
| 1.0113 | 1.0274 | 0.3889 | 0.5294 | 0.4484 | 0.6040 | 3 |
| 0.9157 | 1.0104 | 0.4428 | 0.5414 | 0.4871 | 0.6152 | 4 |
| 0.7484 | 1.0807 | 0.4742 | 0.5354 | 0.5029 | 0.6153 | 5 |
| 0.6791 | 1.2077 | 0.4709 | 0.5434 | 0.5045 | 0.6049 | 6 |
| 0.5937 | 1.1902 | 0.4751 | 0.5850 | 0.5244 | 0.6201 | 7 |
### Framework versions
- Transformers 4.38.1
- TensorFlow 2.15.0
- Datasets 2.17.1
- Tokenizers 0.15.2
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