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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: 0.2511
- Validation Loss: 0.6882
- Train Overall Precision: 0.7189
- Train Overall Recall: 0.7878
- Train Overall F1: 0.7517
- Train Overall Accuracy: 0.8039
- 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 |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.6546 | 1.3264 | 0.3384 | 0.3708 | 0.3538 | 0.5774 | 0 |
| 1.0901 | 0.8303 | 0.6013 | 0.6508 | 0.6251 | 0.7392 | 1 |
| 0.7169 | 0.6666 | 0.6778 | 0.7441 | 0.7094 | 0.7864 | 2 |
| 0.5285 | 0.6429 | 0.6859 | 0.7702 | 0.7256 | 0.8022 | 3 |
| 0.4270 | 0.6216 | 0.7089 | 0.7832 | 0.7442 | 0.8092 | 4 |
| 0.3451 | 0.6699 | 0.7038 | 0.7832 | 0.7414 | 0.7972 | 5 |
| 0.2867 | 0.6886 | 0.7203 | 0.7868 | 0.7520 | 0.7965 | 6 |
| 0.2511 | 0.6882 | 0.7189 | 0.7878 | 0.7517 | 0.8039 | 7 |
### Framework versions
- Transformers 4.41.0
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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