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---
base_model: microsoft/layoutlm-base-uncased
library_name: transformers
license: mit
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: 0.2459
- Validation Loss: 0.6795
- Train Overall Precision: 0.7276
- Train Overall Recall: 0.7812
- Train Overall F1: 0.7534
- Train Overall Accuracy: 0.8032
- 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 |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.7017 | 1.4088 | 0.2201 | 0.2373 | 0.2284 | 0.4952 | 0 |
| 1.1605 | 0.8580 | 0.5955 | 0.6849 | 0.6371 | 0.7371 | 1 |
| 0.7603 | 0.6747 | 0.6574 | 0.7220 | 0.6882 | 0.7808 | 2 |
| 0.5694 | 0.6343 | 0.6674 | 0.7551 | 0.7086 | 0.7919 | 3 |
| 0.4494 | 0.6429 | 0.6828 | 0.7787 | 0.7276 | 0.7887 | 4 |
| 0.3628 | 0.6226 | 0.7220 | 0.7752 | 0.7476 | 0.8112 | 5 |
| 0.3092 | 0.6537 | 0.7172 | 0.7837 | 0.7490 | 0.8010 | 6 |
| 0.2459 | 0.6795 | 0.7276 | 0.7812 | 0.7534 | 0.8032 | 7 |
### Framework versions
- Transformers 4.44.2
- TensorFlow 2.17.0
- Datasets 3.0.1
- Tokenizers 0.19.1
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