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---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlm-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6097
- Answer: {'precision': 0.43703703703703706, 'recall': 0.6413043478260869, 'f1': 0.5198237885462555, 'number': 92}
- Header: {'precision': 0.2894736842105263, 'recall': 0.34375, 'f1': 0.3142857142857143, 'number': 32}
- Overall Precision: 0.4046
- Overall Recall: 0.5645
- Overall F1: 0.4714
- Overall Accuracy: 0.8656
## 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:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.4561 | 1.0 | 2 | 1.0789 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.0 | 0.0 | 0.0 | 0.8182 |
| 0.7649 | 2.0 | 4 | 0.9219 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.0 | 0.0 | 0.0 | 0.8182 |
| 0.5601 | 3.0 | 6 | 0.8338 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.0 | 0.0 | 0.0 | 0.8182 |
| 0.4611 | 4.0 | 8 | 0.7533 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.0 | 0.0 | 0.0 | 0.8182 |
| 0.3306 | 5.0 | 10 | 0.6861 | {'precision': 0.75, 'recall': 0.03260869565217391, 'f1': 0.06249999999999999, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.75 | 0.0242 | 0.0469 | 0.8207 |
| 0.3001 | 6.0 | 12 | 0.6509 | {'precision': 0.43243243243243246, 'recall': 0.5217391304347826, 'f1': 0.47290640394088673, 'number': 92} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | 0.4324 | 0.3871 | 0.4085 | 0.8592 |
| 0.3436 | 7.0 | 14 | 0.6713 | {'precision': 0.33689839572192515, 'recall': 0.6847826086956522, 'f1': 0.45161290322580644, 'number': 92} | {'precision': 0.14285714285714285, 'recall': 0.03125, 'f1': 0.05128205128205128, 'number': 32} | 0.3299 | 0.5161 | 0.4025 | 0.8284 |
| 0.3624 | 8.0 | 16 | 0.6454 | {'precision': 0.3516483516483517, 'recall': 0.6956521739130435, 'f1': 0.46715328467153283, 'number': 92} | {'precision': 0.4, 'recall': 0.0625, 'f1': 0.10810810810810811, 'number': 32} | 0.3529 | 0.5323 | 0.4244 | 0.8387 |
| 0.4258 | 9.0 | 18 | 0.6192 | {'precision': 0.3668639053254438, 'recall': 0.6739130434782609, 'f1': 0.475095785440613, 'number': 92} | {'precision': 0.5555555555555556, 'recall': 0.15625, 'f1': 0.24390243902439024, 'number': 32} | 0.3764 | 0.5403 | 0.4437 | 0.8528 |
| 0.2221 | 10.0 | 20 | 0.6282 | {'precision': 0.36942675159235666, 'recall': 0.6304347826086957, 'f1': 0.465863453815261, 'number': 92} | {'precision': 0.3181818181818182, 'recall': 0.21875, 'f1': 0.25925925925925924, 'number': 32} | 0.3631 | 0.5242 | 0.4290 | 0.8476 |
| 0.2069 | 11.0 | 22 | 0.6241 | {'precision': 0.40559440559440557, 'recall': 0.6304347826086957, 'f1': 0.4936170212765958, 'number': 92} | {'precision': 0.34375, 'recall': 0.34375, 'f1': 0.34375, 'number': 32} | 0.3943 | 0.5565 | 0.4615 | 0.8592 |
| 0.2035 | 12.0 | 24 | 0.6218 | {'precision': 0.4084507042253521, 'recall': 0.6304347826086957, 'f1': 0.49572649572649574, 'number': 92} | {'precision': 0.3125, 'recall': 0.3125, 'f1': 0.3125, 'number': 32} | 0.3908 | 0.5484 | 0.4564 | 0.8604 |
| 0.1729 | 13.0 | 26 | 0.6175 | {'precision': 0.41843971631205673, 'recall': 0.6413043478260869, 'f1': 0.5064377682403434, 'number': 92} | {'precision': 0.3125, 'recall': 0.3125, 'f1': 0.3125, 'number': 32} | 0.3988 | 0.5565 | 0.4646 | 0.8643 |
| 0.1759 | 14.0 | 28 | 0.6127 | {'precision': 0.427536231884058, 'recall': 0.6413043478260869, 'f1': 0.5130434782608696, 'number': 92} | {'precision': 0.3142857142857143, 'recall': 0.34375, 'f1': 0.3283582089552239, 'number': 32} | 0.4046 | 0.5645 | 0.4714 | 0.8656 |
| 0.2299 | 15.0 | 30 | 0.6097 | {'precision': 0.43703703703703706, 'recall': 0.6413043478260869, 'f1': 0.5198237885462555, 'number': 92} | {'precision': 0.2894736842105263, 'recall': 0.34375, 'f1': 0.3142857142857143, 'number': 32} | 0.4046 | 0.5645 | 0.4714 | 0.8656 |
### Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
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