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---
library_name: transformers
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: layoutlm-receipts
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-receipts
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: 0.0896
- Precision: 0.75
- Recall: 0.75
- F1: 0.75
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.1758 | 1.0 | 8 | 0.1713 | 0.3529 | 0.6 | 0.4444 |
| 0.1438 | 2.0 | 16 | 0.2149 | 0.1111 | 0.15 | 0.1277 |
| 0.0494 | 3.0 | 24 | 0.2381 | 0.36 | 0.45 | 0.4000 |
| 0.042 | 4.0 | 32 | 0.1144 | 0.5455 | 0.6 | 0.5714 |
| 0.0236 | 5.0 | 40 | 0.0788 | 0.7 | 0.7 | 0.7 |
| 0.0111 | 6.0 | 48 | 0.0804 | 0.8333 | 0.75 | 0.7895 |
| 0.0114 | 7.0 | 56 | 0.0964 | 0.6667 | 0.7 | 0.6829 |
| 0.0031 | 8.0 | 64 | 0.0892 | 0.8333 | 0.75 | 0.7895 |
| 0.0065 | 9.0 | 72 | 0.1038 | 0.75 | 0.75 | 0.75 |
| 0.0014 | 10.0 | 80 | 0.1093 | 0.75 | 0.75 | 0.75 |
| 0.0045 | 11.0 | 88 | 0.0998 | 0.75 | 0.75 | 0.75 |
| 0.0027 | 12.0 | 96 | 0.0738 | 0.9444 | 0.85 | 0.8947 |
| 0.0008 | 13.0 | 104 | 0.0745 | 0.9444 | 0.85 | 0.8947 |
| 0.0029 | 14.0 | 112 | 0.1234 | 0.5833 | 0.7 | 0.6364 |
| 0.004 | 15.0 | 120 | 0.0865 | 0.6364 | 0.7 | 0.6667 |
| 0.0007 | 16.0 | 128 | 0.0888 | 0.8333 | 0.75 | 0.7895 |
| 0.0055 | 17.0 | 136 | 0.0934 | 0.75 | 0.75 | 0.75 |
| 0.0004 | 18.0 | 144 | 0.0854 | 0.8333 | 0.75 | 0.7895 |
| 0.0004 | 19.0 | 152 | 0.0846 | 0.8333 | 0.75 | 0.7895 |
| 0.0005 | 20.0 | 160 | 0.0843 | 0.8333 | 0.75 | 0.7895 |
| 0.0005 | 21.0 | 168 | 0.0852 | 0.8333 | 0.75 | 0.7895 |
| 0.0004 | 22.0 | 176 | 0.0862 | 0.8333 | 0.75 | 0.7895 |
| 0.0005 | 23.0 | 184 | 0.0875 | 0.8333 | 0.75 | 0.7895 |
| 0.0003 | 24.0 | 192 | 0.0892 | 0.75 | 0.75 | 0.75 |
| 0.0005 | 25.0 | 200 | 0.0896 | 0.75 | 0.75 | 0.75 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
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