---
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