---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: NLPmonster/layoutlmv3-for-receipt-understanding
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-for-complete-receipt-understanding
  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. -->

# layoutlmv3-for-complete-receipt-understanding

This model is a fine-tuned version of [NLPmonster/layoutlmv3-for-receipt-understanding](https://huggingface.co/NLPmonster/layoutlmv3-for-receipt-understanding) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5246
- Precision: 0.7795
- Recall: 0.7867
- F1: 0.7831
- Accuracy: 0.8572

## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2792        | 0.4425 | 50   | 0.5236          | 0.7608    | 0.7535 | 0.7571 | 0.8398   |
| 0.3022        | 0.8850 | 100  | 0.5262          | 0.7709    | 0.7541 | 0.7624 | 0.8420   |
| 0.2821        | 1.3274 | 150  | 0.5263          | 0.7704    | 0.7616 | 0.7660 | 0.8403   |
| 0.2801        | 1.7699 | 200  | 0.5310          | 0.7645    | 0.7659 | 0.7652 | 0.8412   |
| 0.2545        | 2.2124 | 250  | 0.5425          | 0.7606    | 0.7685 | 0.7645 | 0.8416   |
| 0.2453        | 2.6549 | 300  | 0.5237          | 0.7624    | 0.7602 | 0.7613 | 0.8417   |
| 0.2464        | 3.0973 | 350  | 0.5169          | 0.7699    | 0.7721 | 0.7710 | 0.8459   |
| 0.2248        | 3.5398 | 400  | 0.5266          | 0.7666    | 0.7701 | 0.7683 | 0.8447   |
| 0.2117        | 3.9823 | 450  | 0.5041          | 0.7754    | 0.7751 | 0.7753 | 0.8496   |
| 0.1986        | 4.4248 | 500  | 0.5327          | 0.7673    | 0.7729 | 0.7701 | 0.8453   |
| 0.1832        | 4.8673 | 550  | 0.5462          | 0.7658    | 0.7606 | 0.7632 | 0.8423   |
| 0.1752        | 5.3097 | 600  | 0.5207          | 0.7738    | 0.7830 | 0.7783 | 0.8519   |
| 0.1698        | 5.7522 | 650  | 0.5247          | 0.7737    | 0.7763 | 0.7750 | 0.8514   |
| 0.1495        | 6.1947 | 700  | 0.5433          | 0.7702    | 0.7754 | 0.7727 | 0.8495   |
| 0.1487        | 6.6372 | 750  | 0.5363          | 0.7731    | 0.7784 | 0.7757 | 0.8505   |
| 0.1431        | 7.0796 | 800  | 0.5276          | 0.7792    | 0.7754 | 0.7773 | 0.8544   |
| 0.1283        | 7.5221 | 850  | 0.5344          | 0.7752    | 0.7816 | 0.7784 | 0.8536   |
| 0.1253        | 7.9646 | 900  | 0.5166          | 0.7887    | 0.7834 | 0.7861 | 0.8594   |
| 0.1176        | 8.4071 | 950  | 0.5261          | 0.7794    | 0.7834 | 0.7814 | 0.8571   |
| 0.1124        | 8.8496 | 1000 | 0.5246          | 0.7795    | 0.7867 | 0.7831 | 0.8572   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1