---
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: 0.4421
- Precision: 0.6502
- Recall: 0.6607
- F1: 0.6554
- Accuracy: 0.8760

## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2604        | 0.4673 | 50   | 0.6573          | 0.5077    | 0.4747 | 0.4906 | 0.7863   |
| 0.5256        | 0.9346 | 100  | 0.4824          | 0.5732    | 0.6521 | 0.6101 | 0.8465   |
| 0.4529        | 1.4019 | 150  | 0.4461          | 0.5926    | 0.6509 | 0.6204 | 0.8464   |
| 0.3767        | 1.8692 | 200  | 0.4265          | 0.5747    | 0.6740 | 0.6204 | 0.8492   |
| 0.3403        | 2.3364 | 250  | 0.4557          | 0.5564    | 0.6083 | 0.5812 | 0.8451   |
| 0.331         | 2.8037 | 300  | 0.4065          | 0.6384    | 0.6671 | 0.6524 | 0.8669   |
| 0.2984        | 3.2710 | 350  | 0.3820          | 0.6411    | 0.6411 | 0.6411 | 0.8729   |
| 0.2763        | 3.7383 | 400  | 0.4078          | 0.6104    | 0.6037 | 0.6070 | 0.8576   |
| 0.2626        | 4.2056 | 450  | 0.4203          | 0.6268    | 0.6164 | 0.6216 | 0.8589   |
| 0.2456        | 4.6729 | 500  | 0.3960          | 0.6240    | 0.6406 | 0.6322 | 0.8686   |
| 0.2078        | 5.1402 | 550  | 0.4074          | 0.6401    | 0.6290 | 0.6345 | 0.8709   |
| 0.1859        | 5.6075 | 600  | 0.3853          | 0.6511    | 0.6601 | 0.6556 | 0.8733   |
| 0.2059        | 6.0748 | 650  | 0.3845          | 0.6539    | 0.6509 | 0.6524 | 0.8772   |
| 0.1649        | 6.5421 | 700  | 0.4128          | 0.6298    | 0.6486 | 0.6390 | 0.8706   |
| 0.1599        | 7.0093 | 750  | 0.4328          | 0.6302    | 0.6578 | 0.6437 | 0.8644   |
| 0.1437        | 7.4766 | 800  | 0.4100          | 0.6510    | 0.6469 | 0.6489 | 0.8727   |
| 0.1377        | 7.9439 | 850  | 0.4409          | 0.6317    | 0.6699 | 0.6503 | 0.8711   |
| 0.1164        | 8.4112 | 900  | 0.4331          | 0.6301    | 0.6642 | 0.6467 | 0.8717   |
| 0.1149        | 8.8785 | 950  | 0.4523          | 0.6466    | 0.6555 | 0.6510 | 0.8712   |
| 0.1096        | 9.3458 | 1000 | 0.4421          | 0.6502    | 0.6607 | 0.6554 | 0.8760   |


### Framework versions

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