---
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: 0.4673
- Precision: 0.8401
- Recall: 0.8399
- F1: 0.8400
- Accuracy: 0.8784

## 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: 2000

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0756        | 0.4425  | 50   | 0.5379          | 0.7401    | 0.7577 | 0.7488 | 0.8092   |
| 0.5502        | 0.8850  | 100  | 0.4509          | 0.7628    | 0.8035 | 0.7827 | 0.8354   |
| 0.4459        | 1.3274  | 150  | 0.4267          | 0.7667    | 0.8307 | 0.7974 | 0.8461   |
| 0.4209        | 1.7699  | 200  | 0.4030          | 0.7837    | 0.8130 | 0.7981 | 0.8476   |
| 0.3973        | 2.2124  | 250  | 0.3828          | 0.7930    | 0.8222 | 0.8073 | 0.8545   |
| 0.3421        | 2.6549  | 300  | 0.3754          | 0.8199    | 0.8060 | 0.8129 | 0.8618   |
| 0.3529        | 3.0973  | 350  | 0.3780          | 0.7888    | 0.8464 | 0.8166 | 0.8585   |
| 0.2961        | 3.5398  | 400  | 0.4031          | 0.7724    | 0.8512 | 0.8099 | 0.8493   |
| 0.3119        | 3.9823  | 450  | 0.3564          | 0.8111    | 0.8424 | 0.8265 | 0.8676   |
| 0.2629        | 4.4248  | 500  | 0.3746          | 0.7991    | 0.8427 | 0.8203 | 0.8649   |
| 0.2684        | 4.8673  | 550  | 0.3764          | 0.8198    | 0.8028 | 0.8112 | 0.8611   |
| 0.2433        | 5.3097  | 600  | 0.3752          | 0.8225    | 0.8330 | 0.8277 | 0.8684   |
| 0.2289        | 5.7522  | 650  | 0.3966          | 0.7908    | 0.8377 | 0.8136 | 0.8561   |
| 0.2141        | 6.1947  | 700  | 0.3870          | 0.8251    | 0.8175 | 0.8213 | 0.8645   |
| 0.2072        | 6.6372  | 750  | 0.3782          | 0.8129    | 0.8427 | 0.8275 | 0.8694   |
| 0.2101        | 7.0796  | 800  | 0.3758          | 0.8311    | 0.8379 | 0.8345 | 0.8743   |
| 0.1848        | 7.5221  | 850  | 0.3959          | 0.8063    | 0.8342 | 0.8200 | 0.8638   |
| 0.1787        | 7.9646  | 900  | 0.4088          | 0.8127    | 0.8360 | 0.8241 | 0.8634   |
| 0.1563        | 8.4071  | 950  | 0.4146          | 0.8068    | 0.8222 | 0.8144 | 0.8598   |
| 0.1617        | 8.8496  | 1000 | 0.3919          | 0.8220    | 0.8360 | 0.8289 | 0.8714   |
| 0.1498        | 9.2920  | 1050 | 0.4222          | 0.8149    | 0.8222 | 0.8186 | 0.8625   |
| 0.1422        | 9.7345  | 1100 | 0.4104          | 0.8188    | 0.8402 | 0.8293 | 0.8699   |
| 0.1341        | 10.1770 | 1150 | 0.4207          | 0.8370    | 0.8115 | 0.8241 | 0.8701   |
| 0.1311        | 10.6195 | 1200 | 0.4277          | 0.8401    | 0.8135 | 0.8266 | 0.8710   |
| 0.1239        | 11.0619 | 1250 | 0.4153          | 0.8368    | 0.8222 | 0.8295 | 0.8729   |
| 0.1139        | 11.5044 | 1300 | 0.4330          | 0.8272    | 0.8379 | 0.8325 | 0.8721   |
| 0.1126        | 11.9469 | 1350 | 0.4389          | 0.8393    | 0.8295 | 0.8344 | 0.8739   |
| 0.0983        | 12.3894 | 1400 | 0.4601          | 0.8362    | 0.8148 | 0.8254 | 0.8679   |
| 0.1027        | 12.8319 | 1450 | 0.4431          | 0.8369    | 0.8280 | 0.8324 | 0.8732   |
| 0.0944        | 13.2743 | 1500 | 0.4557          | 0.8253    | 0.8422 | 0.8337 | 0.8717   |
| 0.0866        | 13.7168 | 1550 | 0.4566          | 0.8333    | 0.8312 | 0.8323 | 0.8734   |
| 0.0872        | 14.1593 | 1600 | 0.4609          | 0.8390    | 0.8312 | 0.8351 | 0.8760   |
| 0.079         | 14.6018 | 1650 | 0.4522          | 0.8349    | 0.8357 | 0.8353 | 0.8765   |
| 0.0793        | 15.0442 | 1700 | 0.4590          | 0.8263    | 0.8447 | 0.8354 | 0.8740   |
| 0.0738        | 15.4867 | 1750 | 0.4606          | 0.8373    | 0.8275 | 0.8324 | 0.8751   |
| 0.0704        | 15.9292 | 1800 | 0.4553          | 0.8454    | 0.8369 | 0.8411 | 0.8812   |
| 0.0642        | 16.3717 | 1850 | 0.4724          | 0.8339    | 0.8424 | 0.8381 | 0.8766   |
| 0.0647        | 16.8142 | 1900 | 0.4670          | 0.8429    | 0.8417 | 0.8423 | 0.8812   |
| 0.0624        | 17.2566 | 1950 | 0.4647          | 0.8410    | 0.8402 | 0.8406 | 0.8792   |
| 0.0593        | 17.6991 | 2000 | 0.4673          | 0.8401    | 0.8399 | 0.8400 | 0.8784   |


### Framework versions

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