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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- ls-generated4
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-invoice-model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ls-generated4
type: ls-generated4
config: invoice
split: test
args: invoice
metrics:
- name: Precision
type: precision
value: 0.9185733512786003
- name: Recall
type: recall
value: 0.9375
- name: F1
type: f1
value: 0.9279401767505099
- name: Accuracy
type: accuracy
value: 0.9536870503597122
---
<!-- 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-invoice-model
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the ls-generated4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3694
- Precision: 0.9186
- Recall: 0.9375
- F1: 0.9279
- Accuracy: 0.9537
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.85 | 100 | 0.7836 | 0.5238 | 0.5982 | 0.5585 | 0.7680 |
| No log | 1.69 | 200 | 0.4954 | 0.6888 | 0.7479 | 0.7172 | 0.8422 |
| No log | 2.54 | 300 | 0.3483 | 0.7807 | 0.8462 | 0.8121 | 0.9040 |
| No log | 3.39 | 400 | 0.3200 | 0.8113 | 0.8654 | 0.8375 | 0.9125 |
| 0.5923 | 4.24 | 500 | 0.2775 | 0.8593 | 0.8853 | 0.8721 | 0.9319 |
| 0.5923 | 5.08 | 600 | 0.2674 | 0.8700 | 0.9052 | 0.8872 | 0.9377 |
| 0.5923 | 5.93 | 700 | 0.2766 | 0.8739 | 0.9135 | 0.8932 | 0.9386 |
| 0.5923 | 6.78 | 800 | 0.2641 | 0.8879 | 0.9190 | 0.9031 | 0.9472 |
| 0.5923 | 7.63 | 900 | 0.2893 | 0.9094 | 0.9238 | 0.9165 | 0.9447 |
| 0.0802 | 8.47 | 1000 | 0.3369 | 0.9145 | 0.9258 | 0.9201 | 0.9465 |
| 0.0802 | 9.32 | 1100 | 0.3037 | 0.9043 | 0.9341 | 0.9189 | 0.9505 |
| 0.0802 | 10.17 | 1200 | 0.3510 | 0.9032 | 0.9231 | 0.9130 | 0.9472 |
| 0.0802 | 11.02 | 1300 | 0.3224 | 0.9138 | 0.9251 | 0.9195 | 0.9501 |
| 0.0802 | 11.86 | 1400 | 0.3873 | 0.9133 | 0.9265 | 0.9199 | 0.9456 |
| 0.0198 | 12.71 | 1500 | 0.3786 | 0.9120 | 0.9327 | 0.9222 | 0.9492 |
| 0.0198 | 13.56 | 1600 | 0.3807 | 0.9050 | 0.9293 | 0.9170 | 0.9469 |
| 0.0198 | 14.41 | 1700 | 0.3664 | 0.9088 | 0.9313 | 0.9199 | 0.9510 |
| 0.0198 | 15.25 | 1800 | 0.3582 | 0.9152 | 0.9341 | 0.9245 | 0.9521 |
| 0.0198 | 16.1 | 1900 | 0.3736 | 0.9198 | 0.9368 | 0.9282 | 0.9528 |
| 0.007 | 16.95 | 2000 | 0.3694 | 0.9186 | 0.9375 | 0.9279 | 0.9537 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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