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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- generated
metrics:
- precision
- recall
- f1
- accuracy
base_model: microsoft/layoutlmv3-base
model-index:
- name: layoutlmv3-finetuned-invoice
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: generated
type: generated
config: sroie
split: train
args: sroie
metrics:
- type: precision
value: 0.9959514170040485
name: Precision
- type: recall
value: 0.9979716024340771
name: Recall
- type: f1
value: 0.9969604863221885
name: F1
- type: accuracy
value: 0.9995786812723826
name: Accuracy
---
<!-- 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-finetuned-invoice
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0028
- Precision: 0.9960
- Recall: 0.9980
- F1: 0.9970
- Accuracy: 0.9996
## 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: 1e-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 | 2.0 | 100 | 0.0502 | 0.97 | 0.9838 | 0.9768 | 0.9968 |
| No log | 4.0 | 200 | 0.0194 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 6.0 | 300 | 0.0160 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 8.0 | 400 | 0.0123 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| 0.053 | 10.0 | 500 | 0.0089 | 0.9757 | 0.9757 | 0.9757 | 0.9966 |
| 0.053 | 12.0 | 600 | 0.0058 | 0.9959 | 0.9919 | 0.9939 | 0.9992 |
| 0.053 | 14.0 | 700 | 0.0046 | 0.9939 | 0.9919 | 0.9929 | 0.9989 |
| 0.053 | 16.0 | 800 | 0.0037 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.053 | 18.0 | 900 | 0.0068 | 0.9959 | 0.9878 | 0.9919 | 0.9987 |
| 0.0057 | 20.0 | 1000 | 0.0054 | 0.9919 | 0.9959 | 0.9939 | 0.9992 |
| 0.0057 | 22.0 | 1100 | 0.0057 | 0.9919 | 0.9959 | 0.9939 | 0.9992 |
| 0.0057 | 24.0 | 1200 | 0.0049 | 0.9919 | 0.9959 | 0.9939 | 0.9992 |
| 0.0057 | 26.0 | 1300 | 0.0052 | 0.9919 | 0.9959 | 0.9939 | 0.9992 |
| 0.0057 | 28.0 | 1400 | 0.0030 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0022 | 30.0 | 1500 | 0.0028 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0022 | 32.0 | 1600 | 0.0030 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0022 | 34.0 | 1700 | 0.0030 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0022 | 36.0 | 1800 | 0.0037 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0022 | 38.0 | 1900 | 0.0037 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0017 | 40.0 | 2000 | 0.0037 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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