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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- generated
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: generated
type: generated
config: sroie
split: test
args: sroie
metrics:
- name: Precision
type: precision
value: 0.9959514170040485
- name: Recall
type: recall
value: 0.9979716024340771
- name: F1
type: f1
value: 0.9969604863221885
- name: Accuracy
type: accuracy
value: 0.9995786812723826
---
<!-- 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: 0.0042
- 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 | 0.1164 | 0.902 | 0.9148 | 0.9084 | 0.9897 |
| No log | 4.0 | 200 | 0.0262 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 6.0 | 300 | 0.0157 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 8.0 | 400 | 0.0097 | 0.9877 | 0.9797 | 0.9837 | 0.9979 |
| 0.1294 | 10.0 | 500 | 0.0065 | 0.9939 | 0.9959 | 0.9949 | 0.9994 |
| 0.1294 | 12.0 | 600 | 0.0042 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.1294 | 14.0 | 700 | 0.0048 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.1294 | 16.0 | 800 | 0.0047 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.1294 | 18.0 | 900 | 0.0045 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 20.0 | 1000 | 0.0042 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 22.0 | 1100 | 0.0041 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 24.0 | 1200 | 0.0041 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 26.0 | 1300 | 0.0040 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 28.0 | 1400 | 0.0040 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 30.0 | 1500 | 0.0040 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 32.0 | 1600 | 0.0040 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 34.0 | 1700 | 0.0039 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 36.0 | 1800 | 0.0039 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 38.0 | 1900 | 0.0039 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0018 | 40.0 | 2000 | 0.0039 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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