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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: 0.9939271255060729
- name: Recall
type: recall
value: 0.9959432048681541
- name: F1
type: f1
value: 0.9949341438703141
- name: Accuracy
type: accuracy
value: 0.9993680219085739
---
<!-- 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: 0.0076
- Precision: 0.9939
- Recall: 0.9959
- F1: 0.9949
- Accuracy: 0.9994
## 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: 750
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.0 | 100 | 0.1086 | 0.87 | 0.8824 | 0.8761 | 0.9863 |
| No log | 4.0 | 200 | 0.0258 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 6.0 | 300 | 0.0172 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 8.0 | 400 | 0.0126 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| 0.1338 | 10.0 | 500 | 0.0076 | 0.9939 | 0.9959 | 0.9949 | 0.9994 |
| 0.1338 | 12.0 | 600 | 0.0073 | 0.9919 | 0.9959 | 0.9939 | 0.9992 |
| 0.1338 | 14.0 | 700 | 0.0048 | 0.9980 | 0.9980 | 0.9980 | 0.9998 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
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