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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: 1.0
- name: Recall
type: recall
value: 1.0
- name: F1
type: f1
value: 1.0
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- 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: 0.0014
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
## 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: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.0 | 100 | 0.0766 | 0.97 | 0.9838 | 0.9768 | 0.9968 |
| No log | 4.0 | 200 | 0.0214 | 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 | 0.0142 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| 0.1264 | 10.0 | 500 | 0.0129 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| 0.1264 | 12.0 | 600 | 0.0118 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| 0.1264 | 14.0 | 700 | 0.0038 | 0.9980 | 0.9959 | 0.9970 | 0.9996 |
| 0.1264 | 16.0 | 800 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.1264 | 18.0 | 900 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0064 | 20.0 | 1000 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0064 | 22.0 | 1100 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0064 | 24.0 | 1200 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0064 | 26.0 | 1300 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0064 | 28.0 | 1400 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 30.0 | 1500 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 32.0 | 1600 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 34.0 | 1700 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 36.0 | 1800 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 38.0 | 1900 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 40.0 | 2000 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 42.0 | 2100 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 44.0 | 2200 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 46.0 | 2300 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 48.0 | 2400 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 50.0 | 2500 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 52.0 | 2600 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 54.0 | 2700 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 56.0 | 2800 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 58.0 | 2900 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 60.0 | 3000 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 62.0 | 3100 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 64.0 | 3200 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 66.0 | 3300 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 68.0 | 3400 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 70.0 | 3500 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 72.0 | 3600 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 74.0 | 3700 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 76.0 | 3800 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 78.0 | 3900 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 80.0 | 4000 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 82.0 | 4100 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 84.0 | 4200 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 86.0 | 4300 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 88.0 | 4400 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 90.0 | 4500 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 92.0 | 4600 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 94.0 | 4700 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 96.0 | 4800 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 98.0 | 4900 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 100.0 | 5000 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
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