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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- invoices
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: invoices
type: invoices
config: sroie
split: train
args: sroie
metrics:
- name: Precision
type: precision
value: 0.975
- name: Recall
type: recall
value: 0.975
- name: F1
type: f1
value: 0.975
- name: Accuracy
type: accuracy
value: 0.975
---
<!-- 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 invoices dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2299
- Precision: 0.975
- Recall: 0.975
- F1: 0.975
- Accuracy: 0.975
## 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 | 14.29 | 100 | 0.1616 | 0.975 | 0.975 | 0.975 | 0.975 |
| No log | 28.57 | 200 | 0.1909 | 0.975 | 0.975 | 0.975 | 0.975 |
| No log | 42.86 | 300 | 0.2046 | 0.975 | 0.975 | 0.975 | 0.975 |
| No log | 57.14 | 400 | 0.2134 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.1239 | 71.43 | 500 | 0.2299 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.1239 | 85.71 | 600 | 0.2309 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.1239 | 100.0 | 700 | 0.2342 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.1239 | 114.29 | 800 | 0.2407 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.1239 | 128.57 | 900 | 0.2428 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0007 | 142.86 | 1000 | 0.2449 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0007 | 157.14 | 1100 | 0.2465 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0007 | 171.43 | 1200 | 0.2488 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0007 | 185.71 | 1300 | 0.2515 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0007 | 200.0 | 1400 | 0.2525 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0004 | 214.29 | 1500 | 0.2540 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0004 | 228.57 | 1600 | 0.2557 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0004 | 242.86 | 1700 | 0.2564 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0004 | 257.14 | 1800 | 0.2570 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0004 | 271.43 | 1900 | 0.2573 | 0.975 | 0.975 | 0.975 | 0.975 |
| 0.0003 | 285.71 | 2000 | 0.2574 | 0.975 | 0.975 | 0.975 | 0.975 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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