
# LayoutLM-v3 model fine-tuned on invoice dataset

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoice dataset.

We use Microsoft’s LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds.

It achieves the following results on the evaluation set:
- Loss: 0.0012
- 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
All the training codes are available from the below GitHub link.

https://github.com/Theivaprakasham/layoutlmv3


The model can be evaluated at the HuggingFace Spaces link:

https://huggingface.co/spaces/Theivaprakasham/layoutlmv3_invoice

## 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  | 0.0878          | 0.968     | 0.9817 | 0.9748 | 0.9966   |
| No log        | 4.0   | 200  | 0.0241          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 6.0   | 300  | 0.0186          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 8.0   | 400  | 0.0184          | 0.9854    | 0.9574 | 0.9712 | 0.9956   |
| 0.1308        | 10.0  | 500  | 0.0121          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1308        | 12.0  | 600  | 0.0076          | 0.9939    | 0.9878 | 0.9908 | 0.9987   |
| 0.1308        | 14.0  | 700  | 0.0047          | 1.0       | 0.9959 | 0.9980 | 0.9996   |
| 0.1308        | 16.0  | 800  | 0.0036          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.1308        | 18.0  | 900  | 0.0045          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0069        | 20.0  | 1000 | 0.0043          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0069        | 22.0  | 1100 | 0.0016          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0069        | 24.0  | 1200 | 0.0015          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0069        | 26.0  | 1300 | 0.0014          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0069        | 28.0  | 1400 | 0.0013          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0026        | 30.0  | 1500 | 0.0012          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0026        | 32.0  | 1600 | 0.0012          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0026        | 34.0  | 1700 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0026        | 36.0  | 1800 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0026        | 38.0  | 1900 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.002         | 40.0  | 2000 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["invoice"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-invoice", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "Invoice", "type": "invoice", "args": "invoice"}, "metrics": [{"type": "precision", "value": 1.0, "name": "Precision"}, {"type": "recall", "value": 1.0, "name": "Recall"}, {"type": "f1", "value": 1.0, "name": "F1"}, {"type": "accuracy", "value": 1.0, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:invoice model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

LayoutLM-v3 model fine-tuned on invoice dataset
===============================================


This model is a fine-tuned version of microsoft/layoutlmv3-base on the invoice dataset.


We use Microsoft’s LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post\_code, Due\_date, GST, Invoice\_date, Invoice\_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds.


It achieves the following results on the evaluation set:


* Loss: 0.0012
* 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
----------------------------


All the training codes are available from the below GitHub link.


URL


The model can be evaluated at the HuggingFace Spaces link:


URL


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



### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### 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### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1