
# 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 Eurocorporation dataset.

We use Microsoft’s LayoutLMv3 trained on Eurocorporation Dataset to predict the produttore_key, produttore_value, unitloc_key, unitloc_value, cliente_key, cliente_value, operatore_key, operatore_value, referente_key, referente_value, telefono_key, telefono_value, cfproduttore_key, cfproduttore_value, emailcliente_key, emailcliente_value, datarichiesta_key, datarichiesta_value, orariorichiesta_key, orariorichiesta_value,emailproduttore_key,emailproduttore_value,mattina_key,mattina_value,pomeriggio_key,pomeriggio_value,cer_key,cer_value,descrizione_key,descrizione_value,sf_key,sf_value,classpericolo_key,destino_key,destino_value,confezionamento_key,confezionamento_value,destinazione_key and destinazione_value.. To use it, simply upload an image. 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/jyotiyadav94/data/blob/main/Layoutlmv3_training_inference_1_(1).ipynb


The model can be evaluated at the HuggingFace Spaces link:

https://huggingface.co/DataIntelligenceTeam/layoutlmv3-finetuned-eurocorporation

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- 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

| Step | Training Loss | Validation Loss| Precision | Recall | F1     | Accuracy |
|:---:|:------------:|:--------------:|:---------:|:------:|:------:|:---------:|
|100 |	No log       | 0.57765	|0.568773	| 0.600000	|0.583969|  	0.895848|
|200 |	No log       | 0.181364 |	0.933594|	0.937255|	0.935421|	0.988037|
|300 |	No log       |	0.091626|	0.945312|	0.949020|	0.947162|	0.991555|
|400 |	No log       |	0.060504|	0.964981|	0.972549|	0.968750|	0.995074|
|500 |	0.360900     |	0.046041|	0.988327|	0.996078|	0.992188|	0.999296|
|600 |	0.360900     |	0.036889|	0.988327|	0.996078|	0.992188|	0.999296|
|700 |	0.360900     |	0.032077|	0.988327|	0.996078|	0.992188|	0.999296|
|800 |	0.360900     |	0.028109|	0.988327|	0.996078|	0.992188|	0.999296|
|900 |	0.360900     |	0.027945|	0.988327|	0.996078|	0.992188|	0.999296|
|1000|	0.037800     |	0.027469|	0.988327|	0.996078|	0.992188|	0.999296

### 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 region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice #model-index #autotrain_compatible #endpoints_compatible #region-us 

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


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


We use Microsoft’s LayoutLMv3 trained on Eurocorporation Dataset to predict the produttore\_key, produttore\_value, unitloc\_key, unitloc\_value, cliente\_key, cliente\_value, operatore\_key, operatore\_value, referente\_key, referente\_value, telefono\_key, telefono\_value, cfproduttore\_key, cfproduttore\_value, emailcliente\_key, emailcliente\_value, datarichiesta\_key, datarichiesta\_value, orariorichiesta\_key, orariorichiesta\_value,emailproduttore\_key,emailproduttore\_value,mattina\_key,mattina\_value,pomeriggio\_key,pomeriggio\_value,cer\_key,cer\_value,descrizione\_key,descrizione\_value,sf\_key,sf\_value,classpericolo\_key,destino\_key,destino\_value,confezionamento\_key,confezionamento\_value,destinazione\_key and destinazione\_value.. To use it, simply upload an image. 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: 5
* 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: 5
* 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 #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 5
* 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 #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 5
* 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