readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - invoice
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-invoice
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: Invoice
        type: invoice
        args: invoice
      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
  ---
  
  # 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
  