readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: SOCIEDADES
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: sroie
        type: sroie
        config: discharge
        split: train
        args: discharge
      metrics:
      - name: Precision
        type: precision
        value: 0.8387096774193549
      - name: Recall
        type: recall
        value: 0.9629629629629629
      - name: F1
        type: f1
        value: 0.896551724137931
      - name: Accuracy
        type: accuracy
        value: 0.9990103908955963
  ---
  
  <!-- 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. -->
  
  # SOCIEDADES
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the sroie dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0018
  - Precision: 0.8387
  - Recall: 0.9630
  - F1: 0.8966
  - Accuracy: 0.9990
  
  ## 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: 1000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 4.76  | 100  | 0.0027          | 0.8966    | 0.9630 | 0.9286 | 0.9990   |
  | No log        | 9.52  | 200  | 0.0000          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 14.29 | 300  | 0.0000          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 19.05 | 400  | 0.0000          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0039        | 23.81 | 500  | 0.0000          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0039        | 28.57 | 600  | 0.0000          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0039        | 33.33 | 700  | 0.0001          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0039        | 38.1  | 800  | 0.0022          | 0.8387    | 0.9630 | 0.8966 | 0.9990   |
  | 0.0039        | 42.86 | 900  | 0.0018          | 0.8387    | 0.9630 | 0.8966 | 0.9990   |
  | 0.0001        | 47.62 | 1000 | 0.0018          | 0.8387    | 0.9630 | 0.8966 | 0.9990   |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.2.2
  - Tokenizers 0.13.1
  