readme: ---
  model-index:
  - name: eurocorpV4
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: sroie
        type: sroie
        config: discharge
        split: test
        args: discharge
      metrics:
      - name: Precision
        type: precision
        value: 0.9548022598870056
      - name: Recall
        type: recall
        value: 0.9602272727272727
      - name: F1
        type: f1
        value: 0.9575070821529744
      - name: Accuracy
        type: accuracy
        value: 0.9819121447028424
  pipeline_tag: document-question-answering
  ---
  
  <!-- 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. -->
  
  # eurocorpV4
  
  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: 0.1239
  - Precision: 0.9548
  - Recall: 0.9602
  - F1: 0.9575
  - Accuracy: 0.9819
  
  ## 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        | 0.33  | 100  | 0.3452          | 0.7399    | 0.7273 | 0.7335 | 0.9276   |
  | No log        | 0.66  | 200  | 0.1688          | 0.9070    | 0.8864 | 0.8966 | 0.9677   |
  | No log        | 0.99  | 300  | 0.1135          | 0.9157    | 0.9261 | 0.9209 | 0.9742   |
  | No log        | 1.32  | 400  | 0.1319          | 0.9261    | 0.9261 | 0.9261 | 0.9742   |
  | 0.2834        | 1.65  | 500  | 0.1300          | 0.9375    | 0.9375 | 0.9375 | 0.9755   |
  | 0.2834        | 1.98  | 600  | 0.1384          | 0.9598    | 0.9489 | 0.9543 | 0.9780   |
  | 0.2834        | 2.31  | 700  | 0.1298          | 0.9266    | 0.9318 | 0.9292 | 0.9767   |
  | 0.2834        | 2.64  | 800  | 0.1232          | 0.9438    | 0.9545 | 0.9492 | 0.9806   |
  | 0.2834        | 2.97  | 900  | 0.1261          | 0.9548    | 0.9602 | 0.9575 | 0.9819   |
  | 0.0247        | 3.3   | 1000 | 0.1239          | 0.9548    | 0.9602 | 0.9575 | 0.9819   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.2.2
  - Tokenizers 0.13.2