readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: lilt-ruroberta
    results: []
  ---
  
  <!-- 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. -->
  
  # lilt-ruroberta
  
  This model was trained from scratch on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4919
  - Comment: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}
  - Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
  - Labname: {'precision': 0.5833333333333334, 'recall': 0.6666666666666666, 'f1': 0.6222222222222222, 'number': 21}
  - Laboratory: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
  - Measure: {'precision': 0.5833333333333334, 'recall': 0.7777777777777778, 'f1': 0.6666666666666666, 'number': 9}
  - Ref Value: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
  - Result: {'precision': 0.25, 'recall': 0.25, 'f1': 0.25, 'number': 12}
  - Overall Precision: 0.4528
  - Overall Recall: 0.4
  - Overall F1: 0.4248
  - Overall Accuracy: 0.8698
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 25
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Comment                                                   | Date                                                      | Labname                                                                                                 | Laboratory                                                | Measure                                                                                                | Ref Value                                                 | Result                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 2.4398        | 5.0   | 5    | 1.5928          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}                                 | 0.0               | 0.0            | 0.0        | 0.5850           |
  | 1.4788        | 10.0  | 10   | 1.1857          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}                                 | 0.0               | 0.0            | 0.0        | 0.6512           |
  | 0.9806        | 15.0  | 15   | 0.8188          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.21875, 'recall': 0.3333333333333333, 'f1': 0.2641509433962264, 'number': 21}            | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.5, 'recall': 0.1111111111111111, 'f1': 0.1818181818181818, 'number': 9}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}                                 | 0.1667            | 0.1333         | 0.1481     | 0.7660           |
  | 0.6358        | 20.0  | 20   | 0.5763          | {'preci