readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-finetuned-ner-without-data-sort
    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. -->
  
  # roberta-finetuned-ner-without-data-sort
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0420
  - Precision: 0.9914
  - Recall: 0.9909
  - F1: 0.9912
  - Accuracy: 0.9920
  
  ## 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: 2e-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
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 213  | 0.1879          | 0.9378    | 0.9414 | 0.9396 | 0.9493   |
  | No log        | 2.0   | 426  | 0.1038          | 0.9725    | 0.9750 | 0.9737 | 0.9751   |
  | 0.4424        | 3.0   | 639  | 0.0701          | 0.9861    | 0.9851 | 0.9856 | 0.9863   |
  | 0.4424        | 4.0   | 852  | 0.0637          | 0.9882    | 0.9880 | 0.9881 | 0.9880   |
  | 0.0675        | 5.0   | 1065 | 0.0546          | 0.9851    | 0.9878 | 0.9865 | 0.9879   |
  | 0.0675        | 6.0   | 1278 | 0.0480          | 0.9894    | 0.9904 | 0.9899 | 0.9901   |
  | 0.0675        | 7.0   | 1491 | 0.0473          | 0.9919    | 0.9904 | 0.9912 | 0.9911   |
  | 0.0426        | 8.0   | 1704 | 0.0441          | 0.9921    | 0.9916 | 0.9919 | 0.9921   |
  | 0.0426        | 9.0   | 1917 | 0.0426          | 0.9921    | 0.9916 | 0.9919 | 0.9922   |
  | 0.033         | 10.0  | 2130 | 0.0420          | 0.9914    | 0.9909 | 0.9912 | 0.9920   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  