readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-fa-aug-ner
    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. -->
  
  # xlm-roberta-base-fa-aug-ner
  
  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: 0.2714
  - Precision: 0.5446
  - Recall: 0.5882
  - F1: 0.5655
  - Accuracy: 0.9201
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.5864        | 1.0   | 784  | 0.3619          | 0.4741    | 0.4005 | 0.4342 | 0.8993   |
  | 0.2659        | 2.0   | 1568 | 0.3057          | 0.5016    | 0.5178 | 0.5096 | 0.9093   |
  | 0.2293        | 3.0   | 2352 | 0.2790          | 0.5380    | 0.5607 | 0.5491 | 0.9180   |
  | 0.1945        | 4.0   | 3136 | 0.2715          | 0.5451    | 0.5672 | 0.5559 | 0.9191   |
  | 0.1794        | 5.0   | 3920 | 0.2714          | 0.5446    | 0.5882 | 0.5655 | 0.9201   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  