readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: xlm-roberta-base_single_finetuned_on_cedr_augmented
    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_single_finetuned_on_cedr_augmented
  
  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: 0.4650
  - Accuracy: 0.8820
  - F1: 0.8814
  - Precision: 0.8871
  - Recall: 0.8820
  
  ## 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: 0.0001
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.8868        | 1.0   | 69   | 0.4939          | 0.8403   | 0.8376 | 0.8431    | 0.8403 |
  | 0.4248        | 2.0   | 138  | 0.3969          | 0.8779   | 0.8768 | 0.8798    | 0.8779 |
  | 0.3197        | 3.0   | 207  | 0.4019          | 0.8758   | 0.8757 | 0.8758    | 0.8758 |
  | 0.2737        | 4.0   | 276  | 0.3915          | 0.8831   | 0.8827 | 0.8847    | 0.8831 |
  | 0.2053        | 5.0   | 345  | 0.4445          | 0.8643   | 0.8650 | 0.8714    | 0.8643 |
  | 0.1705        | 6.0   | 414  | 0.4650          | 0.8820   | 0.8814 | 0.8871    | 0.8820 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  