readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: xlm-roberta-base-finetuned-TRAC-DS
    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-finetuned-TRAC-DS
  
  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: 1.0206
  - Accuracy: 0.6814
  - Precision: 0.6561
  - Recall: 0.6528
  - F1: 0.6543
  
  ## 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: 8
  - eval_batch_size: 16
  - seed: 43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.9928        | 0.5   | 612  | 0.9026          | 0.6201   | 0.5845    | 0.5812 | 0.5809 |
  | 0.8756        | 1.0   | 1224 | 0.7883          | 0.6373   | 0.6358    | 0.6382 | 0.6251 |
  | 0.7793        | 1.5   | 1836 | 0.8551          | 0.6340   | 0.6226    | 0.6368 | 0.6020 |
  | 0.7667        | 2.0   | 2448 | 0.7861          | 0.6618   | 0.6518    | 0.6637 | 0.6442 |
  | 0.6619        | 2.5   | 3060 | 0.8597          | 0.6887   | 0.6662    | 0.6472 | 0.6503 |
  | 0.6786        | 3.0   | 3672 | 0.7905          | 0.6634   | 0.6587    | 0.6658 | 0.6513 |
  | 0.573         | 3.5   | 4284 | 0.9263          | 0.6797   | 0.6575    | 0.6488 | 0.6514 |
  | 0.5805        | 4.0   | 4896 | 0.8351          | 0.6944   | 0.6719    | 0.6740 | 0.6723 |
  | 0.5069        | 4.5   | 5508 | 0.9772          | 0.6748   | 0.6564    | 0.6572 | 0.6546 |
  | 0.5085        | 5.0   | 6120 | 1.0206          | 0.6814   | 0.6561    | 0.6528 | 0.6543 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  