readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - common_language
  metrics:
  - accuracy
  model-index:
  - name: language-detection-fine-tuned-on-xlm-roberta-base
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: common_language
        type: common_language
        args: full
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9760187824920342
  ---
  
  <!-- 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. -->
  
  # language-detection-fine-tuned-on-xlm-roberta-base
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the common_language dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1642
  - Accuracy: 0.9760
  
  ## 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: 3e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 1
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.0725        | 1.0   | 22194 | 0.1642          | 0.9760   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  