readme: ---
  license: mit
  tags:
  - text-classification
  - generated_from_trainer
  datasets:
  - xnli
  metrics:
  - accuracy
  model-index:
  - name: xnli_xlm_r_base_only_en_automodel_single_gpu
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: xnli
        type: xnli
        config: en
        split: train
        args: en
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.3333333333333333
  ---
  
  <!-- 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. -->
  
  # xnli_xlm_r_base_only_en_automodel_single_gpu
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xnli dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0986
  - Accuracy: 0.3333
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.1064        | 0.04  | 1000  | 1.1003          | 0.3333   |
  | 1.1042        | 0.08  | 2000  | 1.1006          | 0.3333   |
  | 1.1049        | 0.12  | 3000  | 1.0992          | 0.3333   |
  | 1.1037        | 0.16  | 4000  | 1.1019          | 0.3333   |
  | 1.1037        | 0.2   | 5000  | 1.0986          | 0.3333   |
  | 1.1028        | 0.24  | 6000  | 1.1014          | 0.3333   |
  | 1.1044        | 0.29  | 7000  | 1.1059          | 0.3333   |
  | 1.102         | 0.33  | 8000  | 1.1000          | 0.3333   |
  | 1.1022        | 0.37  | 9000  | 1.1012          | 0.3333   |
  | 1.1019        | 0.41  | 10000 | 1.0995          | 0.3333   |
  | 1.1018        | 0.45  | 11000 | 1.0990          | 0.3333   |
  | 1.103         | 0.49  | 12000 | 1.1018          | 0.3333   |
  | 1.1016        | 0.53  | 13000 | 1.0989          | 0.3333   |
  | 1.1021        | 0.57  | 14000 | 1.0995          | 0.3333   |
  | 1.1012        | 0.61  | 15000 | 1.1026          | 0.3333   |
  | 1.1012        | 0.65  | 16000 | 1.1000          | 0.3333   |
  | 1.1018        | 0.69  | 17000 | 1.0992          | 0.3333   |
  | 1.1004        | 0.73  | 18000 | 1.0996          | 0.3333   |
  | 1.101         | 0.77  | 19000 | 1.0987          | 0.3333   |
  | 1.1011        | 0.81  | 20000 | 1.1001          | 0.3333   |
  | 1.1006        | 0.86  | 21000 | 1.0991          | 0.3333   |
  | 1.1006        | 0.9   | 22000 | 1.1028          | 0.3333   |
  | 1.1003        | 0.94  | 23000 | 1.0988          | 0.3333   |
  | 1.1006        | 0.98  | 24000 | 1.0987          | 0.3333   |
  | 1.1008        | 1.02  | 25000 | 1.0995          | 0.3333   |
  | 1.1011        | 1.06  | 26000 | 1.0987          | 0.3333   |
  | 1.1003        | 1.1   | 27000 | 1.0987          | 0.3333   |
  | 1.1002        | 1.14  | 28000 | 1.1020          | 0.3333   |
  | 1.1           | 1.18  | 29000 | 1.0988          | 0.3333   |
  | 1.1002        | 1.22  | 30000 | 1.0995          | 0.3333   |
  | 1.1001        | 1.26  | 31000 | 1.0989          | 0.3333   |
  | 1.1001        | 1.3   | 32000 | 1.0986          | 0.3333   |
  | 1.0999        | 1.34  | 33000 | 1.0989          | 0.3333   |
  | 1.1004        | 1.39  | 34000 | 1.0987          | 0.3333   |
  | 1.0993        | 1.43  | 35000 | 1.0989          | 0.3333   |
  | 1.1003        | 1.47  | 36000 | 1.0989          | 0.3333   |
  | 1.0999        | 1.51  | 37000 | 1.0991          | 0.3333   |
  | 1.0999        | 1.55  | 38000 | 1.0993          | 0.3333   |
  | 1.0994        | 1.59  | 39000 | 1.0993          | 0.3333   |
  | 1.0994        | 1.63  | 40000 | 1.0989          | 0.3333   |
  | 1.0999        | 1.67  | 41000 | 1.0988          | 0.3333   |
  | 1.0995        | 1.71  | 42000 | 1.0996          | 0.3333   |
  | 1.1003        | 1.75  | 43000 | 1.0987          | 0.3333   |
  | 1.0996        | 1.79  | 44000 | 1.0987          | 0.3333   |
  | 1.0996        | 1.83  | 45000 | 1.0990          | 0.3333   |
  | 1.0994        | 1.87  | 46000 | 1.0990          | 0.3333   |
  | 1.0992        | 1.91  | 47000 | 1.1000          | 0.3333   |
  | 1.0992        | 1.96  | 48000 | 1.0989          | 0.3333   |
  | 1.0991        | 2.0   | 49000 | 1.0991          | 0.3333   |
  | 1.099         | 2.04  | 50000 | 1.0987          | 0.3333   |
  | 1.0992        | 2.08  | 51000 | 1.0987          | 0.3333   |
  | 1.0995        | 2.12  | 52000 | 1.0988          | 0.3333   |
  | 1.0994        | 2.16  | 53000 | 1.0989          | 0.3333   |
  | 1.0994        | 2.2   | 54000 | 1.0989          | 0.3333   |
  | 1.0993        | 2.24  | 55000 | 1.0988          | 0.3333   |
  | 1.0988        | 2.28  | 56000 | 1