readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - swiss_judgment_prediction
  metrics:
  - accuracy
  model-index:
  - name: xlm-roberta-large-xnli-finetuned-mnli-SJP
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: swiss_judgment_prediction
        type: swiss_judgment_prediction
        args: all_languages
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7957142857142857
  ---
  
  <!-- 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-large-xnli-finetuned-mnli-SJP
  
  This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3456
  - Accuracy: 0.7957
  
  ## 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: 2
  - eval_batch_size: 2
  - 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 | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 5    | 1.8460          | 0.7956   |
  | No log        | 2.0   | 10   | 1.3456          | 0.7957   |
  | No log        | 3.0   | 15   | 1.2799          | 0.7957   |
  | No log        | 4.0   | 20   | 1.2866          | 0.7957   |
  | No log        | 5.0   | 25   | 1.3162          | 0.7956   |
  
  
  ### Framework versions
  
  - Transformers 4.20.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  