readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: xlm-roberta-large-finetuned-ours-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-large-finetuned-ours-DS
  
  This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9568
  - Accuracy: 0.71
  - Precision: 0.6689
  - Recall: 0.6607
  - F1: 0.6637
  
  ## 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: 1.6820964947491663e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.9953        | 1.99  | 199  | 0.7955          | 0.66     | 0.7533    | 0.5971 | 0.5352 |
  | 0.6638        | 3.98  | 398  | 0.8043          | 0.735    | 0.7068    | 0.6782 | 0.6846 |
  | 0.3457        | 5.97  | 597  | 0.9568          | 0.71     | 0.6689    | 0.6607 | 0.6637 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  