readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: xlm-roberta-base-finetuned-combined-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-combined-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: 1.0232
  - Accuracy: 0.6362
  - Precision: 0.6193
  - Recall: 0.6204
  - F1: 0.6160
  
  ## 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: 4.1187640010910775e-05
  - train_batch_size: 16
  - eval_batch_size: 32
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.0408        | 1.0   | 711  | 1.0206          | 0.5723   | 0.5597    | 0.5122 | 0.4897 |
  | 0.9224        | 2.0   | 1422 | 0.9092          | 0.5695   | 0.5745    | 0.5610 | 0.5572 |
  | 0.8395        | 3.0   | 2133 | 0.8878          | 0.6088   | 0.6083    | 0.6071 | 0.5981 |
  | 0.7418        | 3.99  | 2844 | 0.8828          | 0.6088   | 0.6009    | 0.6068 | 0.5936 |
  | 0.6484        | 4.99  | 3555 | 0.9636          | 0.6355   | 0.6235    | 0.6252 | 0.6184 |
  | 0.5644        | 5.99  | 4266 | 1.0232          | 0.6362   | 0.6193    | 0.6204 | 0.6160 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  