readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: xlm-roberta-large-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-large-finetuned-combined-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: 0.9169
  - Accuracy: 0.6587
  - Precision: 0.6417
  - Recall: 0.6445
  - F1: 0.6396
  
  ## 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: 1e-05
  - train_batch_size: 8
  - eval_batch_size: 16
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.0116        | 0.5   | 711  | 0.9454          | 0.5892   | 0.6556    | 0.5190 | 0.4582 |
  | 0.8678        | 1.0   | 1422 | 0.9676          | 0.6503   | 0.6383    | 0.6076 | 0.6103 |
  | 0.7644        | 1.5   | 2133 | 0.8672          | 0.6355   | 0.6142    | 0.6206 | 0.6166 |
  | 0.8198        | 2.0   | 2844 | 0.8319          | 0.6713   | 0.6460    | 0.6448 | 0.6453 |
  | 0.6665        | 2.5   | 3555 | 0.8342          | 0.6538   | 0.6359    | 0.6414 | 0.6349 |
  | 0.6473        | 3.0   | 4266 | 0.9169          | 0.6587   | 0.6417    | 0.6445 | 0.6396 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  