readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: xlm-roberta-large-finetuned-code-mixed-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-code-mixed-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: 0.7328
  - Accuracy: 0.7022
  - Precision: 0.6437
  - Recall: 0.6634
  - F1: 0.6483
  
  ## 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: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.098         | 0.5   | 248  | 1.0944          | 0.5352   | 0.2355    | 0.3344 | 0.2397 |
  | 1.0827        | 1.0   | 496  | 1.0957          | 0.5352   | 0.5789    | 0.3379 | 0.2502 |
  | 1.0503        | 1.5   | 744  | 0.9969          | 0.5312   | 0.3621    | 0.4996 | 0.3914 |
  | 0.9728        | 2.0   | 992  | 0.8525          | 0.6056   | 0.5096    | 0.5565 | 0.4678 |
  | 0.9271        | 2.49  | 1240 | 0.7809          | 0.6378   | 0.6014    | 0.6320 | 0.5963 |
  | 0.7977        | 2.99  | 1488 | 0.8290          | 0.5875   | 0.5630    | 0.5918 | 0.5390 |
  | 0.752         | 3.49  | 1736 | 0.7684          | 0.7123   | 0.6526    | 0.6610 | 0.6558 |
  | 0.6846        | 3.99  | 1984 | 0.7328          | 0.7022   | 0.6437    | 0.6634 | 0.6483 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  