readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: 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. -->
  
  # roberta-large-finetuned-code-mixed-DS
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.1340
  - Accuracy: 0.7203
  - Precision: 0.6584
  - Recall: 0.6548
  - F1: 0.6558
  
  ## 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: 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.9729        | 1.0   | 248  | 0.7491          | 0.6922   | 0.6434    | 0.6625 | 0.6358 |
  | 0.7474        | 1.99  | 496  | 0.6947          | 0.7183   | 0.6712    | 0.6915 | 0.6760 |
  | 0.5938        | 2.99  | 744  | 0.7370          | 0.7123   | 0.6624    | 0.6839 | 0.6642 |
  | 0.4264        | 3.98  | 992  | 0.8820          | 0.7123   | 0.6540    | 0.6636 | 0.6492 |
  | 0.2806        | 4.98  | 1240 | 1.2022          | 0.7404   | 0.6807    | 0.6694 | 0.6742 |
  | 0.2239        | 5.98  | 1488 | 1.3933          | 0.7223   | 0.6593    | 0.6587 | 0.6568 |
  | 0.1585        | 6.97  | 1736 | 1.8543          | 0.7304   | 0.6730    | 0.6763 | 0.6737 |
  | 0.1302        | 7.97  | 1984 | 2.0783          | 0.7143   | 0.6495    | 0.6520 | 0.6504 |
  | 0.1008        | 8.96  | 2232 | 2.3523          | 0.7183   | 0.6588    | 0.6561 | 0.6552 |
  | 0.0793        | 9.96  | 2480 | 2.5260          | 0.7163   | 0.6516    | 0.6566 | 0.6538 |
  | 0.0498        | 10.96 | 2728 | 2.6074          | 0.7425   | 0.6902    | 0.6817 | 0.6830 |
  | 0.0484        | 11.95 | 2976 | 2.6758          | 0.7284   | 0.6687    | 0.6734 | 0.6709 |
  | 0.0409        | 12.95 | 3224 | 2.8658          | 0.7425   | 0.6817    | 0.6756 | 0.6781 |
  | 0.0239        | 13.94 | 3472 | 2.9484          | 0.7465   | 0.6980    | 0.6818 | 0.6870 |
  | 0.025         | 14.94 | 3720 | 3.0827          | 0.7304   | 0.6778    | 0.6577 | 0.6641 |
  | 0.0286        | 15.94 | 3968 | 3.0011          | 0.7183   | 0.6509    | 0.6475 | 0.6491 |
  | 0.0264        | 16.93 | 4216 | 3.1581          | 0.7264   | 0.6645    | 0.6563 | 0.6595 |
  | 0.009         | 17.93 | 4464 | 3.1200          | 0.7223   | 0.6589    | 0.6561 | 0.6569 |
  | 0.012         | 18.92 | 4712 | 3.1364          | 0.7203   | 0.6573    | 0.6503 | 0.6525 |
  | 0.017         | 19.92 | 4960 | 3.1340          | 0.7203   | 0.6584    | 0.6548 | 0.6558 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  