readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-roberta-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. -->
  
  # hing-roberta-finetuned-ours-DS
  
  This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7267
  - Accuracy: 0.68
  - Precision: 0.6320
  - Recall: 0.6234
  - F1: 0.6133
  
  ## 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: 1.0638650088808569e-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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.0144        | 0.99  | 99   | 0.8326          | 0.585    | 0.4260    | 0.5589 | 0.4470 |
  | 0.7626        | 1.98  | 198  | 0.7314          | 0.625    | 0.5366    | 0.5541 | 0.4828 |
  | 0.6243        | 2.97  | 297  | 0.7064          | 0.65     | 0.5702    | 0.5888 | 0.5562 |
  | 0.5539        | 3.96  | 396  | 0.7261          | 0.695    | 0.6598    | 0.6362 | 0.6326 |
  | 0.4881        | 4.95  | 495  | 0.7267          | 0.68     | 0.6320    | 0.6234 | 0.6133 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  