readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-roberta-CM-run-3
    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-CM-run-3
  
  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: 2.6968
  - Accuracy: 0.7565
  - Precision: 0.7045
  - Recall: 0.7064
  - F1: 0.7050
  
  ## 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: 3e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.8232        | 1.0   | 497  | 0.7145          | 0.6620   | 0.6319    | 0.6585 | 0.6167 |
  | 0.5799        | 2.0   | 994  | 0.7155          | 0.7203   | 0.6718    | 0.6928 | 0.6672 |
  | 0.4152        | 3.0   | 1491 | 0.8823          | 0.7485   | 0.6962    | 0.7136 | 0.7022 |
  | 0.2657        | 4.0   | 1988 | 1.4502          | 0.7465   | 0.6945    | 0.7037 | 0.6968 |
  | 0.16          | 5.0   | 2485 | 2.0667          | 0.7465   | 0.6890    | 0.6827 | 0.6855 |
  | 0.0945        | 6.0   | 2982 | 2.0120          | 0.7565   | 0.7091    | 0.7159 | 0.7103 |
  | 0.0802        | 7.0   | 3479 | 2.2426          | 0.7686   | 0.7253    | 0.7065 | 0.7088 |
  | 0.059         | 8.0   | 3976 | 2.3472          | 0.7425   | 0.6844    | 0.6881 | 0.6861 |
  | 0.041         | 9.0   | 4473 | 2.4801          | 0.7666   | 0.7258    | 0.7144 | 0.7145 |
  | 0.0307        | 10.0  | 4970 | 2.6317          | 0.7545   | 0.7102    | 0.7021 | 0.7019 |
  | 0.0471        | 11.0  | 5467 | 2.4626          | 0.7364   | 0.6836    | 0.6780 | 0.6788 |
  | 0.0282        | 12.0  | 5964 | 2.3949          | 0.7586   | 0.7067    | 0.7108 | 0.7087 |
  | 0.0267        | 13.0  | 6461 | 2.4750          | 0.7465   | 0.6938    | 0.6921 | 0.6921 |
  | 0.0274        | 14.0  | 6958 | 2.5942          | 0.7565   | 0.7022    | 0.7062 | 0.7039 |
  | 0.0212        | 15.0  | 7455 | 2.6728          | 0.7404   | 0.6851    | 0.6893 | 0.6867 |
  | 0.026         | 16.0  | 7952 | 2.6683          | 0.7565   | 0.7064    | 0.7122 | 0.7085 |
  | 0.0175        | 17.0  | 8449 | 2.6646          | 0.7505   | 0.7030    | 0.7087 | 0.7039 |
  | 0.0126        | 18.0  | 8946 | 2.6948          | 0.7565   | 0.7021    | 0.7039 | 0.7030 |
  | 0.0065        | 19.0  | 9443 | 2.6984          | 0.7565   | 0.7045    | 0.7064 | 0.7050 |
  | 0.0103        | 20.0  | 9940 | 2.6968          | 0.7565   | 0.7045    | 0.7064 | 0.7050 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  