readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-roberta-CM-run-4
    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-4
  
  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: 2.5827
  - Accuracy: 0.7525
  - Precision: 0.6967
  - Recall: 0.7004
  - F1: 0.6980
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.8734        | 1.0   | 497  | 0.7673          | 0.7203   | 0.6617    | 0.6600 | 0.6604 |
  | 0.6245        | 2.0   | 994  | 0.7004          | 0.7485   | 0.6951    | 0.7137 | 0.7015 |
  | 0.4329        | 3.0   | 1491 | 1.0469          | 0.7223   | 0.6595    | 0.6640 | 0.6538 |
  | 0.2874        | 4.0   | 1988 | 1.3103          | 0.7586   | 0.7064    | 0.7157 | 0.7104 |
  | 0.1837        | 5.0   | 2485 | 1.7916          | 0.7425   | 0.6846    | 0.6880 | 0.6861 |
  | 0.1121        | 6.0   | 2982 | 2.0721          | 0.7465   | 0.7064    | 0.7041 | 0.7003 |
  | 0.0785        | 7.0   | 3479 | 2.3469          | 0.7425   | 0.6898    | 0.6795 | 0.6807 |
  | 0.0609        | 8.0   | 3976 | 2.2775          | 0.7404   | 0.6819    | 0.6881 | 0.6845 |
  | 0.0817        | 9.0   | 4473 | 2.1992          | 0.7686   | 0.7342    | 0.7147 | 0.7166 |
  | 0.042         | 10.0  | 4970 | 2.2359          | 0.7565   | 0.7211    | 0.7141 | 0.7106 |
  | 0.0463        | 11.0  | 5467 | 2.2291          | 0.7646   | 0.7189    | 0.7186 | 0.7177 |
  | 0.027         | 12.0  | 5964 | 2.3955          | 0.7525   | 0.6994    | 0.7073 | 0.7028 |
  | 0.0314        | 13.0  | 6461 | 2.4256          | 0.7565   | 0.7033    | 0.7153 | 0.7082 |
  | 0.0251        | 14.0  | 6958 | 2.4578          | 0.7565   | 0.7038    | 0.7025 | 0.7027 |
  | 0.0186        | 15.0  | 7455 | 2.5984          | 0.7565   | 0.7141    | 0.6945 | 0.6954 |
  | 0.0107        | 16.0  | 7952 | 2.5068          | 0.7425   | 0.6859    | 0.7016 | 0.6912 |
  | 0.0134        | 17.0  | 8449 | 2.5876          | 0.7606   | 0.7018    | 0.7041 | 0.7029 |
  | 0.0145        | 18.0  | 8946 | 2.6011          | 0.7626   | 0.7072    | 0.7079 | 0.7073 |
  | 0.0108        | 19.0  | 9443 | 2.5861          | 0.7545   | 0.6973    | 0.7017 | 0.6990 |
  | 0.0076        | 20.0  | 9940 | 2.5827          | 0.7525   | 0.6967    | 0.7004 | 0.6980 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  