readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-roberta-CM-run-5
    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-5
  
  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: 2.6447
  - Accuracy: 0.7525
  - Precision: 0.7030
  - Recall: 0.7120
  - F1: 0.7064
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.9492        | 1.0   | 497  | 0.7476          | 0.6157   | 0.6060    | 0.6070 | 0.5171 |
  | 0.7013        | 2.0   | 994  | 0.7093          | 0.6982   | 0.6716    | 0.6864 | 0.6663 |
  | 0.4871        | 3.0   | 1491 | 0.8294          | 0.7284   | 0.6714    | 0.6867 | 0.6723 |
  | 0.3838        | 4.0   | 1988 | 1.1275          | 0.7505   | 0.6969    | 0.7025 | 0.6994 |
  | 0.254         | 5.0   | 2485 | 1.3831          | 0.7264   | 0.6781    | 0.6975 | 0.6850 |
  | 0.1765        | 6.0   | 2982 | 2.0625          | 0.7384   | 0.7068    | 0.6948 | 0.6896 |
  | 0.1127        | 7.0   | 3479 | 1.9691          | 0.7425   | 0.6925    | 0.7065 | 0.6982 |
  | 0.0757        | 8.0   | 3976 | 2.3871          | 0.7425   | 0.7183    | 0.6926 | 0.6924 |
  | 0.0572        | 9.0   | 4473 | 2.4037          | 0.7344   | 0.6916    | 0.6929 | 0.6882 |
  | 0.0458        | 10.0  | 4970 | 2.3062          | 0.7586   | 0.7174    | 0.7219 | 0.7164 |
  | 0.0405        | 11.0  | 5467 | 2.5591          | 0.7445   | 0.6925    | 0.6964 | 0.6942 |
  | 0.0292        | 12.0  | 5964 | 2.5215          | 0.7384   | 0.6875    | 0.6998 | 0.6917 |
  | 0.0264        | 13.0  | 6461 | 2.7551          | 0.7586   | 0.7122    | 0.7035 | 0.7037 |
  | 0.0299        | 14.0  | 6958 | 2.6536          | 0.7465   | 0.7114    | 0.7088 | 0.7035 |
  | 0.0208        | 15.0  | 7455 | 2.5190          | 0.7505   | 0.6989    | 0.7083 | 0.7030 |
  | 0.0263        | 16.0  | 7952 | 2.7092          | 0.7485   | 0.7076    | 0.6998 | 0.6962 |
  | 0.0077        | 17.0  | 8449 | 2.5933          | 0.7525   | 0.7042    | 0.7143 | 0.7081 |
  | 0.009         | 18.0  | 8946 | 2.5831          | 0.7485   | 0.6991    | 0.7152 | 0.7050 |
  | 0.0108        | 19.0  | 9443 | 2.6360          | 0.7545   | 0.7050    | 0.7167 | 0.7098 |
  | 0.0077        | 20.0  | 9940 | 2.6447          | 0.7525   | 0.7030    | 0.7120 | 0.7064 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  