readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-roberta-NCM-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-NCM-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: 3.3405
  - Accuracy: 0.6505
  - Precision: 0.6410
  - Recall: 0.6318
  - F1: 0.6350
  
  ## 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     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.8975        | 1.0   | 927   | 0.9553          | 0.6127   | 0.5994    | 0.6026 | 0.5930 |
  | 0.6924        | 2.0   | 1854  | 0.8426          | 0.6505   | 0.6535    | 0.6344 | 0.6372 |
  | 0.472         | 3.0   | 2781  | 1.0533          | 0.6570   | 0.6449    | 0.6442 | 0.6442 |
  | 0.3271        | 4.0   | 3708  | 1.8111          | 0.6624   | 0.6635    | 0.6407 | 0.6448 |
  | 0.2368        | 5.0   | 4635  | 2.1234          | 0.6483   | 0.6297    | 0.6288 | 0.6267 |
  | 0.172         | 6.0   | 5562  | 2.5340          | 0.6419   | 0.6312    | 0.6164 | 0.6199 |
  | 0.1251        | 7.0   | 6489  | 2.5758          | 0.6472   | 0.6405    | 0.6311 | 0.6336 |
  | 0.0943        | 8.0   | 7416  | 2.9090          | 0.6332   | 0.6337    | 0.6090 | 0.6124 |
  | 0.0919        | 9.0   | 8343  | 2.8236          | 0.6494   | 0.6394    | 0.6301 | 0.6329 |
  | 0.0851        | 10.0  | 9270  | 2.9368          | 0.6570   | 0.6448    | 0.6405 | 0.6422 |
  | 0.0602        | 11.0  | 10197 | 3.2925          | 0.6289   | 0.6221    | 0.6111 | 0.6140 |
  | 0.0551        | 12.0  | 11124 | 3.1185          | 0.6397   | 0.6239    | 0.6108 | 0.6131 |
  | 0.0498        | 13.0  | 12051 | 3.0170          | 0.6559   | 0.6400    | 0.6322 | 0.6341 |
  | 0.0309        | 14.0  | 12978 | 3.0934          | 0.6537   | 0.6481    | 0.6386 | 0.6410 |
  | 0.0303        | 15.0  | 13905 | 3.1530          | 0.6440   | 0.6292    | 0.6258 | 0.6272 |
  | 0.028         | 16.0  | 14832 | 3.1491          | 0.6570   | 0.6502    | 0.6346 | 0.6385 |
  | 0.0199        | 17.0  | 15759 | 3.2515          | 0.6526   | 0.6394    | 0.6295 | 0.6324 |
  | 0.0245        | 18.0  | 16686 | 3.2644          | 0.6526   | 0.6494    | 0.6315 | 0.6356 |
  | 0.0159        | 19.0  | 17613 | 3.3344          | 0.6483   | 0.6377    | 0.6295 | 0.6324 |
  | 0.0116        | 20.0  | 18540 | 3.3405          | 0.6505   | 0.6410    | 0.6318 | 0.6350 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  