readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-roberta-NCM-run-1
    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-1
  
  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: 3.2912
  - Accuracy: 0.6667
  - Precision: 0.6513
  - Recall: 0.6494
  - F1: 0.6502
  
  ## 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     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.8968        | 1.0   | 927   | 0.8552          | 0.6257   | 0.6508    | 0.5961 | 0.5969 |
  | 0.7022        | 2.0   | 1854  | 1.1142          | 0.3937   | 0.3270    | 0.3273 | 0.2051 |
  | 0.5569        | 3.0   | 2781  | 0.9130          | 0.6591   | 0.6566    | 0.6612 | 0.6509 |
  | 0.363         | 4.0   | 3708  | 1.6630          | 0.6526   | 0.6634    | 0.6414 | 0.6436 |
  | 0.2801        | 5.0   | 4635  | 2.0458          | 0.6451   | 0.6339    | 0.6345 | 0.6330 |
  | 0.1925        | 6.0   | 5562  | 2.3378          | 0.6570   | 0.6439    | 0.6254 | 0.6277 |
  | 0.1297        | 7.0   | 6489  | 2.5205          | 0.6839   | 0.6719    | 0.6651 | 0.6675 |
  | 0.114         | 8.0   | 7416  | 2.8373          | 0.6505   | 0.6379    | 0.6249 | 0.6280 |
  | 0.0994        | 9.0   | 8343  | 2.5358          | 0.6634   | 0.6539    | 0.6446 | 0.6474 |
  | 0.0977        | 10.0  | 9270  | 2.8244          | 0.6537   | 0.6489    | 0.6210 | 0.6238 |
  | 0.0623        | 11.0  | 10197 | 2.7593          | 0.6764   | 0.6602    | 0.6487 | 0.6510 |
  | 0.0537        | 12.0  | 11124 | 2.9823          | 0.6677   | 0.6679    | 0.6450 | 0.6488 |
  | 0.0432        | 13.0  | 12051 | 3.0792          | 0.6537   | 0.6465    | 0.6352 | 0.6378 |
  | 0.0406        | 14.0  | 12978 | 3.0707          | 0.6688   | 0.6592    | 0.6509 | 0.6534 |
  | 0.0296        | 15.0  | 13905 | 3.3289          | 0.6667   | 0.6596    | 0.6452 | 0.6486 |
  | 0.0288        | 16.0  | 14832 | 3.2147          | 0.6645   | 0.6592    | 0.6512 | 0.6528 |
  | 0.024         | 17.0  | 15759 | 3.3284          | 0.6645   | 0.6470    | 0.6405 | 0.6425 |
  | 0.0201        | 18.0  | 16686 | 3.2428          | 0.6688   | 0.6515    | 0.6515 | 0.6515 |
  | 0.0176        | 19.0  | 17613 | 3.2680          | 0.6710   | 0.6574    | 0.6536 | 0.6547 |
  | 0.0168        | 20.0  | 18540 | 3.2912          | 0.6667   | 0.6513    | 0.6494 | 0.6502 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  