readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: indic-bert-finetuned-non-code-mixed-DS
    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. -->
  
  # indic-bert-finetuned-non-code-mixed-DS
  
  This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9997
  - Accuracy: 0.5620
  - Precision: 0.5591
  - Recall: 0.5203
  - F1: 0.5078
  
  ## 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: 1e-06
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 43
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.0673        | 3.99  | 926  | 1.0361          | 0.4142   | 0.4092    | 0.3851 | 0.2750 |
  | 1.0144        | 7.98  | 1852 | 1.0147          | 0.5146   | 0.5851    | 0.4714 | 0.4184 |
  | 0.9882        | 11.97 | 2778 | 1.0045          | 0.5599   | 0.5728    | 0.5191 | 0.5047 |
  | 0.9699        | 15.97 | 3704 | 1.0004          | 0.5642   | 0.5620    | 0.5264 | 0.5193 |
  | 0.9591        | 19.96 | 4630 | 0.9997          | 0.5620   | 0.5591    | 0.5203 | 0.5078 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  