readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: indic-bert-finetuned-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-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: 0.8647
  - Accuracy: 0.5795
  - Precision: 0.5485
  - Recall: 0.5287
  - F1: 0.4391
  
  ## 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: 16
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.0937        | 2.0   | 497  | 1.0813          | 0.3602   | 0.3587    | 0.4257 | 0.2834 |
  | 1.0189        | 3.99  | 994  | 0.9482          | 0.5493   | 0.3887    | 0.5246 | 0.4080 |
  | 0.9208        | 5.99  | 1491 | 0.9002          | 0.5714   | 0.3813    | 0.5292 | 0.4170 |
  | 0.8803        | 7.98  | 1988 | 0.8758          | 0.5654   | 0.3889    | 0.5300 | 0.4159 |
  | 0.8482        | 9.98  | 2485 | 0.8657          | 0.5795   | 0.3867    | 0.5365 | 0.4228 |
  | 0.8293        | 11.98 | 2982 | 0.8734          | 0.5835   | 0.3796    | 0.5298 | 0.4214 |
  | 0.8131        | 13.97 | 3479 | 0.8567          | 0.5835   | 0.5018    | 0.5414 | 0.4350 |
  | 0.8           | 15.97 | 3976 | 0.8547          | 0.5835   | 0.5610    | 0.5460 | 0.4361 |
  | 0.7933        | 17.96 | 4473 | 0.8650          | 0.5775   | 0.5317    | 0.5252 | 0.4373 |
  | 0.7835        | 19.96 | 4970 | 0.8647          | 0.5795   | 0.5485    | 0.5287 | 0.4391 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  