readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: indic-bert-finetuned-TRAC-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-TRAC-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: 0.9922
  - Accuracy: 0.5825
  - Precision: 0.5493
  - Recall: 0.5412
  - F1: 0.5428
  
  ## 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: 32
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.0755        | 1.99  | 612  | 1.0346          | 0.5057   | 0.4072    | 0.4554 | 0.3806 |
  | 1.0175        | 3.99  | 1224 | 1.0096          | 0.5678   | 0.6135    | 0.5011 | 0.4422 |
  | 0.9974        | 5.98  | 1836 | 1.0010          | 0.5776   | 0.5637    | 0.5140 | 0.4799 |
  | 0.9812        | 7.97  | 2448 | 0.9960          | 0.5694   | 0.5426    | 0.5283 | 0.5298 |
  | 0.9675        | 9.97  | 3060 | 0.9956          | 0.5776   | 0.5565    | 0.5422 | 0.5442 |
  | 0.9542        | 11.96 | 3672 | 0.9925          | 0.5882   | 0.5601    | 0.5420 | 0.5419 |
  | 0.944         | 13.95 | 4284 | 0.9907          | 0.5866   | 0.5525    | 0.5441 | 0.5454 |
  | 0.9347        | 15.95 | 4896 | 0.9921          | 0.5858   | 0.5527    | 0.5441 | 0.5456 |
  | 0.9271        | 17.94 | 5508 | 0.9906          | 0.5931   | 0.5596    | 0.5482 | 0.5490 |
  | 0.9236        | 19.93 | 6120 | 0.9922          | 0.5825   | 0.5493    | 0.5412 | 0.5428 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  