readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-mbert-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. -->
  
  # hing-mbert-finetuned-non-code-mixed-DS
  
  This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.6572
  - Accuracy: 0.6429
  - Precision: 0.6334
  - Recall: 0.6231
  - F1: 0.6262
  
  ## 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: 4.932923543227153e-05
  - train_batch_size: 4
  - eval_batch_size: 8
  - seed: 43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.005         | 0.5   | 926  | 0.9346          | 0.5707   | 0.5844    | 0.5274 | 0.5108 |
  | 0.969         | 1.0   | 1852 | 1.0295          | 0.5858   | 0.5893    | 0.5685 | 0.5455 |
  | 0.8976        | 1.5   | 2778 | 1.0491          | 0.5739   | 0.5712    | 0.5295 | 0.5152 |
  | 0.8578        | 2.0   | 3704 | 0.8577          | 0.6343   | 0.6443    | 0.6379 | 0.6318 |
  | 0.7164        | 2.5   | 4630 | 1.3325          | 0.6300   | 0.6219    | 0.5932 | 0.5939 |
  | 0.7391        | 3.0   | 5556 | 1.0329          | 0.6537   | 0.6489    | 0.6519 | 0.6467 |
  | 0.5454        | 3.5   | 6482 | 1.6031          | 0.6375   | 0.6525    | 0.6188 | 0.6218 |
  | 0.4927        | 4.0   | 7408 | 1.6572          | 0.6429   | 0.6334    | 0.6231 | 0.6262 |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  