readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-mbert-ours-run-4
    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-ours-run-4
  
  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: 3.0173
  - Accuracy: 0.68
  - Precision: 0.6330
  - Recall: 0.6325
  - F1: 0.6320
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.9781        | 1.0   | 100  | 0.8852          | 0.55     | 0.4044    | 0.5284 | 0.4211 |
  | 0.7568        | 2.0   | 200  | 0.8110          | 0.655    | 0.5994    | 0.6013 | 0.5762 |
  | 0.5121        | 3.0   | 300  | 0.9735          | 0.65     | 0.6145    | 0.6131 | 0.5965 |
  | 0.314         | 4.0   | 400  | 1.1324          | 0.65     | 0.6305    | 0.6355 | 0.6266 |
  | 0.1298        | 5.0   | 500  | 2.8247          | 0.61     | 0.5804    | 0.5087 | 0.5092 |
  | 0.1013        | 6.0   | 600  | 2.8183          | 0.635    | 0.6212    | 0.5674 | 0.5667 |
  | 0.0989        | 7.0   | 700  | 2.3235          | 0.635    | 0.5944    | 0.5922 | 0.5916 |
  | 0.0481        | 8.0   | 800  | 2.5240          | 0.68     | 0.6334    | 0.6172 | 0.6221 |
  | 0.018         | 9.0   | 900  | 2.6782          | 0.65     | 0.6123    | 0.6054 | 0.6062 |
  | 0.0285        | 10.0  | 1000 | 2.3400          | 0.67     | 0.6206    | 0.6327 | 0.6189 |
  | 0.014         | 11.0  | 1100 | 2.6558          | 0.66     | 0.6098    | 0.5992 | 0.6018 |
  | 0.0085        | 12.0  | 1200 | 2.9366          | 0.66     | 0.6076    | 0.5961 | 0.5991 |
  | 0.0106        | 13.0  | 1300 | 2.8567          | 0.665    | 0.6198    | 0.6193 | 0.6186 |
  | 0.0097        | 14.0  | 1400 | 3.1526          | 0.64     | 0.6089    | 0.5975 | 0.5954 |
  | 0.0022        | 15.0  | 1500 | 2.7305          | 0.69     | 0.6404    | 0.6404 | 0.6398 |
  | 0.0016        | 16.0  | 1600 | 2.7670          | 0.69     | 0.6418    | 0.6434 | 0.6425 |
  | 0.0017        | 17.0  | 1700 | 2.8193          | 0.7      | 0.6533    | 0.6566 | 0.6546 |
  | 0.0009        | 18.0  | 1800 | 2.9959          | 0.685    | 0.6400    | 0.6389 | 0.6384 |
  | 0.0006        | 19.0  | 1900 | 3.0153          | 0.68     | 0.6330    | 0.6325 | 0.6320 |
  | 0.0005        | 20.0  | 2000 | 3.0173          | 0.68     | 0.6330    | 0.6325 | 0.6320 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Tokenizers 0.13.2
  