readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-mbert-ours-run-5
    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-5
  
  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: 3.2437
  - Accuracy: 0.665
  - Precision: 0.6223
  - Recall: 0.5991
  - F1: 0.6039
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.9643        | 1.0   | 100  | 0.7996          | 0.69     | 0.6596    | 0.6593 | 0.6521 |
  | 0.6951        | 2.0   | 200  | 1.0464          | 0.66     | 0.6597    | 0.5831 | 0.5734 |
  | 0.4245        | 3.0   | 300  | 0.9640          | 0.64     | 0.6025    | 0.6033 | 0.6010 |
  | 0.238         | 4.0   | 400  | 1.6744          | 0.68     | 0.7095    | 0.6445 | 0.6359 |
  | 0.1477        | 5.0   | 500  | 1.7115          | 0.665    | 0.6362    | 0.6422 | 0.6360 |
  | 0.1206        | 6.0   | 600  | 2.0459          | 0.635    | 0.5749    | 0.5752 | 0.5726 |
  | 0.0528        | 7.0   | 700  | 2.5698          | 0.66     | 0.6230    | 0.5904 | 0.5985 |
  | 0.0525        | 8.0   | 800  | 2.2729          | 0.625    | 0.5741    | 0.5860 | 0.5733 |
  | 0.0174        | 9.0   | 900  | 2.6227          | 0.635    | 0.6099    | 0.6044 | 0.6019 |
  | 0.0088        | 10.0  | 1000 | 2.8854          | 0.63     | 0.5699    | 0.5676 | 0.5680 |
  | 0.0085        | 11.0  | 1100 | 3.2173          | 0.655    | 0.6043    | 0.5771 | 0.5821 |
  | 0.0121        | 12.0  | 1200 | 3.1270          | 0.665    | 0.6214    | 0.5903 | 0.5971 |
  | 0.0141        | 13.0  | 1300 | 2.6648          | 0.655    | 0.5981    | 0.5978 | 0.5961 |
  | 0.0116        | 14.0  | 1400 | 3.1711          | 0.665    | 0.6192    | 0.5915 | 0.5971 |
  | 0.007         | 15.0  | 1500 | 3.0954          | 0.66     | 0.6156    | 0.5961 | 0.6009 |
  | 0.0037        | 16.0  | 1600 | 3.3065          | 0.65     | 0.6027    | 0.5791 | 0.5824 |
  | 0.0031        | 17.0  | 1700 | 3.1715          | 0.665    | 0.6177    | 0.5999 | 0.6048 |
  | 0.0021        | 18.0  | 1800 | 3.1602          | 0.665    | 0.6220    | 0.6029 | 0.6082 |
  | 0.0021        | 19.0  | 1900 | 3.2027          | 0.655    | 0.6096    | 0.5893 | 0.5937 |
  | 0.0018        | 20.0  | 2000 | 3.2437          | 0.665    | 0.6223    | 0.5991 | 0.6039 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Tokenizers 0.13.2
  