readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-mbert-ours-run-1
    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-1
  
  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: 2.9965
  - Accuracy: 0.665
  - Precision: 0.6151
  - Recall: 0.6082
  - F1: 0.6090
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.9757        | 1.0   | 100  | 0.7526          | 0.665    | 0.6358    | 0.6149 | 0.5854 |
  | 0.7227        | 2.0   | 200  | 1.1062          | 0.69     | 0.6679    | 0.6031 | 0.6025 |
  | 0.4345        | 3.0   | 300  | 1.2601          | 0.67     | 0.6512    | 0.6031 | 0.6001 |
  | 0.2738        | 4.0   | 400  | 1.4485          | 0.67     | 0.6333    | 0.5968 | 0.6050 |
  | 0.1171        | 5.0   | 500  | 1.9132          | 0.65     | 0.6216    | 0.6170 | 0.5944 |
  | 0.0941        | 6.0   | 600  | 1.8293          | 0.685    | 0.6420    | 0.6439 | 0.6409 |
  | 0.0348        | 7.0   | 700  | 2.3249          | 0.675    | 0.6424    | 0.6386 | 0.6384 |
  | 0.0317        | 8.0   | 800  | 2.4134          | 0.67     | 0.6171    | 0.6120 | 0.6128 |
  | 0.0056        | 9.0   | 900  | 2.6733          | 0.68     | 0.6343    | 0.6313 | 0.6300 |
  | 0.0095        | 10.0  | 1000 | 2.5950          | 0.685    | 0.6318    | 0.6289 | 0.6295 |
  | 0.0081        | 11.0  | 1100 | 2.3885          | 0.69     | 0.6407    | 0.6434 | 0.6410 |
  | 0.023         | 12.0  | 1200 | 2.4087          | 0.67     | 0.6206    | 0.6231 | 0.6212 |
  | 0.0054        | 13.0  | 1300 | 2.4516          | 0.675    | 0.6229    | 0.6227 | 0.6128 |
  | 0.0047        | 14.0  | 1400 | 2.6152          | 0.68     | 0.6285    | 0.6256 | 0.6263 |
  | 0.0063        | 15.0  | 1500 | 2.8077          | 0.69     | 0.6498    | 0.6281 | 0.6309 |
  | 0.0028        | 16.0  | 1600 | 2.7084          | 0.675    | 0.6254    | 0.6214 | 0.6207 |
  | 0.0025        | 17.0  | 1700 | 2.8360          | 0.67     | 0.6175    | 0.6128 | 0.6145 |
  | 0.0011        | 18.0  | 1800 | 2.8591          | 0.655    | 0.6001    | 0.5958 | 0.5971 |
  | 0.0005        | 19.0  | 1900 | 2.9419          | 0.665    | 0.6151    | 0.6082 | 0.6090 |
  | 0.0002        | 20.0  | 2000 | 2.9965          | 0.665    | 0.6151    | 0.6082 | 0.6090 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Tokenizers 0.13.2
  