readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-mbert-ours-run-3
    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-3
  
  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: 2.9769
  - Accuracy: 0.675
  - Precision: 0.6433
  - Recall: 0.6344
  - F1: 0.6344
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.9089        | 1.0   | 100  | 1.0993          | 0.635    | 0.6487    | 0.5304 | 0.5060 |
  | 0.6657        | 2.0   | 200  | 0.8138          | 0.645    | 0.6550    | 0.6482 | 0.6234 |
  | 0.3858        | 3.0   | 300  | 1.1334          | 0.665    | 0.6162    | 0.6061 | 0.5995 |
  | 0.208         | 4.0   | 400  | 1.9041          | 0.685    | 0.6488    | 0.6169 | 0.6087 |
  | 0.0996        | 5.0   | 500  | 2.3735          | 0.645    | 0.5867    | 0.5781 | 0.5794 |
  | 0.0296        | 6.0   | 600  | 2.5772          | 0.665    | 0.6284    | 0.6208 | 0.6198 |
  | 0.0623        | 7.0   | 700  | 2.8906          | 0.655    | 0.6040    | 0.5916 | 0.5926 |
  | 0.0395        | 8.0   | 800  | 2.6567          | 0.675    | 0.6279    | 0.6254 | 0.6219 |
  | 0.029         | 9.0   | 900  | 2.9277          | 0.655    | 0.6208    | 0.5950 | 0.5991 |
  | 0.0194        | 10.0  | 1000 | 2.7362          | 0.665    | 0.6241    | 0.6208 | 0.6190 |
  | 0.0092        | 11.0  | 1100 | 2.5561          | 0.68     | 0.6396    | 0.6401 | 0.6385 |
  | 0.0059        | 12.0  | 1200 | 3.1112          | 0.675    | 0.6350    | 0.5967 | 0.6042 |
  | 0.0133        | 13.0  | 1300 | 2.5269          | 0.685    | 0.6520    | 0.6607 | 0.6519 |
  | 0.0051        | 14.0  | 1400 | 2.8736          | 0.68     | 0.6381    | 0.6158 | 0.6134 |
  | 0.0044        | 15.0  | 1500 | 2.9132          | 0.675    | 0.6336    | 0.6180 | 0.6200 |
  | 0.0029        | 16.0  | 1600 | 2.8701          | 0.675    | 0.6337    | 0.6214 | 0.6233 |
  | 0.0015        | 17.0  | 1700 | 2.8115          | 0.69     | 0.6475    | 0.6388 | 0.6420 |
  | 0.0019        | 18.0  | 1800 | 2.9517          | 0.67     | 0.6373    | 0.6276 | 0.6273 |
  | 0.0013        | 19.0  | 1900 | 2.9633          | 0.67     | 0.6373    | 0.6276 | 0.6273 |
  | 0.0007        | 20.0  | 2000 | 2.9769          | 0.675    | 0.6433    | 0.6344 | 0.6344 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Tokenizers 0.13.2
  