readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hing-mbert-finetuned-TRAC-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-TRAC-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: 1.9044
  - Accuracy: 0.7010
  - Precision: 0.6772
  - Recall: 0.6723
  - F1: 0.6740
  
  ## 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: 2.824279936868144e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.837         | 1.0   | 1224 | 0.7640          | 0.6422   | 0.6377    | 0.6475 | 0.6277 |
  | 0.6164        | 2.0   | 2448 | 0.8456          | 0.6724   | 0.6581    | 0.6623 | 0.6547 |
  | 0.434         | 3.0   | 3672 | 1.0284          | 0.6969   | 0.6715    | 0.6771 | 0.6729 |
  | 0.267         | 4.0   | 4896 | 1.5533          | 0.6912   | 0.6644    | 0.6675 | 0.6655 |
  | 0.1542        | 5.0   | 6120 | 1.9044          | 0.7010   | 0.6772    | 0.6723 | 0.6740 |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  