readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: digikala_products_parsbert_model
    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. -->
  
  # digikala_products_parsbert_model
  
  This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.7245
  
  ## 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: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | No log        | 1.0   | 25   | 7.8477          |
  | No log        | 2.0   | 50   | 7.0014          |
  | No log        | 3.0   | 75   | 6.3235          |
  | No log        | 4.0   | 100  | 5.6651          |
  | No log        | 5.0   | 125  | 4.9101          |
  | No log        | 6.0   | 150  | 4.2448          |
  | No log        | 7.0   | 175  | 3.8656          |
  | No log        | 8.0   | 200  | 3.4329          |
  | No log        | 9.0   | 225  | 3.3204          |
  | No log        | 10.0  | 250  | 3.0740          |
  | No log        | 11.0  | 275  | 2.9556          |
  | No log        | 12.0  | 300  | 2.9938          |
  | No log        | 13.0  | 325  | 2.8620          |
  | No log        | 14.0  | 350  | 2.7879          |
  | No log        | 15.0  | 375  | 2.8619          |
  | No log        | 16.0  | 400  | 2.8521          |
  | No log        | 17.0  | 425  | 2.7920          |
  | No log        | 18.0  | 450  | 2.8494          |
  | No log        | 19.0  | 475  | 2.8209          |
  | 4.1477        | 20.0  | 500  | 2.8471          |
  | 4.1477        | 21.0  | 525  | 2.8478          |
  | 4.1477        | 22.0  | 550  | 2.7904          |
  | 4.1477        | 23.0  | 575  | 2.7961          |
  | 4.1477        | 24.0  | 600  | 2.7494          |
  | 4.1477        | 25.0  | 625  | 2.8250          |
  | 4.1477        | 26.0  | 650  | 2.7439          |
  | 4.1477        | 27.0  | 675  | 2.7539          |
  | 4.1477        | 28.0  | 700  | 2.7635          |
  | 4.1477        | 29.0  | 725  | 2.7742          |
  | 4.1477        | 30.0  | 750  | 2.7711          |
  | 4.1477        | 31.0  | 775  | 2.8243          |
  | 4.1477        | 32.0  | 800  | 2.7547          |
  | 4.1477        | 33.0  | 825  | 2.7690          |
  | 4.1477        | 34.0  | 850  | 2.7178          |
  | 4.1477        | 35.0  | 875  | 2.7554          |
  | 4.1477        | 36.0  | 900  | 2.7701          |
  | 4.1477        | 37.0  | 925  | 2.7953          |
  | 4.1477        | 38.0  | 950  | 2.8062          |
  | 4.1477        | 39.0  | 975  | 2.7637          |
  | 2.772         | 40.0  | 1000 | 2.7675          |
  | 2.772         | 41.0  | 1025 | 2.7953          |
  | 2.772         | 42.0  | 1050 | 2.8003          |
  | 2.772         | 43.0  | 1075 | 2.7484          |
  | 2.772         | 44.0  | 1100 | 2.7292          |
  | 2.772         | 45.0  | 1125 | 2.7287          |
  | 2.772         | 46.0  | 1150 | 2.6998          |
  | 2.772         | 47.0  | 1175 | 2.7381          |
  | 2.772         | 48.0  | 1200 | 2.7196          |
  | 2.772         | 49.0  | 1225 | 2.7450          |
  | 2.772         | 50.0  | 1250 | 2.7293          |
  | 2.772         | 51.0  | 1275 | 2.7216          |
  | 2.772         | 52.0  | 1300 | 2.7981          |
  | 2.772         | 53.0  | 1325 | 2.7405          |
  | 2.772         | 54.0  | 1350 | 2.7895          |
  | 2.772         | 55.0  | 1375 | 2.7092          |
  | 2.772         | 56.0  | 1400 | 2.7977          |
  | 2.772         | 57.0  | 1425 | 2.7012          |
  | 2.772         | 58.0  | 1450 | 2.7752          |
  | 2.772         | 59.0  | 1475 | 2.7469          |
  | 2.742         | 60.0  | 1500 | 2.7205          |
  | 2.742         | 61.0  | 1525 | 2.7752          |
  | 2.742         | 62.0  | 1550 | 2.6942          |
  | 2.742         | 63.0  | 1575 | 2.6916          |
  | 2.742         | 64.0  | 1600 | 2.8169          |
  | 2.742         | 65.0  | 1625 | 2.7256          |
  | 2.742         | 66.0  | 1650 | 2.6844          |
  | 2.742         | 67.0  | 1675 | 2.7544          |
  | 2.742         | 68.0  | 1700 | 2.7083          |
  | 2.742         | 69.0  | 1725 | 2.7286          |
  | 2.742         | 70.0  | 1750 | 2.7492          |
  | 2.742         | 71.0  | 1775 | 2.6946          |
  | 2.742         | 72.0  | 1800 | 2.7395          |
  | 2.742         | 73.0  | 1825 | 2.7597          |
  | 2.742         | 74.0  | 1850 | 2.7953          |
  | 2.742         | 75.0  | 1875 | 2.7468          |
  | 2.742         | 76.0  | 1900 | 2.7274          |
  | 2.742         | 77.0  | 1925 | 2.7507          |
  |