readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: categorization-finetuned-20220721-164940-distilled-20220811-132317
    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. -->
  
  # categorization-finetuned-20220721-164940-distilled-20220811-132317
  
  This model is a fine-tuned version of [carted-nlp/categorization-finetuned-20220721-164940](https://huggingface.co/carted-nlp/categorization-finetuned-20220721-164940) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1522
  - Accuracy: 0.8783
  - F1: 0.8779
  
  ## 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: 4e-05
  - train_batch_size: 64
  - eval_batch_size: 128
  - seed: 314
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 30.0
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
  | 0.5212        | 0.56  | 2500   | 0.2564          | 0.7953   | 0.7921 |
  | 0.243         | 1.12  | 5000   | 0.2110          | 0.8270   | 0.8249 |
  | 0.2105        | 1.69  | 7500   | 0.1925          | 0.8409   | 0.8391 |
  | 0.1939        | 2.25  | 10000  | 0.1837          | 0.8476   | 0.8465 |
  | 0.1838        | 2.81  | 12500  | 0.1771          | 0.8528   | 0.8517 |
  | 0.1729        | 3.37  | 15000  | 0.1722          | 0.8564   | 0.8555 |
  | 0.1687        | 3.94  | 17500  | 0.1684          | 0.8593   | 0.8576 |
  | 0.1602        | 4.5   | 20000  | 0.1653          | 0.8614   | 0.8604 |
  | 0.1572        | 5.06  | 22500  | 0.1629          | 0.8648   | 0.8638 |
  | 0.1507        | 5.62  | 25000  | 0.1605          | 0.8654   | 0.8646 |
  | 0.1483        | 6.19  | 27500  | 0.1602          | 0.8661   | 0.8653 |
  | 0.1431        | 6.75  | 30000  | 0.1597          | 0.8669   | 0.8663 |
  | 0.1393        | 7.31  | 32500  | 0.1581          | 0.8691   | 0.8687 |
  | 0.1374        | 7.87  | 35000  | 0.1556          | 0.8704   | 0.8697 |
  | 0.1321        | 8.43  | 37500  | 0.1558          | 0.8707   | 0.8700 |
  | 0.1328        | 9.0   | 40000  | 0.1536          | 0.8719   | 0.8711 |
  | 0.1261        | 9.56  | 42500  | 0.1544          | 0.8716   | 0.8708 |
  | 0.1256        | 10.12 | 45000  | 0.1541          | 0.8731   | 0.8725 |
  | 0.122         | 10.68 | 47500  | 0.1520          | 0.8741   | 0.8734 |
  | 0.1196        | 11.25 | 50000  | 0.1529          | 0.8734   | 0.8728 |
  | 0.1182        | 11.81 | 52500  | 0.1510          | 0.8758   | 0.8751 |
  | 0.1145        | 12.37 | 55000  | 0.1526          | 0.8746   | 0.8737 |
  | 0.1141        | 12.93 | 57500  | 0.1512          | 0.8765   | 0.8759 |
  | 0.1094        | 13.5  | 60000  | 0.1517          | 0.8760   | 0.8753 |
  | 0.1098        | 14.06 | 62500  | 0.1513          | 0.8771   | 0.8764 |
  | 0.1058        | 14.62 | 65000  | 0.1506          | 0.8775   | 0.8768 |
  | 0.1048        | 15.18 | 67500  | 0.1521          | 0.8774   | 0.8768 |
  | 0.1028        | 15.74 | 70000  | 0.1520          | 0.8778   | 0.8773 |
  | 0.1006        | 16.31 | 72500  | 0.1517          | 0.8780   | 0.8774 |
  | 0.1001        | 16.87 | 75000  | 0.1505          | 0.8794   | 0.8790 |
  | 0.0971        | 17.43 | 77500  | 0.1520          | 0.8784   | 0.8778 |
  | 0.0973        | 17.99 | 80000  | 0.1514          | 0.8796   | 0.8790 |
  | 0.0938        | 18.56 | 82500  | 0.1516          | 0.8795   | 0.8789 |
  | 0.0942        | 19.12 | 85000  | 0.1522          | 0.8794   | 0.8789 |
  | 0.0918        | 19.68 | 87500  | 0.1518          | 0.8799   | 0.8793 |
  | 0.0909        | 20.24 | 90000  | 0.1528          | 0.8803   | 0.8796 |
  | 0.0901        | 20.81 | 92500  | 0.1516          | 0.8799   | 0.8793 |
  | 0.0882        | 21.37 | 95000  | 0.1519          | 0.8800   | 0.8794 |
  | 0.088         | 21.93 | 97500  | 0.1517          | 0.8802   | 0.8798 |
  | 0.086         | 22.49 | 100000 | 0.1530          | 0.8800   | 0.8795 |
  | 0.0861        | 23.05 | 102500 | 0.1523          | 0.8806   | 0.8801 |
  | 0.0846        | 23.62 | 105000 | 0.1524          | 0.8808   | 0.8802 |
  | 0.0843        | 24.18 | 107500 | 0.1522          | 0.8805   | 0.8800 |
  | 0.0836        | 24.74 | 110000 | 0.1525          | 0.8808   | 0.8803 |
  | 0.083         | 25.3  | 112500 | 0.1528          | 0.8810   | 0.8803 |
  | 0.0829        | 25.87 | 115000 | 0.1528          | 0.8808   | 0.8802 |
  | 0.082         | 26.43 | 117500 | 0.1529          | 0.8808   | 0.8802 |
  | 0.0818        | 26.99 | 120000 | 0.1525          | 0.8811   | 0.8805 |
  | 0.0816        | 27.55 | 122500 | 0.1526          |