readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: categorization-finetuned-20220721-164940-distilled-20220811-013354
    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-20220811-013354
  
  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: 0.0645
  - Accuracy: 0.8776
  - F1: 0.8768
  
  ## 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: 64
  - eval_batch_size: 96
  - 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: 1500
  - num_epochs: 30.0
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
  | 0.2702        | 0.56  | 2500   | 0.1290          | 0.7832   | 0.7783 |
  | 0.1246        | 1.12  | 5000   | 0.1047          | 0.8169   | 0.8137 |
  | 0.1066        | 1.69  | 7500   | 0.0945          | 0.8301   | 0.8276 |
  | 0.0975        | 2.25  | 10000  | 0.0888          | 0.8386   | 0.8367 |
  | 0.0917        | 2.81  | 12500  | 0.0849          | 0.8445   | 0.8428 |
  | 0.0865        | 3.37  | 15000  | 0.0818          | 0.8496   | 0.8484 |
  | 0.0835        | 3.94  | 17500  | 0.0796          | 0.8526   | 0.8509 |
  | 0.08          | 4.5   | 20000  | 0.0777          | 0.8552   | 0.8542 |
  | 0.0778        | 5.06  | 22500  | 0.0763          | 0.8580   | 0.8567 |
  | 0.0753        | 5.62  | 25000  | 0.0744          | 0.8604   | 0.8592 |
  | 0.0739        | 6.19  | 27500  | 0.0738          | 0.8614   | 0.8603 |
  | 0.0716        | 6.75  | 30000  | 0.0729          | 0.8630   | 0.8620 |
  | 0.0701        | 7.31  | 32500  | 0.0719          | 0.8645   | 0.8638 |
  | 0.0689        | 7.87  | 35000  | 0.0708          | 0.8657   | 0.8647 |
  | 0.067         | 8.43  | 37500  | 0.0705          | 0.8671   | 0.8660 |
  | 0.0669        | 9.0   | 40000  | 0.0699          | 0.8681   | 0.8674 |
  | 0.0647        | 9.56  | 42500  | 0.0697          | 0.8683   | 0.8673 |
  | 0.0641        | 10.12 | 45000  | 0.0693          | 0.8691   | 0.8681 |
  | 0.063         | 10.68 | 47500  | 0.0685          | 0.8702   | 0.8694 |
  | 0.0618        | 11.25 | 50000  | 0.0681          | 0.8709   | 0.8701 |
  | 0.0614        | 11.81 | 52500  | 0.0675          | 0.8720   | 0.8712 |
  | 0.0601        | 12.37 | 55000  | 0.0678          | 0.8724   | 0.8713 |
  | 0.0598        | 12.93 | 57500  | 0.0670          | 0.8732   | 0.8725 |
  | 0.0584        | 13.5  | 60000  | 0.0670          | 0.8732   | 0.8723 |
  | 0.0584        | 14.06 | 62500  | 0.0665          | 0.8740   | 0.8732 |
  | 0.0572        | 14.62 | 65000  | 0.0665          | 0.8744   | 0.8734 |
  | 0.0567        | 15.18 | 67500  | 0.0661          | 0.8753   | 0.8745 |
  | 0.0561        | 15.74 | 70000  | 0.0660          | 0.8756   | 0.8750 |
  | 0.0554        | 16.31 | 72500  | 0.0661          | 0.8759   | 0.8751 |
  | 0.0552        | 16.87 | 75000  | 0.0656          | 0.8755   | 0.8749 |
  | 0.0544        | 17.43 | 77500  | 0.0657          | 0.8762   | 0.8754 |
  | 0.0544        | 17.99 | 80000  | 0.0654          | 0.8767   | 0.8760 |
  | 0.0534        | 18.56 | 82500  | 0.0654          | 0.8767   | 0.8759 |
  | 0.0534        | 19.12 | 85000  | 0.0653          | 0.8773   | 0.8767 |
  | 0.0528        | 19.68 | 87500  | 0.0649          | 0.8775   | 0.8768 |
  | 0.0525        | 20.24 | 90000  | 0.0651          | 0.8776   | 0.8769 |
  | 0.0523        | 20.81 | 92500  | 0.0649          | 0.8775   | 0.8768 |
  | 0.0517        | 21.37 | 95000  | 0.0648          | 0.8782   | 0.8775 |
  | 0.0516        | 21.93 | 97500  | 0.0648          | 0.8783   | 0.8776 |
  | 0.0511        | 22.49 | 100000 | 0.0648          | 0.8781   | 0.8774 |
  | 0.0511        | 23.05 | 102500 | 0.0647          | 0.8783   | 0.8776 |
  | 0.0508        | 23.62 | 105000 | 0.0647          | 0.8785   | 0.8778 |
  | 0.0505        | 24.18 | 107500 | 0.0647          | 0.8785   | 0.8777 |
  | 0.0505        | 24.74 | 110000 | 0.0646          | 0.8788   | 0.8781 |
  | 0.0503        | 25.3  | 112500 | 0.0646          | 0.8786   | 0.8779 |
  | 0.0502        | 25.87 | 115000 | 0.0646          | 0.8789   | 0.8782 |
  | 0.0501        | 26.43 | 117500 | 0.0646          | 0.8788   | 0.8781 |
  | 0.0501        | 26.99 | 120000 | 0.0645          | 0.8791   | 0.8784 |
  | 0.05          | 27.55 | 122500 | 0.0646          | 