readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: categorization-finetuned-20220721-164940-distilled-20220810-185342
    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-20220810-185342
  
  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: 0.0639
  - Accuracy: 0.87
  - F1: 0.8690
  
  ## 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: 1e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - 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: linear
  - lr_scheduler_warmup_steps: 1500
  - num_epochs: 30.0
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
  | 0.269         | 0.56  | 2500   | 0.1280          | 0.7547   | 0.7461 |
  | 0.125         | 1.12  | 5000   | 0.1052          | 0.7960   | 0.7916 |
  | 0.1079        | 1.69  | 7500   | 0.0950          | 0.8132   | 0.8102 |
  | 0.0992        | 2.25  | 10000  | 0.0898          | 0.8216   | 0.8188 |
  | 0.0938        | 2.81  | 12500  | 0.0859          | 0.8294   | 0.8268 |
  | 0.0891        | 3.37  | 15000  | 0.0828          | 0.8349   | 0.8329 |
  | 0.0863        | 3.94  | 17500  | 0.0806          | 0.8391   | 0.8367 |
  | 0.0834        | 4.5   | 20000  | 0.0788          | 0.8417   | 0.8400 |
  | 0.081         | 5.06  | 22500  | 0.0774          | 0.8449   | 0.8430 |
  | 0.0792        | 5.62  | 25000  | 0.0754          | 0.8475   | 0.8460 |
  | 0.0778        | 6.19  | 27500  | 0.0749          | 0.8489   | 0.8474 |
  | 0.0758        | 6.75  | 30000  | 0.0738          | 0.8517   | 0.8502 |
  | 0.0745        | 7.31  | 32500  | 0.0729          | 0.8531   | 0.8519 |
  | 0.0733        | 7.87  | 35000  | 0.0720          | 0.8544   | 0.8528 |
  | 0.072         | 8.43  | 37500  | 0.0714          | 0.8559   | 0.8546 |
  | 0.0716        | 9.0   | 40000  | 0.0707          | 0.8565   | 0.8554 |
  | 0.0701        | 9.56  | 42500  | 0.0704          | 0.8574   | 0.8558 |
  | 0.0693        | 10.12 | 45000  | 0.0700          | 0.8581   | 0.8569 |
  | 0.0686        | 10.68 | 47500  | 0.0690          | 0.8600   | 0.8588 |
  | 0.0675        | 11.25 | 50000  | 0.0690          | 0.8605   | 0.8593 |
  | 0.0673        | 11.81 | 52500  | 0.0682          | 0.8614   | 0.8603 |
  | 0.0663        | 12.37 | 55000  | 0.0682          | 0.8619   | 0.8606 |
  | 0.0657        | 12.93 | 57500  | 0.0675          | 0.8634   | 0.8624 |
  | 0.0648        | 13.5  | 60000  | 0.0674          | 0.8636   | 0.8625 |
  | 0.0647        | 14.06 | 62500  | 0.0668          | 0.8644   | 0.8633 |
  | 0.0638        | 14.62 | 65000  | 0.0669          | 0.8648   | 0.8635 |
  | 0.0634        | 15.18 | 67500  | 0.0665          | 0.8654   | 0.8643 |
  | 0.063         | 15.74 | 70000  | 0.0663          | 0.8664   | 0.8654 |
  | 0.0623        | 16.31 | 72500  | 0.0662          | 0.8663   | 0.8652 |
  | 0.0622        | 16.87 | 75000  | 0.0657          | 0.8669   | 0.8660 |
  | 0.0615        | 17.43 | 77500  | 0.0658          | 0.8670   | 0.8660 |
  | 0.0616        | 17.99 | 80000  | 0.0655          | 0.8676   | 0.8667 |
  | 0.0608        | 18.56 | 82500  | 0.0653          | 0.8683   | 0.8672 |
  | 0.0606        | 19.12 | 85000  | 0.0653          | 0.8679   | 0.8669 |
  | 0.0602        | 19.68 | 87500  | 0.0648          | 0.8690   | 0.8680 |
  | 0.0599        | 20.24 | 90000  | 0.0650          | 0.8688   | 0.8677 |
  | 0.0598        | 20.81 | 92500  | 0.0647          | 0.8689   | 0.8680 |
  | 0.0592        | 21.37 | 95000  | 0.0647          | 0.8692   | 0.8681 |
  | 0.0591        | 21.93 | 97500  | 0.0646          | 0.8698   | 0.8688 |
  | 0.0587        | 22.49 | 100000 | 0.0645          | 0.8699   | 0.8690 |
  | 0.0586        | 23.05 | 102500 | 0.0644          | 0.8699   | 0.8690 |
  | 0.0583        | 23.62 | 105000 | 0.0644          | 0.8699   | 0.8690 |
  | 0.058         | 24.18 | 107500 | 0.0642          | 0.8703   | 0.8693 |
  | 0.058         | 24.74 | 110000 | 0.0642          | 0.8704   | 0.8694 |
  | 0.0578        | 25.3  | 112500 | 0.0641          | 0.8703   | 0.8693 |
  | 0.0576        | 25.87 | 115000 | 0.0641          | 0.8708   | 0.8699 |
  | 0.0573        | 26.43 | 117500 | 0.0641          | 0.8708   | 0.8698 |
  | 0.0574        | 26.99 | 120000 | 0.0639          | 0.8711   | 0.8702 |
  | 0.0571        | 27.55 | 122500 | 0.0640          | 0.