readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: categorization-finetuned-20220721-164940-distilled-20220811-074207
    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-20220811-074207
  
  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: 0.1499
  - Accuracy: 0.8771
  - F1: 0.8763
  
  ## 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     |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
  | 0.5644        | 0.56  | 2500   | 0.2739          | 0.7822   | 0.7774 |
  | 0.2658        | 1.12  | 5000   | 0.2288          | 0.8159   | 0.8127 |
  | 0.2307        | 1.69  | 7500   | 0.2082          | 0.8298   | 0.8273 |
  | 0.2126        | 2.25  | 10000  | 0.1970          | 0.8389   | 0.8370 |
  | 0.2012        | 2.81  | 12500  | 0.1888          | 0.8450   | 0.8433 |
  | 0.1903        | 3.37  | 15000  | 0.1829          | 0.8496   | 0.8485 |
  | 0.1846        | 3.94  | 17500  | 0.1783          | 0.8529   | 0.8511 |
  | 0.1771        | 4.5   | 20000  | 0.1750          | 0.8548   | 0.8537 |
  | 0.1726        | 5.06  | 22500  | 0.1727          | 0.8577   | 0.8564 |
  | 0.1673        | 5.62  | 25000  | 0.1683          | 0.8602   | 0.8591 |
  | 0.1648        | 6.19  | 27500  | 0.1675          | 0.8608   | 0.8597 |
  | 0.1596        | 6.75  | 30000  | 0.1657          | 0.8630   | 0.8620 |
  | 0.1563        | 7.31  | 32500  | 0.1635          | 0.8646   | 0.8639 |
  | 0.154         | 7.87  | 35000  | 0.1613          | 0.8656   | 0.8647 |
  | 0.1496        | 8.43  | 37500  | 0.1611          | 0.8666   | 0.8656 |
  | 0.1496        | 9.0   | 40000  | 0.1598          | 0.8676   | 0.8669 |
  | 0.1445        | 9.56  | 42500  | 0.1594          | 0.8681   | 0.8671 |
  | 0.1435        | 10.12 | 45000  | 0.1588          | 0.8688   | 0.8679 |
  | 0.1407        | 10.68 | 47500  | 0.1568          | 0.8703   | 0.8695 |
  | 0.1382        | 11.25 | 50000  | 0.1564          | 0.8708   | 0.8700 |
  | 0.1372        | 11.81 | 52500  | 0.1550          | 0.8720   | 0.8713 |
  | 0.1344        | 12.37 | 55000  | 0.1559          | 0.8718   | 0.8708 |
  | 0.1337        | 12.93 | 57500  | 0.1540          | 0.8735   | 0.8729 |
  | 0.1303        | 13.5  | 60000  | 0.1541          | 0.8729   | 0.8721 |
  | 0.1304        | 14.06 | 62500  | 0.1531          | 0.8735   | 0.8727 |
  | 0.1274        | 14.62 | 65000  | 0.1535          | 0.8736   | 0.8727 |
  | 0.1266        | 15.18 | 67500  | 0.1527          | 0.8750   | 0.8742 |
  | 0.1251        | 15.74 | 70000  | 0.1525          | 0.8755   | 0.8748 |
  | 0.1234        | 16.31 | 72500  | 0.1528          | 0.8753   | 0.8745 |
  | 0.1229        | 16.87 | 75000  | 0.1516          | 0.8760   | 0.8753 |
  | 0.121         | 17.43 | 77500  | 0.1523          | 0.8759   | 0.8752 |
  | 0.1212        | 17.99 | 80000  | 0.1515          | 0.8760   | 0.8754 |
  | 0.1185        | 18.56 | 82500  | 0.1514          | 0.8765   | 0.8757 |
  | 0.1186        | 19.12 | 85000  | 0.1516          | 0.8766   | 0.8760 |
  | 0.1172        | 19.68 | 87500  | 0.1506          | 0.8774   | 0.8767 |
  | 0.1164        | 20.24 | 90000  | 0.1513          | 0.8770   | 0.8763 |
  | 0.116         | 20.81 | 92500  | 0.1507          | 0.8774   | 0.8767 |
  | 0.1145        | 21.37 | 95000  | 0.1507          | 0.8777   | 0.8770 |
  | 0.1143        | 21.93 | 97500  | 0.1506          | 0.8776   | 0.8770 |
  | 0.1131        | 22.49 | 100000 | 0.1507          | 0.8779   | 0.8772 |
  | 0.1131        | 23.05 | 102500 | 0.1505          | 0.8779   | 0.8772 |
  | 0.1123        | 23.62 | 105000 | 0.1506          | 0.8781   | 0.8774 |
  | 0.1117        | 24.18 | 107500 | 0.1504          | 0.8783   | 0.8776 |
  | 0.1118        | 24.74 | 110000 | 0.1503          | 0.8784   | 0.8777 |
  | 0.1111        | 25.3  | 112500 | 0.1503          | 0.8783   | 0.8776 |
  | 0.1111        | 25.87 | 115000 | 0.1502          | 0.8784   | 0.8777 |
  | 0.1105        | 26.43 | 117500 | 0.1504          | 0.8783   | 0.8776 |
  | 0.1105        | 26.99 | 120000 | 0.1502          | 0.8786   | 0.8779 |
  | 0.1104        | 27.55 | 122500 | 0.1503          | 