readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: categorization-finetuned-20220721-164940-distilled-20220810-123313
    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-20220810-123313
  
  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: 0.0787
  - Accuracy: 0.8416
  - F1: 0.8396
  
  ## 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: 7e-06
  - 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: 15.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
  | 0.2976        | 0.56  | 2500  | 0.1441          | 0.7219   | 0.7071 |
  | 0.1417        | 1.12  | 5000  | 0.1180          | 0.7719   | 0.7653 |
  | 0.1236        | 1.69  | 7500  | 0.1076          | 0.7901   | 0.7854 |
  | 0.1148        | 2.25  | 10000 | 0.1014          | 0.8015   | 0.7977 |
  | 0.1092        | 2.81  | 12500 | 0.0972          | 0.8089   | 0.8052 |
  | 0.1043        | 3.37  | 15000 | 0.0942          | 0.8135   | 0.8102 |
  | 0.1013        | 3.94  | 17500 | 0.0916          | 0.8181   | 0.8147 |
  | 0.0985        | 4.5   | 20000 | 0.0897          | 0.8219   | 0.8190 |
  | 0.0962        | 5.06  | 22500 | 0.0881          | 0.8241   | 0.8215 |
  | 0.0945        | 5.62  | 25000 | 0.0866          | 0.8270   | 0.8246 |
  | 0.0928        | 6.19  | 27500 | 0.0857          | 0.8286   | 0.8262 |
  | 0.0912        | 6.75  | 30000 | 0.0843          | 0.8310   | 0.8286 |
  | 0.0901        | 7.31  | 32500 | 0.0836          | 0.8321   | 0.8299 |
  | 0.0887        | 7.87  | 35000 | 0.0827          | 0.8339   | 0.8315 |
  | 0.0879        | 8.43  | 37500 | 0.0821          | 0.8350   | 0.8329 |
  | 0.0875        | 9.0   | 40000 | 0.0814          | 0.8362   | 0.8342 |
  | 0.0865        | 9.56  | 42500 | 0.0811          | 0.8370   | 0.8348 |
  | 0.0855        | 10.12 | 45000 | 0.0806          | 0.8375   | 0.8355 |
  | 0.0853        | 10.68 | 47500 | 0.0798          | 0.8386   | 0.8367 |
  | 0.0845        | 11.25 | 50000 | 0.0799          | 0.8392   | 0.8372 |
  | 0.0844        | 11.81 | 52500 | 0.0793          | 0.8401   | 0.8383 |
  | 0.0838        | 12.37 | 55000 | 0.0793          | 0.8402   | 0.8381 |
  | 0.0834        | 12.93 | 57500 | 0.0790          | 0.8410   | 0.8390 |
  | 0.0832        | 13.5  | 60000 | 0.0788          | 0.8414   | 0.8394 |
  | 0.083         | 14.06 | 62500 | 0.0787          | 0.8415   | 0.8395 |
  | 0.0828        | 14.62 | 65000 | 0.0787          | 0.8416   | 0.8396 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.11.6
  