readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: categorization-finetuned-20220721-164940-pruned-20220803-184651
    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-pruned-20220803-184651
  
  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: 0.4673
  - Accuracy: 0.8760
  - F1: 0.8751
  
  ## 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: 48
  - eval_batch_size: 48
  - seed: 314
  - gradient_accumulation_steps: 6
  - total_train_batch_size: 288
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
  | 0.3404        | 0.51  | 2000  | 0.4329          | 0.8872   | 0.8865 |
  | 0.3433        | 1.01  | 4000  | 0.4280          | 0.8883   | 0.8876 |
  | 0.3281        | 1.52  | 6000  | 0.4302          | 0.8890   | 0.8883 |
  | 0.331         | 2.02  | 8000  | 0.4265          | 0.8891   | 0.8885 |
  | 0.3224        | 2.53  | 10000 | 0.4300          | 0.8881   | 0.8874 |
  | 0.3361        | 3.04  | 12000 | 0.4291          | 0.8889   | 0.8882 |
  | 0.3323        | 3.54  | 14000 | 0.4337          | 0.8878   | 0.8871 |
  | 0.3556        | 4.05  | 16000 | 0.4345          | 0.8857   | 0.8851 |
  | 0.3663        | 4.56  | 18000 | 0.4417          | 0.8836   | 0.8828 |
  | 0.3902        | 5.06  | 20000 | 0.4555          | 0.8789   | 0.8781 |
  | 0.4036        | 5.57  | 22000 | 0.4556          | 0.8788   | 0.8779 |
  | 0.4305        | 6.07  | 24000 | 0.4697          | 0.8751   | 0.8742 |
  | 0.4501        | 6.58  | 26000 | 0.4763          | 0.8738   | 0.8725 |
  | 0.4733        | 7.09  | 28000 | 0.4857          | 0.8710   | 0.8700 |
  | 0.4851        | 7.59  | 30000 | 0.4863          | 0.8705   | 0.8695 |
  | 0.4846        | 8.1   | 32000 | 0.4849          | 0.8708   | 0.8698 |
  | 0.4856        | 8.61  | 34000 | 0.4835          | 0.8707   | 0.8695 |
  | 0.4774        | 9.11  | 36000 | 0.4797          | 0.8719   | 0.8708 |
  | 0.4635        | 9.62  | 38000 | 0.4776          | 0.8728   | 0.8717 |
  | 0.4561        | 10.12 | 40000 | 0.4746          | 0.8739   | 0.8729 |
  | 0.4475        | 10.63 | 42000 | 0.4705          | 0.8749   | 0.8740 |
  | 0.4413        | 11.14 | 44000 | 0.4691          | 0.8754   | 0.8744 |
  | 0.4389        | 11.64 | 46000 | 0.4679          | 0.8760   | 0.8750 |
  | 0.4361        | 12.15 | 48000 | 0.4677          | 0.8759   | 0.8749 |
  | 0.4362        | 12.65 | 50000 | 0.4672          | 0.8763   | 0.8753 |
  | 0.4309        | 13.16 | 52000 | 0.4671          | 0.8761   | 0.8751 |
  | 0.4316        | 13.67 | 54000 | 0.4670          | 0.8764   | 0.8754 |
  | 0.4321        | 14.17 | 56000 | 0.4668          | 0.8764   | 0.8755 |
  | 0.4311        | 14.68 | 58000 | 0.4668          | 0.8764   | 0.8754 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0.dev0
  - Pytorch 1.9.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.11.6
  