readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - filter_sort
  metrics:
  - f1
  - accuracy
  model-index:
  - name: favs-filtersort-multilabel-classification-bert-base-cased
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: filter_sort
        type: filter_sort
        config: default
        split: train
        args: default
      metrics:
      - name: F1
        type: f1
        value: 0.7428571428571428
      - name: Accuracy
        type: accuracy
        value: 0.2
  ---
  
  <!-- 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. -->
  
  # favs-filtersort-multilabel-classification-bert-base-cased
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_sort dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3066
  - F1: 0.7429
  - Roc Auc: 0.8142
  - Accuracy: 0.2
  
  ## 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: 1.5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
  | 0.7601        | 1.0   | 12   | 0.6966          | 0.2564 | 0.4518  | 0.0      |
  | 0.6757        | 2.0   | 24   | 0.5629          | 0.6667 | 0.7785  | 0.0      |
  | 0.5796        | 3.0   | 36   | 0.4652          | 0.6286 | 0.7477  | 0.0      |
  | 0.5026        | 4.0   | 48   | 0.4161          | 0.6479 | 0.7605  | 0.0      |
  | 0.4282        | 5.0   | 60   | 0.3830          | 0.6849 | 0.7862  | 0.0      |
  | 0.4085        | 6.0   | 72   | 0.3658          | 0.7273 | 0.7962  | 0.0      |
  | 0.3847        | 7.0   | 84   | 0.3538          | 0.7353 | 0.8052  | 0.0      |
  | 0.3829        | 8.0   | 96   | 0.3457          | 0.6761 | 0.7772  | 0.0      |
  | 0.3758        | 9.0   | 108  | 0.3409          | 0.6857 | 0.7810  | 0.0      |
  | 0.3487        | 10.0  | 120  | 0.3327          | 0.7143 | 0.7976  | 0.0      |
  | 0.3421        | 11.0  | 132  | 0.3268          | 0.6866 | 0.7758  | 0.0      |
  | 0.3351        | 12.0  | 144  | 0.3183          | 0.7059 | 0.7886  | 0.0      |
  | 0.3245        | 13.0  | 156  | 0.3149          | 0.7246 | 0.8014  | 0.0      |
  | 0.3191        | 14.0  | 168  | 0.3087          | 0.7246 | 0.8014  | 0.1      |
  | 0.3083        | 15.0  | 180  | 0.3066          | 0.7429 | 0.8142  | 0.2      |
  | 0.3061        | 16.0  | 192  | 0.3062          | 0.7429 | 0.8142  | 0.2      |
  | 0.2935        | 17.0  | 204  | 0.3017          | 0.7429 | 0.8142  | 0.2      |
  | 0.2888        | 18.0  | 216  | 0.3009          | 0.7429 | 0.8142  | 0.2      |
  | 0.297         | 19.0  | 228  | 0.3022          | 0.7429 | 0.8142  | 0.2      |
  | 0.2868        | 20.0  | 240  | 0.3014          | 0.7429 | 0.8142  | 0.2      |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  