readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - filter_v2
  metrics:
  - f1
  - accuracy
  model-index:
  - name: favs_filter_classification_v2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: filter_v2
        type: filter_v2
        config: default
        split: train
        args: default
      metrics:
      - name: F1
        type: f1
        value: 0.9761904761904762
      - name: Accuracy
        type: accuracy
        value: 0.9545454545454546
  ---
  
  <!-- 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_filter_classification_v2
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_v2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2016
  - F1: 0.9762
  - Roc Auc: 0.9844
  - Accuracy: 0.9545
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
  | 0.6596        | 1.0   | 16   | 0.6086          | 0.2687 | 0.5474  | 0.0      |
  | 0.5448        | 2.0   | 32   | 0.5354          | 0.3824 | 0.6063  | 0.0      |
  | 0.5106        | 3.0   | 48   | 0.4874          | 0.4444 | 0.6382  | 0.0455   |
  | 0.4353        | 4.0   | 64   | 0.4301          | 0.5352 | 0.6889  | 0.1818   |
  | 0.3699        | 5.0   | 80   | 0.3890          | 0.6579 | 0.7640  | 0.3636   |
  | 0.349         | 6.0   | 96   | 0.3663          | 0.6667 | 0.7633  | 0.3182   |
  | 0.3104        | 7.0   | 112  | 0.3327          | 0.7105 | 0.7953  | 0.4545   |
  | 0.3023        | 8.0   | 128  | 0.2971          | 0.7733 | 0.8303  | 0.5455   |
  | 0.2676        | 9.0   | 144  | 0.2766          | 0.8395 | 0.8861  | 0.7727   |
  | 0.2374        | 10.0  | 160  | 0.2541          | 0.8537 | 0.8980  | 0.7727   |
  | 0.2238        | 11.0  | 176  | 0.2399          | 0.9024 | 0.9293  | 0.8182   |
  | 0.2084        | 12.0  | 192  | 0.2221          | 0.9286 | 0.9531  | 0.8636   |
  | 0.2143        | 13.0  | 208  | 0.2138          | 0.9286 | 0.9531  | 0.8636   |
  | 0.1846        | 14.0  | 224  | 0.2016          | 0.9762 | 0.9844  | 0.9545   |
  | 0.1812        | 15.0  | 240  | 0.1957          | 0.9762 | 0.9844  | 0.9545   |
  | 0.1756        | 16.0  | 256  | 0.1881          | 0.9647 | 0.9806  | 0.9091   |
  | 0.1662        | 17.0  | 272  | 0.1845          | 0.9762 | 0.9844  | 0.9545   |
  | 0.1715        | 18.0  | 288  | 0.1802          | 0.9762 | 0.9844  | 0.9545   |
  | 0.1585        | 19.0  | 304  | 0.1782          | 0.9762 | 0.9844  | 0.9545   |
  | 0.1595        | 20.0  | 320  | 0.1775          | 0.9762 | 0.9844  | 0.9545   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  