readme: ---
  datasets:
  - ticket-tagger
  metrics:
  - accuracy
  model-index:
  - name: distil-bert-uncased-finetuned-github-issues
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: ticket tagger
        type: ticket tagger
        args: full
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7862
  ---
  # Model Description
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) and fine-tuning it on the
  [github ticket tagger dataset](https://tickettagger.blob.core.windows.net/datasets/dataset-labels-top3-30k-real.txt). It classifies issue into 3 common categories: Bug, Enhancement, Questions.
  
  It achieves the following results on the evaluation set:
  - Accuracy: 0.7862
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-5
  - train_batch_size: 16
  - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 0
  - num_epochs: 5
  ### Codes
  https://github.com/IvanLauLinTiong/IntelliLabel