---
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