| | --- |
| | 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 |
