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license: apache-2.0
---

# GitHub issues classifier (using zero shot classification)

This model was trained using the [**Zero-shot classifier distillation**](https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation) method
with the [BART-large-mnli](https://huggingface.co/facebook/bart-large-mnli) model as teacher model, to train a classifier on Github issues from the [Github Issues Prediction dataset](https://www.kaggle.com/datasets/anmolkumar/github-bugs-prediction)

## Labels

As per the dataset Kaggle competition, the classifier predicts wether an issue is a bug, feature or question. After playing around with different labels pre-training I've used a different mapping
of labels that yielded better predictions (see notebook [here](https://www.kaggle.com/code/antoinemacia/zero-shot-classifier-for-bug-analysis/edit) for details), labels being

* issue
* feature request
* question 


## Training data

This model was trained on 5k titles (unlabelled, as per distillation requierements) from the train dataset of [Github Issues Prediction dataset](https://www.kaggle.com/datasets/anmolkumar/github-bugs-prediction)

## Acknowledgements

* Joe Davison and his article on [Zero-Shot Learning in Modern NLP](https://joeddav.github.io/blog/2020/05/29/ZSL.html)
* Jeremy Evans and his notebook on [Iterate like a grandmaster](https://www.kaggle.com/code/antoinemacia/iterate-like-a-grandmaster)