
# GitHub issues classifier (using zero shot classification)

Predicts wether a statement is a feature request, issue/bug or question 

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

* 15k of Github issues titles ("unlabeled_titles_simple.txt")
* Hypothesis used: "This request is a {}"
* Teacher model used: valhalla/distilbart-mnli-12-1
* Studend model used: distilbert-base-uncased

## Results

Agreement of student and teacher predictions: **94.82%**

See [this notebook](https://www.kaggle.com/code/antoinemacia/zero-shot-classifier-for-bug-analysis/edit) for more info on feature engineering choice made


## How to train using your own dataset
* Download training dataset from https://www.kaggle.com/datasets/anmolkumar/github-bugs-prediction
* Modify and run convert.py, updating the paths to convert to a CSV
* Run distill.py with the csv file (see [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation) for more info)

## Acknowledgements

* Joe Davison and his article on [Zero-Shot Learning in Modern NLP](https://joeddav.github.io/blog/2020/05/29/ZSL.html)
* Jeremy Howard, fast.ai and his notebook [Iterate like a grandmaster](https://www.kaggle.com/code/antoinemacia/iterate-like-a-grandmaster)
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["customer-service-tickets", "github-issues", "bart-large-mnli", "zero-shot-classification", "NLP"], "metrics": ["accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "Sign up form is not working", "example_title": "Example 1"}, {"text": "json and yaml support", "example_title": "Example 2"}, {"text": "fullscreen and tabs media key don't do what they should", "example_title": "Example 2"}]}
transformers pytorch distilbert text-classification customer-service-tickets github-issues bart-large-mnli zero-shot-classification NLP en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #customer-service-tickets #github-issues #bart-large-mnli #zero-shot-classification #NLP #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# GitHub issues classifier (using zero shot classification)

Predicts wether a statement is a feature request, issue/bug or question 

This model was trained using the Zero-shot classifier distillation method
with the BART-large-mnli model as teacher model, to train a classifier on Github issues from the Github Issues Prediction dataset

## 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 for details), labels being

* issue
* feature request
* question


## Training data

* 15k of Github issues titles ("unlabeled_titles_simple.txt")
* Hypothesis used: "This request is a {}"
* Teacher model used: valhalla/distilbart-mnli-12-1
* Studend model used: distilbert-base-uncased

## Results

Agreement of student and teacher predictions: 94.82%

See this notebook for more info on feature engineering choice made


## How to train using your own dataset
* Download training dataset from URL
* Modify and run URL, updating the paths to convert to a CSV
* Run URL with the csv file (see here for more info)

## Acknowledgements

* Joe Davison and his article on Zero-Shot Learning in Modern NLP
* Jeremy Howard, URL and his notebook Iterate like a grandmaster
# GitHub issues classifier (using zero shot classification)

Predicts wether a statement is a feature request, issue/bug or question 

This model was trained using the Zero-shot classifier distillation method
with the BART-large-mnli model as teacher model, to train a classifier on Github issues from the Github Issues Prediction dataset ## 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 for details), labels being

* issue
* feature request
* question ## Training data

* 15k of Github issues titles ("unlabeled_titles_simple.txt")
* Hypothesis used: "This request is a {}"
* Teacher model used: valhalla/distilbart-mnli-12-1
* Studend model used: distilbert-base-uncased ## Results

Agreement of student and teacher predictions: 94.82%

See this notebook for more info on feature engineering choice made ## How to train using your own dataset
* Download training dataset from URL
* Modify and run URL, updating the paths to convert to a CSV
* Run URL with the csv file (see here for more info) ## Acknowledgements

* Joe Davison and his article on Zero-Shot Learning in Modern NLP
* Jeremy Howard, URL and his notebook Iterate like a grandmaster
TAGS
#transformers #pytorch #distilbert #text-classification #customer-service-tickets #github-issues #bart-large-mnli #zero-shot-classification #NLP #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # GitHub issues classifier (using zero shot classification)

Predicts wether a statement is a feature request, issue/bug or question 

This model was trained using the Zero-shot classifier distillation method
with the BART-large-mnli model as teacher model, to train a classifier on Github issues from the Github Issues Prediction dataset ## 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 for details), labels being

* issue
* feature request
* question ## Training data

* 15k of Github issues titles ("unlabeled_titles_simple.txt")
* Hypothesis used: "This request is a {}"
* Teacher model used: valhalla/distilbart-mnli-12-1
* Studend model used: distilbert-base-uncased ## Results

Agreement of student and teacher predictions: 94.82%

See this notebook for more info on feature engineering choice made ## How to train using your own dataset
* Download training dataset from URL
* Modify and run URL, updating the paths to convert to a CSV
* Run URL with the csv file (see here for more info) ## Acknowledgements

* Joe Davison and his article on Zero-Shot Learning in Modern NLP
* Jeremy Howard, URL and his notebook Iterate like a grandmaster
TAGS
#transformers #pytorch #distilbert #text-classification #customer-service-tickets #github-issues #bart-large-mnli #zero-shot-classification #NLP #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# GitHub issues classifier (using zero shot classification)

Predicts wether a statement is a feature request, issue/bug or question 

This model was trained using the Zero-shot classifier distillation method
with the BART-large-mnli model as teacher model, to train a classifier on Github issues from the Github Issues Prediction dataset## 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 for details), labels being

* issue
* feature request
* question## Training data

* 15k of Github issues titles ("unlabeled_titles_simple.txt")
* Hypothesis used: "This request is a {}"
* Teacher model used: valhalla/distilbart-mnli-12-1
* Studend model used: distilbert-base-uncased## Results

Agreement of student and teacher predictions: 94.82%

See this notebook for more info on feature engineering choice made## How to train using your own dataset
* Download training dataset from URL
* Modify and run URL, updating the paths to convert to a CSV
* Run URL with the csv file (see here for more info)## Acknowledgements

* Joe Davison and his article on Zero-Shot Learning in Modern NLP
* Jeremy Howard, URL and his notebook Iterate like a grandmaster