Omkar2118/datasets huggingface dataset:
CoNaLa
Full name: CMU CoNaLa, the Code/Natural Language Challenge

The **CMU CoNaLa, the Code/Natural Language Challenge** dataset is a joint project from the Carnegie Mellon University [NeuLab](http://www.cs.cmu.edu/~neulab/) and [Strudel](https://cmustrudel.github.io/) labs. Its purpose is for testing the generation of code snippets from natural language. The data comes from StackOverflow questions. There are 2379 training and 500 test examples that were manually annotated. Every example has a natural language *intent* and its corresponding python *snippet*.  In addition to the manually annotated dataset, there are also 598,237 mined intent-snippet pairs. These examples are similar to the hand-annotated ones except that they contain a probability if the pair is valid.

Source: [CoNaLa dataset Homepage](https://conala-corpus.github.io/)

Introducing paper: Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow
SIPaKMeD
Full name: SIPaKMeD Pap Smear dataset

* a high-level explanation of the dataset characteristics
* explain motivations and summary of its content
* potential use cases of the dataset
CoNaLa-Ext
Full name: CoNaLa Extended With Question Text

The **CoNaLa Extended With Question Text** is an extension to the original [CoNaLa Dataset](https://conala-corpus.github.io/) ([Papers With Code Link](https://paperswithcode.com/dataset/conala)) proposed in the NLP4Prog workshop paper "[Reading StackOverflow Encourages Cheating: Adding Question Text
Improves Extractive Code Generation](https://arxiv.org/abs/2106.04447)". The key additions are that every example now has the full question body from its respective StackOverflow Question.

**IMPORTANT** If you use this dataset, you MUST cite the [original CoNaLa dataset paper](https://arxiv.org/abs/1805.08949).

Source: [CoNaLa-Ext Homepage](https://github.com/gabeorlanski/stackoverflow-encourages-cheating)

Introducing paper: Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation