
# Dataset Card for Dataset Name
Please note that this dataset maynot be perfect and may contain a very small quantity of non python codes. But the quantity appears to be very small

### Dataset Summary

The dataset contains a collection of python question and their code. This is meant to be used for training models to be efficient in Python specific coding.
The dataset has two features - 'question' and 'code'. 

An example is:
```
{'question': 'Create a function that takes in a string and counts the number of vowels in it',
 'code': 'def count_vowels(string):\n    vowels = ["a", "e", "i", "o", "u"]\n    count = 0\n    for char in string:\n        if char in vowels:\n            count += 1\n    return count'}
```

### Languages

English, Python


### Source Data

The dataset is derived from two other coding based datasets:
1) sahil2801/CodeAlpaca-20k
2) neulab/conala

@inproceedings{yin2018learning,
  title={Learning to mine aligned code and natural language pairs from stack overflow},
  author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
  booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
  pages={476--486},
  year={2018},
  organization={IEEE}
}

### Licensing Information

This uses MIT licence

### Citation Information

Will be added soon

task_categories:text-generation task_categories:text2text-generation size_categories:10K<n<100K language:en license:mit code region:us
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "text2text-generation"], "pretty_name": "Python codes dataset", "tags": ["code"]}
TAGS
#task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-mit #code #region-us 


# Dataset Card for Dataset Name
Please note that this dataset maynot be perfect and may contain a very small quantity of non python codes. But the quantity appears to be very small

### Dataset Summary

The dataset contains a collection of python question and their code. This is meant to be used for training models to be efficient in Python specific coding.
The dataset has two features - 'question' and 'code'. 

An example is:


### Languages

English, Python


### Source Data

The dataset is derived from two other coding based datasets:
1) sahil2801/CodeAlpaca-20k
2) neulab/conala

@inproceedings{yin2018learning,
  title={Learning to mine aligned code and natural language pairs from stack overflow},
  author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
  booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
  pages={476--486},
  year={2018},
  organization={IEEE}
}

### Licensing Information

This uses MIT licence



Will be added soon

# Dataset Card for Dataset Name
Please note that this dataset maynot be perfect and may contain a very small quantity of non python codes. But the quantity appears to be very small ### Dataset Summary

The dataset contains a collection of python question and their code. This is meant to be used for training models to be efficient in Python specific coding.
The dataset has two features - 'question' and 'code'. 

An example is: ### Languages

English, Python ### Source Data

The dataset is derived from two other coding based datasets:
1) sahil2801/CodeAlpaca-20k
2) neulab/conala

@inproceedings{yin2018learning,
  title={Learning to mine aligned code and natural language pairs from stack overflow},
  author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
  booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
  pages={476--486},
  year={2018},
  organization={IEEE}
} ### Licensing Information

This uses MIT licence



Will be added soon
TAGS
#task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-mit #code #region-us 
 # Dataset Card for Dataset Name
Please note that this dataset maynot be perfect and may contain a very small quantity of non python codes. But the quantity appears to be very small ### Dataset Summary

The dataset contains a collection of python question and their code. This is meant to be used for training models to be efficient in Python specific coding.
The dataset has two features - 'question' and 'code'. 

An example is: ### Languages

English, Python ### Source Data

The dataset is derived from two other coding based datasets:
1) sahil2801/CodeAlpaca-20k
2) neulab/conala

@inproceedings{yin2018learning,
  title={Learning to mine aligned code and natural language pairs from stack overflow},
  author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
  booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
  pages={476--486},
  year={2018},
  organization={IEEE}
} ### Licensing Information

This uses MIT licence



Will be added soon
passage: TAGS
#task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-mit #code #region-us 
# Dataset Card for Dataset Name
Please note that this dataset maynot be perfect and may contain a very small quantity of non python codes. But the quantity appears to be very small### Dataset Summary

The dataset contains a collection of python question and their code. This is meant to be used for training models to be efficient in Python specific coding.
The dataset has two features - 'question' and 'code'. 

An example is:### Languages

English, Python### Source Data

The dataset is derived from two other coding based datasets:
1) sahil2801/CodeAlpaca-20k
2) neulab/conala

@inproceedings{yin2018learning,
  title={Learning to mine aligned code and natural language pairs from stack overflow},
  author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
  booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
  pages={476--486},
  year={2018},
  organization={IEEE}
}### Licensing Information

This uses MIT licence



Will be added soon