# Dataset Card for "reason_code-search-net-python"

## Dataset Description

- **Homepage:** None
- **Repository:** https://huggingface.co/datasets/Nan-Do/reason_code-search-net-python
- **Paper:** None
- **Leaderboard:** None
- **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do)

### Dataset Summary

This dataset is an instructional dataset for Python.  
The dataset contains five different kind of tasks.   

Given a Python 3 function:
 - Type 1: Generate a summary explaining what it does. (For example: This function counts the number of objects stored in the jsonl file passed as input.)
 - Type 2: Generate a summary explaining what its input parameters represent ("For example: infile: a file descriptor of a file containing json objects in jsonl format.")
 - Type 3: Generate a summary explaining what the return value represents ("For example: The function returns the number of json objects in the file passed as input.")
 - Type 4: Generate a summary explaining what is the type of the return value ("For example: The function returns an int.")
 - Type 5: Generate a summary explaining what is the type of its input parameters ("For example: infile: A file descriptor.").

### Languages

The dataset is in English.

### Data Splits

There are no splits (Only training).

## Dataset Creation

May of 2023

### Curation Rationale

This dataset was created to improve the Python 3 reasoning/understanding capabilities of LLMs.

### Source Data

The summarized version of the code-search-net dataset can be found at https://huggingface.co/datasets/Nan-Do/code-search-net-python

### Annotations

The dataset includes an instruction, response and type columns.  
The type colum indicates the type of task (from 1 to 5).

#### Annotation process

The annotation procedure was done using templates, NLP techniques to generate human-like questions and responses, and the Python AST module to parse the code.
The responses were generated parsing the docstrings of the functions. (The ones that included the required information).

### Licensing Information

Apache 2.0
task_categories:summarization task_categories:text-generation task_categories:conversational task_categories:text2text-generation language:en license:apache-2.0 code reasoning Python region:us
{"language": ["en"], "license": "apache-2.0", "task_categories": ["summarization", "text-generation", "conversational", "text2text-generation"], "pretty_name": "Reasoning dataset for Python", "dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "TYPE", "dtype": "int64"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 399930143, "num_examples": 429059}], "download_size": 89360217, "dataset_size": 399930143}, "tags": ["code", "reasoning", "Python"]}
TAGS
#task_categories-summarization #task_categories-text-generation #task_categories-conversational #task_categories-text2text-generation #language-English #license-apache-2.0 #code #reasoning #Python #region-us 

# Dataset Card for "reason_code-search-net-python"

## Dataset Description

- Homepage: None
- Repository: URL
- Paper: None
- Leaderboard: None
- Point of Contact: @Nan-Do

### Dataset Summary

This dataset is an instructional dataset for Python.  
The dataset contains five different kind of tasks.   

Given a Python 3 function:
 - Type 1: Generate a summary explaining what it does. (For example: This function counts the number of objects stored in the jsonl file passed as input.)
 - Type 2: Generate a summary explaining what its input parameters represent ("For example: infile: a file descriptor of a file containing json objects in jsonl format.")
 - Type 3: Generate a summary explaining what the return value represents ("For example: The function returns the number of json objects in the file passed as input.")
 - Type 4: Generate a summary explaining what is the type of the return value ("For example: The function returns an int.")
 - Type 5: Generate a summary explaining what is the type of its input parameters ("For example: infile: A file descriptor.").

### Languages

The dataset is in English.

### Data Splits

There are no splits (Only training).

## Dataset Creation

May of 2023

### Curation Rationale

This dataset was created to improve the Python 3 reasoning/understanding capabilities of LLMs.

### Source Data

The summarized version of the code-search-net dataset can be found at URL

### Annotations

The dataset includes an instruction, response and type columns.  
The type colum indicates the type of task (from 1 to 5).

#### Annotation process

The annotation procedure was done using templates, NLP techniques to generate human-like questions and responses, and the Python AST module to parse the code.
The responses were generated parsing the docstrings of the functions. (The ones that included the required information).

### Licensing Information

Apache 2.0
# Dataset Card for "reason_code-search-net-python" ## Dataset Description

- Homepage: None
- Repository: URL
- Paper: None
- Leaderboard: None
- Point of Contact: @Nan-Do ### Dataset Summary

This dataset is an instructional dataset for Python.  
The dataset contains five different kind of tasks.   

Given a Python 3 function:
 - Type 1: Generate a summary explaining what it does. (For example: This function counts the number of objects stored in the jsonl file passed as input.)
 - Type 2: Generate a summary explaining what its input parameters represent ("For example: infile: a file descriptor of a file containing json objects in jsonl format.")
 - Type 3: Generate a summary explaining what the return value represents ("For example: The function returns the number of json objects in the file passed as input.")
 - Type 4: Generate a summary explaining what is the type of the return value ("For example: The function returns an int.")
 - Type 5: Generate a summary explaining what is the type of its input parameters ("For example: infile: A file descriptor."). ### Languages

The dataset is in English. ### Data Splits

There are no splits (Only training). ## Dataset Creation

May of 2023 ### Curation Rationale

This dataset was created to improve the Python 3 reasoning/understanding capabilities of LLMs. ### Source Data

The summarized version of the code-search-net dataset can be found at URL ### Annotations

The dataset includes an instruction, response and type columns.  
The type colum indicates the type of task (from 1 to 5). #### Annotation process

The annotation procedure was done using templates, NLP techniques to generate human-like questions and responses, and the Python AST module to parse the code.
The responses were generated parsing the docstrings of the functions. (The ones that included the required information). ### Licensing Information

Apache 2.0
TAGS
#task_categories-summarization #task_categories-text-generation #task_categories-conversational #task_categories-text2text-generation #language-English #license-apache-2.0 #code #reasoning #Python #region-us 
 # Dataset Card for "reason_code-search-net-python" ## Dataset Description

- Homepage: None
- Repository: URL
- Paper: None
- Leaderboard: None
- Point of Contact: @Nan-Do ### Dataset Summary

This dataset is an instructional dataset for Python.  
The dataset contains five different kind of tasks.   

Given a Python 3 function:
 - Type 1: Generate a summary explaining what it does. (For example: This function counts the number of objects stored in the jsonl file passed as input.)
 - Type 2: Generate a summary explaining what its input parameters represent ("For example: infile: a file descriptor of a file containing json objects in jsonl format.")
 - Type 3: Generate a summary explaining what the return value represents ("For example: The function returns the number of json objects in the file passed as input.")
 - Type 4: Generate a summary explaining what is the type of the return value ("For example: The function returns an int.")
 - Type 5: Generate a summary explaining what is the type of its input parameters ("For example: infile: A file descriptor."). ### Languages

The dataset is in English. ### Data Splits

There are no splits (Only training). ## Dataset Creation

May of 2023 ### Curation Rationale

This dataset was created to improve the Python 3 reasoning/understanding capabilities of LLMs. ### Source Data

The summarized version of the code-search-net dataset can be found at URL ### Annotations

The dataset includes an instruction, response and type columns.  
The type colum indicates the type of task (from 1 to 5). #### Annotation process

The annotation procedure was done using templates, NLP techniques to generate human-like questions and responses, and the Python AST module to parse the code.
The responses were generated parsing the docstrings of the functions. (The ones that included the required information). ### Licensing Information

Apache 2.0