Datasets:
license: mit
task_categories:
- question-answering
- conversational
- text-generation
language:
- en
tags:
- Python
- ChatBot
size_categories:
- 100K<n<1M
Dataset description:
The Python Code Chatbot dataset is a collection of Python code snippets extracted from various publicly available datasets and platforms. It is designed to facilitate training conversational AI models that can understand and generate Python code. The dataset consists of a total of 1,37,183 prompts, each representing a dialogue between a human and an AI Scientist.
Prompt Card:
Each prompt in the dataset follows a specific format known as the "Prompt Card." The format is as follows:
[SYSTEM]
Below is a dialogue between a human and an AI Scientist
[END]
[USER]
# User Input
[END]
[ASSISTANT]
Bot Output
[END]
The length of the prompts varies within a range of 201 to 2590 tokens. The dataset offers a diverse set of conversational scenarios related to Python programming, covering topics such as code execution, debugging, best practices, code optimization, and more.
How to use:
To access the Python Code Chatbot dataset seamlessly, you can leverage the powerful "datasets" library. The following code snippet illustrates how to load the dataset:
from datasets import load_dataset
dataset = load_dataset("anujsahani01/TextCodeDepot")
Potential Use Cases:
This dataset can be utilized for various natural language processing tasks such as question-answering, conversational AI, and text generation. Some potential use cases for this dataset include:
- Training chatbots or virtual assistants that can understand and respond to Python-related queries from users.
- Developing AI models capable of generating Python code snippets based on user input or conversational context.
- Improving code completion systems by incorporating conversational context and user intent.
- Assisting programmers in debugging their Python code by providing relevant suggestions or explanations.
Feedback:
If you have any feedback, please reach out to me at: LinkedIn | GitHub
Your feedback is valuable in improving the quality and usefulness of this dataset.
Author: @anujsahani01