https://github.com/1rsh/qa-genie
This is a package for generating questions and answers from unstructured data to be used for NLP tasks.
https://github.com/1rsh/qa-genie
llm-finetuning question-answering question-generation question-generator
Last synced: 5 months ago
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This is a package for generating questions and answers from unstructured data to be used for NLP tasks.
- Host: GitHub
- URL: https://github.com/1rsh/qa-genie
- Owner: 1rsh
- License: unlicense
- Created: 2024-01-26T15:07:05.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-01T16:37:31.000Z (over 2 years ago)
- Last Synced: 2025-09-25T06:26:21.896Z (9 months ago)
- Topics: llm-finetuning, question-answering, question-generation, question-generator
- Language: Python
- Homepage: https://pypi.org/project/qa-genie/
- Size: 68.4 KB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# QA Genie
English | [हिंदी](README_hi.md)
QA Genie is a Python package designed for generating questions and answers from unstructured data.
This package is built using the unofficial API of HuggingChat: [hugchat](https://pypi.org/project/hugchat/). It leverages HuggingChat's capabilities for question and answer generation.
[](https://pypi.python.org/pypi/qa_genie)
[](https://pypi.python.org/pypi/qa_genie)
[](https://pypi.python.org/pypi/qa_genie)
[](https://www.pepy.tech/projects/qa_genie)
> **Note**
>
> This package is in its alpha release and more functionality will be added soon!
> **Update 1.0.0a3:** This update enables the user to adjust iteration time. (Solves #1)
> **Update 1.0.0a4:** Enables user to get raw text output from chatbot for manual cleaning. Also fixes bugs in cleaning.
## Installation
```bash
pip install qa_genie
```
or
```bash
pip3 install qa_genie
```
## Usage
```python
email = "your_email@example.com" # huggingface account email
password = "your_password" # huggingface account password
model = "meta" # use "meta" to use meta-llama/Llama-2-70b-chat-hf or "oasst" to use OpenAssistant/oasst-sft-6-llama-30b
# Initialize chatbot
chatbot = get_generator(email, password, model)
# Example usage with a single text
text = "Lorem ipsum dolor sit amet, consectetur adipiscing elit."
result_single = extract_qa(chatbot, text, num_qn=3) # returns pandas.DataFrame with num_qn questions and answers
# Example usage with multiple texts
texts = ["Text 1", "Text 2", "Text 3"]
result_multiple = extract_qas(chatbot, texts, num_qn_each=3) # return pandas.DataFrame with num_qn_each questions and answers generated for each text
```
## Important Note
As mentioned by [Soulter](github.com/Soulter), Server resources are precious, it is not recommended to request this API in a high frequency.
## Contributing
Feel free to contribute to QA Genie by creating issues, submitting pull requests, or suggesting improvements. Your contributions are highly appreciated :)