# 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! <br>
> **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 :)
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"description": "# QA Genie\n\nEnglish | [\u0939\u093f\u0902\u0926\u0940](README_hi.md) \n\nQA Genie is a Python package designed for generating questions and answers from unstructured data.\n\nThis 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.\n\n\n[](https://pypi.python.org/pypi/qa_genie)\n[](https://pypi.python.org/pypi/qa_genie)\n[](https://pypi.python.org/pypi/qa_genie)\n[](https://www.pepy.tech/projects/qa_genie)\n\n> **Note**\n>\n> This package is in its alpha release and more functionality will be added soon! <br>\n> **Update 1.0.0a3:** This update enables the user to adjust iteration time. (Solves #1) \n> **Update 1.0.0a4:** Enables user to get raw text output from chatbot for manual cleaning. Also fixes bugs in cleaning.\n\n\n## Installation\n```bash\npip install qa_genie\n```\nor\n```bash\npip3 install qa_genie\n```\n\n\n## Usage\n```python\nemail = \"your_email@example.com\" # huggingface account email\npassword = \"your_password\" # huggingface account password\nmodel = \"meta\" # use \"meta\" to use meta-llama/Llama-2-70b-chat-hf or \"oasst\" to use OpenAssistant/oasst-sft-6-llama-30b\n\n# Initialize chatbot\nchatbot = get_generator(email, password, model)\n\n# Example usage with a single text\ntext = \"Lorem ipsum dolor sit amet, consectetur adipiscing elit.\"\nresult_single = extract_qa(chatbot, text, num_qn=3) # returns pandas.DataFrame with num_qn questions and answers\n\n# Example usage with multiple texts\ntexts = [\"Text 1\", \"Text 2\", \"Text 3\"]\nresult_multiple = extract_qas(chatbot, texts, num_qn_each=3) # return pandas.DataFrame with num_qn_each questions and answers generated for each text\n```\n\n## Important Note\nAs mentioned by [Soulter](github.com/Soulter), Server resources are precious, it is not recommended to request this API in a high frequency.\n\n## Contributing\nFeel free to contribute to QA Genie by creating issues, submitting pull requests, or suggesting improvements. Your contributions are highly appreciated :)\n",
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