https://github.com/kaludii/eurekaqa
EurekaQA is a Question & Answering Model that has been trained by Kaludi to analyze text data and automatically answer questions based on the information contained within.
https://github.com/kaludii/eurekaqa
ai answer answering document gradio model qna question questioning trained
Last synced: about 2 months ago
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EurekaQA is a Question & Answering Model that has been trained by Kaludi to analyze text data and automatically answer questions based on the information contained within.
- Host: GitHub
- URL: https://github.com/kaludii/eurekaqa
- Owner: Kaludii
- Created: 2023-02-13T20:36:05.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-02-13T21:53:55.000Z (about 2 years ago)
- Last Synced: 2025-01-19T08:17:03.273Z (3 months ago)
- Topics: ai, answer, answering, document, gradio, model, qna, question, questioning, trained
- Language: Python
- Homepage: https://huggingface.co/spaces/Kaludi/EurekaQA
- Size: 116 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## EurekaQA - Question & Answering Model
EurekaQA is a Question & Answering Model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to analyze text data and automatically answer questions based on the information contained within. The model uses advanced machine learning algorithms to perform extractive question-answering, meaning it selects the relevant information from a given text document to present as the answer to the question.
# Web App
Click [Here](https://huggingface.co/spaces/Kaludi/EurekaQA) To View This App Online!
The model is trained on the [EurekaQA dataset](https://huggingface.co/datasets/Kaludi/data-eurekaQA) and the model can be found on [Hugging Face](https://huggingface.co/Kaludi/eurekaQA-model).
EurekaQA can be used in a variety of applications, including customer service, virtual assistants, and information retrieval systems.
## How to use EurekaQA
You can use EurekaQA by importing the `pipeline` method from the `transformers` library and setting the task to "question-answering" and the model to "Kaludi/eurekaQA-model". You can then pass in a context and a question as a list to the `pipeline` method to receive the answer.
`from transformers import pipeline
context = "The Greater Mexico City has a gross domestic product (GDP) of US$411 billion in 2011, making Mexico City urban agglomeration one of the economically largest metropolitan areas in the world. The city was responsible for generating 15.8% of Mexico's Gross Domestic Product and the metropolitan area accounted for about 22% of total national GDP. As a stand-alone country, in 2013, Mexico City would be the fifth-largest economy in Latin America—five times as large as Costa Rica's and about the same size as Peru's."
question = "What percent of the Mexican GDP is the metropolitan area of Mexico City responsible for?"
question_answer = pipeline("question-answering", model = "Kaludi/eurekaQA-model")
answer = question_answer([context, question], top_k=1)
print(answer)`
## Download and Use EurekaQA on App
1. Clone or download the repository.
2. Install the required libraries by running `pip install -r requirements.txt`.
3. Run the script using `python app.py`.
4. Input a customer review in the textbox and click on "Run".
5. The output will show the sentiment prediction of the review as either Positive or Negative along with the respective confidence score.
## Libraries Used
- Gradio
- Transformers
## Contributor
- [Kaludi](https://github.com/Kaludii)