https://github.com/jersongb22/questionanswering-tensorflow

https://github.com/jersongb22/questionanswering-tensorflow

hugging-face plotly python question-answering roberta-large tensorflow

Last synced: 2 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/jersongb22/questionanswering-tensorflow
• Owner: JersonGB22
• Created: 2024-06-23T03:56:36.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-06-26T17:51:57.000Z (11 months ago)
• Last Synced: 2025-01-25T11:25:46.294Z (4 months ago)
• Topics: hugging-face, plotly, python, question-answering, roberta-large, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 2.06 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

Awesome Lists containing this project

README

        
# 
**Question Answering**




 



This repository implements Question Answering models, a natural language processing (NLP) task that retrieves the answer to a question from a given text. These models are created using the TensorFlow and Hugging Face Transformers libraries. There are two types of this task:

1. **Extractive:** Extracts the answer directly from the provided context.

2. **Abstractive:** Generates a correct answer freely based on the context or without it.

## **Common Use Cases:**

- **Virtual Assistants:** Answering user questions using information from a specific database.

- **Document Search:** Finding precise answers in large document sets.

- **Customer Service:** Providing quick and accurate responses to customer inquiries.

- **Education:** Helping students get answers to questions based on study materials.

## **Implemented Models:**

- **Extractive Model for Answerable Questions:** This RoBERTa Large model has been fine-tuned with the [Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/rajpurkar/squad) to appropriately answer questions within a given context. SQuAD consists of 100,000 question-answer pairs across more than 500 Wikipedia articles, where the answer is a text segment from the corresponding passage. The model has achieved an excellent **F1 Score** of **94.1%** on the validation set.

- **Extractive Model for Answerable and Unanswerable Questions:** This model is a more robust and challenging version of the previous one, fine-tuned with [SQuAD 2.0](https://huggingface.co/datasets/rajpurkar/squad_v2) to appropriately answer both answerable and unanswerable questions within the provided context. SQuAD 2.0 consists of the 100,000 question-answer pairs from SQuAD plus 50,000 unanswerable questions. The model is capable of responding to questions within the context and also returns ``No Answer`` if the answer is not in the context or if it is of an abstractive type. It has achieved an excellent **F1 Score** of **87.7%** on the validation set.

## **Some Results**



 



---



 



---



 



---



 



#### *Further results can be found in their respective notebooks.*

## **Technological Stack**

[![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white&labelColor=101010)](https://docs.python.org/3/) 

[![TensorFlow](https://img.shields.io/badge/TensorFlow-FF6F00?style=for-the-badge&logo=tensorflow&logoColor=white&labelColor=101010)](https://www.tensorflow.org/api_docs)

[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-FFD21E?style=for-the-badge&logo=huggingface&logoColor=white&labelColor=101010)](https://huggingface.co/)

[![Plotly](https://img.shields.io/badge/Plotly-3F4F75?style=for-the-badge&logo=plotly&logoColor=white&labelColor=101010)](https://plotly.com/)

## **Contact**

[![Gmail](https://img.shields.io/badge/Gmail-D14836?style=for-the-badge&logo=gmail&logoColor=white&labelColor=101010)](mailto:[email protected])

[![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white&labelColor=101010)](https://www.linkedin.com/in/jerson-gimenes-beltran/)

[![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white&labelColor=101010)](https://github.com/JersonGB22/)