Building Question Answering System using Transformers, Pinecone and Keras
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Updated
Sep 13, 2023 - Jupyter Notebook
Building Question Answering System using Transformers, Pinecone and Keras
Simpletransformer library is based on the Transformers BERT library by HuggingFace. The goal of Question Answering is to find the answer to a question given a question and an accompanying context. The predicted answer will be either a span of text from the context or an empty string (indicating the question cannot be answered from the context.)
This is the avishkaarak-ekta-hindi model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
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HealthQA_API provides a RESTful API built with FastAPI and deployed on Vercel. The API uses the sentence-transformers package with the all-MiniLM-L6-v2 model for question and answer retrieval.
A simple quiz app with sound built using JavaScript.
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In this project, I created a simple Python using the HuggingFace's Transformers library which us an application that is answering "What is the capital of a country?".
Question Answering System
Question Answering with HuggingFace transformers Bert-tiny and DistilRoBERTa
Natural Language Processing (NLP) - History-related Question-Answering System
PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora
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