MurtyShikhar/Question-Answering
TensorFlow implementation of Match-LSTM and Answer pointer for the popular SQuAD dataset.
This project helps researchers and students studying natural language processing to train a question-answering model. You feed it a collection of text documents (like articles or passages) and corresponding questions, and it produces a model capable of finding answers within new text. This is designed for those exploring deep learning architectures for text comprehension.
135 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or student who wants to experiment with or understand the Match-LSTM and Answer Pointer neural network architecture for question answering.
Not ideal if you need a ready-to-use, high-performance question-answering system for practical application, as this is a research implementation focused on reproducing an academic paper.
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135
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67
Language
Python
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Last pushed
Feb 15, 2018
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