R-NET-in-Keras and R-NET-in-Tensorflow

These are parallel implementations of the same R-NET architecture in different deep learning frameworks (Keras vs. TensorFlow), making them competitors that serve the same purpose but allow practitioners to choose based on their preferred framework.

R-NET-in-Keras
49
Emerging
R-NET-in-Tensorflow
38
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 23/25
Maintenance 0/25
Adoption 9/25
Maturity 8/25
Community 21/25
Stars: 177
Forks: 88
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stars: 77
Forks: 41
Downloads: —
Commits (30d): 0
Language: Python
License: —
Stale 6m No Package No Dependents
No License Stale 6m No Package No Dependents

About R-NET-in-Keras

YerevaNN/R-NET-in-Keras

Open R-NET (hy` առնետ 🐁) implementation and detailed analysis: https://git.io/vd8dx

This project offers an implementation of the R-NET neural network for question answering tasks. It takes a question and a relevant text passage as input, and outputs the most likely answer span from that passage. This is primarily a tool for machine learning researchers and developers interested in replicating and analyzing advanced question answering models.

question-answering natural-language-processing neural-networks deep-learning-research

About R-NET-in-Tensorflow

unilight/R-NET-in-Tensorflow

R-NET implementation in TensorFlow.

This tool helps researchers and natural language processing practitioners answer questions based on a given text passage. You input a long text passage and a question, and it identifies the specific segment of the passage that answers the question. It's designed for those working with large question-answering datasets.

question-answering natural-language-understanding text-comprehension information-extraction

Related comparisons

R-NET-in-Keras and R-Net R-NET-in-Keras and R-Net

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