Machine Comprehension using Squad and Triviqa Data sets
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Updated
Dec 11, 2017 - Jupyter Notebook
Machine Comprehension using Squad and Triviqa Data sets
CS224N, Stanford, Winter 2018
Multiple Sentences Bi-directional Attention Flow (Multi-BiDAF) network is a model designed to fit the BiDAF model of Seo et al. (2017) for the Multi-RC dataset. This implementation is built on the AllenNLP library.
Usage example for the AllenNLP BiDAF pre-trained model
BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION
Using QANet and BiDAF on DuReader datasets
Machine Reading Comprehension in Tensorflow
Bi-Directional Attention Flow (BiDAF) question answering model enhanced by multi-layer convolutional neural network character embeddings.
Implementing the Bidirectional Attention Flow model using pytorch
Question Answering System using BiDAF Model on SQuAD v2.0
Implementation of the machine comprehension model in our ACL 2019 paper: Augmenting Neural Networks with First-order Logic.
Important paper implementations for Question Answering using PyTorch
Question answering on the SQuAD dataset, for NLP class at UNIBO
Implementation of the Bi-Directional Attention Flow Model (BiDAF) in Python using Keras
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