attempt at implementing "Memory Architectures in Recurrent Neural Network Language Models" as a part of the ICLR 2018 reproducibility challenge
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Updated
Dec 23, 2017 - Python
attempt at implementing "Memory Architectures in Recurrent Neural Network Language Models" as a part of the ICLR 2018 reproducibility challenge
Neat (Neural Attention) Vision, is a visualization tool for the attention mechanisms of deep-learning models for Natural Language Processing (NLP) tasks. (framework-agnostic)
Implementation of the Paper Structured Self-Attentive Sentence Embedding published in ICLR 2017
Structured Self Attention implementation in tensorflow
Jatext Classification
A Structured Self-attentive Sentence Embedding
Python implementation of N-gram Models, Log linear and Neural Linear Models, Back-propagation and Self-Attention, HMM, PCFG, CRF, EM, VAE
Tensorflow implementation of "A Structured Self-Attentive Sentence Embedding"
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Re-Implementation of "A Structured Self-Attentive Sentence Embedding" by Lin et al., 2017
Tensorflow-based framework which lists attentive implementation of the conventional neural network models (CNN, RNN-based), applicable for Relation Extraction classification tasks as well as API for custom model implementation
This sentiment analysis model utilizes a Transformer architecture to classify text sentiment into positive, negative, or neutral categories with high accuracy. It preprocesses text data, trains the model on the IMDB dataset, and effectively predicts sentiment based on user input.
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