There are 1 repository under self-attentive-rnn topic.
A Structured Self-attentive Sentence Embedding
Neat (Neural Attention) Vision, is a visualization tool for the attention mechanisms of deep-learning models for Natural Language Processing (NLP) tasks. (framework-agnostic)
Tensorflow implementation of "A Structured Self-Attentive Sentence Embedding"
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
attempt at implementing "Memory Architectures in Recurrent Neural Network Language Models" as a part of the ICLR 2018 reproducibility challenge
Structured Self Attention implementation in tensorflow
Python implementation of N-gram Models, Log linear and Neural Linear Models, Back-propagation and Self-Attention, HMM, PCFG, CRF, EM, VAE
Aplikasi ini dibuat untuk membantu pengguna dalam menentukan apakah sebuah berita yang ingin dibaca termasuk clickbait atau bukan.
Jatext Classification
Implementation of the Paper Structured Self-Attentive Sentence Embedding published in ICLR 2017
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.