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

Python implementation of N-gram Models, Log linear and Neural Linear Models, Back-propagation and Self-Attention, HMM, PCFG, CRF, EM, VAE

Structured Self Attention implementation in tensorflow

attempt at implementing "Memory Architectures in Recurrent Neural Network Language Models" as a part of the ICLR 2018 reproducibility challenge

This repository provides a basic implementation of self-attention. The code demonstrates how attention mechanisms work in predicting the next word in a sequence. It's a basic implementation that demonstrates the core concept of attention but lacks the complexity of more advanced models like Transformers.

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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.

Jatext Classification