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self-attentive-rnn

Here are 13 public repositories matching this topic...

Neat (Neural Attention) Vision, is a visualization tool for the attention mechanisms of deep-learning models for Natural Language Processing (NLP) tasks. (framework-agnostic)

  • Updated May 4, 2018
  • Vue
AREnets

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

  • Updated Nov 8, 2023
  • Python

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.

  • Updated Sep 23, 2024
  • Python

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.

  • Updated Apr 5, 2024
  • Jupyter Notebook

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