There are 6 repositories under sequence-to-sequence topic.
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP
CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition
Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch
an open-source implementation of sequence-to-sequence based speech processing engine
Open-Source Neural Machine Translation in Tensorflow
End-to-end ASR/LM implementation with PyTorch
A chatbot implemented in TensorFlow based on the seq2seq model, with certain rules integrated.
An open-source tool for sequence learning in NLP built on TensorFlow.
Sequence to sequence learning using TensorFlow.
Pytorch seq2seq chatbot
Conversation models in TensorFlow. (website removed)
Interpretability for sequence generation models 🐛 🔍
PyTorch implementation of Transformer model used in "Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case"
A web-based document annotation tool, powered by GPT-4 :rocket:
Seq2SeqSharp is a tensor based fast & flexible deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, different network types (Transformer, LSTM, BiLSTM and so on), multi-GPUs supported, cross-platforms (Windows, Linux, x86, x64, ARM), multimodal model for text and images and so on.
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Towards Neural Phrase-based Machine Translation
Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
This repository contains the code for a video captioning system inspired by Sequence to Sequence -- Video to Text. This system takes as input a video and generates a caption in English describing the video.
MWPToolkit is an open-source framework for math word problem(MWP) solvers.
Machine Learning and having it Deep and Structured (MLDS) in 2018 spring
Code For Medium Article "How To Create Data Products That Are Magical Using Sequence-to-Sequence Models"
A dataset for training/evaluating Question Answering Retrieval models on ChatGPT responses with the possibility to training/evaluating on real human responses.
A Keras-based library for analysis of time series data using deep learning algorithms.
The implementation of the paper "Augmenting Neural Response Generation with Context-Aware Topical Attention"
A Keras multi-input multi-output LSTM-based RNN for object trajectory forecasting
Predicting protein structure through sequence modeling