A TensorFlow implementation of Recurrent Neural Networks for Sequence Classification and Sequence Labeling

A Simple but Powerful SOTA NER Model | Official Code For Label Supervised LLaMA Finetuning

Deep neural network based model for sequence to sequence classification

Transformer-based models implemented in tensorflow 2.x(using keras).

Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.

Kun-peng: an ultra-fast, low-memory footprint and accurate taxonomy classifier for all

台北QA問答機器人(使用BERT、ALBERT)

NLP model that predicts subreddit based on the title of a post

High Order Hidden Markov Model for accurate sequence classification.

Scikit-learn compatible sequence classifier

Bioinformatics 2020: FastSK: Fast and Accurate Sequence Classification by making gkm-svm faster and scalable. https://fastsk.readthedocs.io/en/master/

Unsupervised Sequence Embedding via Sequential Patterns

CLANS_2 is a Python-based program for clustering sequences in the 2D or 3D space, based on their sequence similarities. CLANS visualizes the dynamic clustering process and enables the user to interactively control it and explore the cluster map in various ways.

Evaluation of different machine learning models on the task of online handwritten character recognition

One of the main NLU tasks is to understand the intents (sequence classification) and slots (entities within the sequence). This repo help classify both together using Joint Model (multitask model). BERT_SMALL is used which can be changed to any other BERT variant.

Text Classification and NLP in Tensorflow

Machine learning using RNN LSTM with Keras on UTD-MHAD Kinect dataset.

Applied Deep Learning 深度學習之應用 by Vivian Chen 陳縕儂 at NTU CSIE

bullet: A Zero-Shot / Few-Shot Learning, LLM Based, text classification framework

Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type) using a support vector-machine model.