Popular GitHub Repositories for Scoring Sleep Stages

"Effective Whole-body Pose Estimation with Two-stages Distillation" (ICCV 2023, CV4Metaverse Workshop)

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

A developer CLI that reduces the time it takes to get started on Azure. The Azure Developer CLI (azd) provides commands that map to key workflow stages: code, build, deploy, monitor, repeat.

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

Python library & examples for Masked Language Model Scoring (ACL 2020)

This is "SHIELD" a System for Harmful explicit-content Identification and Evaluation through LLM-Driven approach. Its primary object is to score explicit content for 15 different categories of explicitness from 0 to 100, it leverages LLMs as the primary scoring tool.

?Language Model based sentences scoring library

TapeAgents is a framework that facilitates all stages of the LLM Agent development lifecycle

U-Time: A Fully Convolutional Network for Time Series Segmentation

MLOps Platform

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

News reader with tags, scoring, and AI

This repository contains an easy and intuitive approach to few-shot NER using most similar expansion over spaCy embeddings. Now with entity scoring.

Word Embeddings for Information Retrieval

TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG by Akara Supratak and Yike Guo from The Faculty of ICT, Mahidol University and Imperial College London respectively

Design pattern for critical stages in the development process of an AI Stock Trading Bot

[BEA @ ACL 2023] General-purpose tool for linguistic features extraction; Tested on readability assessment, essay scoring, fake news detection, hate speech detection, etc.

End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages.

Amazon SageMaker Solution for explaining credit decisions.