Popular GitHub Repositories for Credit Scoring

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

Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

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

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.

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

Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)

Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022

?Language Model based sentences scoring library

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

An automated scoring function to facilitate and standardize the evaluation of goal-directed generative models for de novo molecular design

Credit Risk analysis by using Python and ML

NGC-Learn: Neurobiological Learning and Biomimetic Systems Simulation in Python

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

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

Amazon SageMaker Solution for explaining credit decisions.