Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks

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dev tools for aiohttp

Data Generation Pipeline for Mobility

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

A code-completion engine for Vim

Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Friedman

Machine Learning for Healthcare 2018 - Reproducible Survival Prediction with SEER Cancer Data

Machine Learning Tool Kit

Sepsis Prediction on MIMIC

CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering

Fit interpretable models. Explain blackbox machine learning.

A web-based user interface to visualize and inspect Explainable Boosting Machines.

Platform enabling Rapid Annotation for Clinical Entity Recognition

Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization)

Codebase for VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain - NeurIPS 2020

An annotated implementation of the Transformer paper.

Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

MIMIC Code Repository: Code shared by the research community for the MIMIC family of databases

Development and Validation of an Interpretable 3-day Intensive Care Unit Readmission Prediction Model Using Explainable Boosting Machines

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

MTEB: Massive Text Embedding Benchmark

Code for "A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models"

Code for 'LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders'

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