Code for "Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies"

What's In My Big Data (WIMBD) - a toolkit for analyzing large text datasets

[ACL2023] We introduce LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source LLMs. LLM-Blender cut …

An Open-Source Framework for Prompt-Learning.

A modular RL library to fine-tune language models to human preferences

Official implementation for "Distance Learner: Incorporating Manifold Prior to Model Training"

Robustness Gym is an evaluation toolkit for machine learning.

Testing Diverse Reasoning of NLI Systems

Pytorch lightning + hydra + neptune template for LM finetuning

PECOS - Prediction for Enormous and Correlated Spaces

Google Drive CLI Client

Concept Bottleneck Models, ICML 2020

Notification Triggers for Python

Training neural models with structured signals.

Repo for the work on hierarchical state space models for disentanglement

LEAF is a learnable alternative to audio features such as mel-filterbanks, that can be initialized as an approximation of mel-filterbanks, and then be trained for the task at hand, while using a ve…

Unsupervised text tokenizer for Neural Network-based text generation.

Label Studio is a multi-type data labeling and annotation tool with standardized output format

Graph Neural Network Library for PyTorch

GraphGallery is a gallery for benchmarking Graph Neural Networks, From InplusLab.

A Shared Task on Contextual Emotion Detection in Text (done in exactly two days; one before mid-eval and one before end-eval)

Code for Graphite iterative graph generation

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…

Source Code for paper "NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction", WWW 2020

This repo contains code for the paper: "B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning".

Supplementary material of "Deep Unsupervised Drum Transcription", ISMIR 2019

Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more