Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
This repository contains the baseline code for the paper published in NAACL 2022: “WALNUT: A Benchmark on Weakly Supervised Learning for Natural Language Understanding”. Detailed description about the data sets and methods can be manuscript…
This repository contains pretraining pipeline of sequence-to-sequence language models.
The GitHub repository includes instructions to build and deploy a video analytics service tailored for operator’s infrastructure using Kubernetes in the Azure public MEC and Azure IoT Edge, and video ML containers like NVIDIA Triton…
This repo contains required files for the INTERSPEECH 2022 Audio Deep Packet Loss Concealment (PLC) Challenge.
We have created AECMOS for evaluating clips with regards to echo ratings and other degradations ratings. There are two ways for you to use AECMOS: a web API and an onnx version of the AECMOS…
Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Such over-reliance on spurious correlations also causes systems to struggle…
We introduce our full experimental data as Hybrid Hiring, a large-scale dataset for studying human AI decision-making that is collected and evaluated on real-world candidates. Comprised of 38,400 human judgements and over 9,600 unique prediction…
Source code and data for the CVPR 2022 paper “Learning to Detect Scene Landmarks for Camera Localization”.
Microsoft is working to make data that is relevant to important social problems as open as possible, including by contributing open data ourselves. The Data for Society resource center provides access to Microsoft’s open datasets,…
