google-research-datasets Goto Github PK
Name: Google Research Datasets
Type: Organization
Bio: Datasets released by Google Research
Location: Mountain View, CA
Blog: research.google
Name: Google Research Datasets
Type: Organization
Bio: Datasets released by Google Research
Location: Mountain View, CA
Blog: research.google
This dataset contains about 110k images annotated with the depth and occlusion relationships between arbitrary objects. It enables research on the 2.5D Visual Relationship Detection (2.5VRD) introduced in https://arxiv.org/abs/2104.12727.
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications
This dataset contains results from all rounds of Adversarial Nibbler. This data includes adversarial prompts fed into public generative text2image models and validations for unsafe images. There will be two sets of data: all prompts submitted and all prompts attempted (sent to t2i models but not submitted as unsafe).
Article-summary entailment annotations for agreement-oriented multidoc summarization
AIS is an evaluation framework for assessing whether the output of natural language models only contains information about the external world that is verifiable in source documents, or "Attributable to Identified Sources".
AltEntities (Alternative-Entities) is a dataset of English-language alternative questions with their answers. In these questions, a user is presented with a choice of two entities. The user answer intends to select one entity from the pair by referring to it in an indirect way, i.e., without referring to its name or position.
This dataset contains human judgements about answer equivalence. The data is based on SQuAD (Stanford Question Answering Dataset), and contains 9k human judgements of answer candidates generated by Albert on the SQuAD train set, and an additional 14k human judgements for answer candidates produced by BiDAF, Luke, and XLNet on the SQuAD dev set.
AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)
We believe the ability of an LLM to attribute the text that it generates is likely to be crucial for both system developers and users in information-seeking scenarios. This release consists of human-rated system outputs for a new question-answering task, Attributed Question Answering (AQA).
The BamTwoogle dataset accompanies "ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent" paper (https://arxiv.org/abs/2312.10003). It was written to be a complementary, slightly more challenging sequel to Bamboogle dataset. It addresses some of the shortcomings of Bamboogle we discovered while performing human evals for the paper.
This dataset contains synthetic training data for grammatical error correction. The corpus is generated by corrupting clean sentences from C4 using a tagged corruption model. The approach and the dataset are described in more detail by Stahlberg and Kumar (2021) (https://www.aclweb.org/anthology/2021.bea-1.4/)
C4RepSet: Representative Subset from C4 data for Training Pre-trained LMs
This dataset is a result of the CATS4ML (Crowdsourcing Adverse Test Sets for Machine Learning) Data Challenge - an adversarial test-set sampling images and labels from the Open Images Dataset for state-of-the-art image classification models. The challenge invited participants to sample this existing publicly available dataset for images that are incorrectly classified by image classification models. It was announced at the HCOMP 2020 conference and ran for three months (Jan-Apr 2021) aiming at submissions by researchers and developers worldwide. This challenge is a first proof-of-concept for the approach using an existing AI dataset, and shows immediate positive impact on improving evaluation datasets in AI research. In the following subsections we describe the main components of the challenge pipeline and data used.
A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers, where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits the 'user’s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The assistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her preferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of entities.
A rendered creature image dataset with part segmentation inputs for machine learning and neural rendering.
Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions. The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversation situations (eg. food preferences of a friend).
cLang-8 is a dataset for grammatical error correction.
The dataset includes UI object type labels (e.g., BUTTON, IMAGE, CHECKBOX) that describes the semantic type of an UI object on Android app screenshots. It is used for training and evaluation of the screen layout denoising models (paper will be linked soon).
The Corpus of Linguistically Significant Entities (CLSE) is a dataset of named entities annotated by linguist experts. It includes 34 languages and covers 74 different semantic types to support various applications from airline ticketing to video games. The aim of the corpus is to facilitate the creation of more linguistically diverse NLG datasets.
A large corpus of discourse annotations and relations on ~10K forum threads.
Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. "commoncrawl" to "common crawl").
The comparative question completion is a dataset consisting of real user comparative questions gathered from question-answering websites. Each question compares between two entities, in one of three domains (animals, NBA players, and locations). In each example one of the two entities is masked, and the goal is to find the masked entity in each example.
Conceptual 12M is a dataset containing (image-URL, caption) pairs collected for vision-and-language pre-training.
Conceptual Captions is a dataset containing (image-URL, caption) pairs designed for the training and evaluation of machine learned image captioning systems.
The Context Tracking dataset consists of annotated human-human social conversations. Each conversation contains annotations for the people and location entities mentioned, their properties and the relationships between them. The annotated data enables several subtasks like slot tagging, coreference resolution, resolving plural mentions and entity linking.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.