Building machines that can reason about physical events and their causal relationships is crucial for flexible interaction with the physical world. However, most existing physical and causal reasoning benchmarks are exclusively based on synthetically generated events and synthetic natural language descriptions of causal relationships. This design brings up two issues. First, there is a lack of diversity in both event types and natural language descriptions; second, causal relationships based on manually-defined heuristics are different from human judgments. To address both shortcomings, we present the CLEVRER-Humans benchmark, a video reasoning dataset for causal judgment of physical events with human labels. We employ two techniques to improve data collection efficiency: first, a novel iterative event cloze task to elicit a new representation of events in videos, which we term Causal Event Graphs (CEGs); second, a data augmentation technique based on neural language generative models. ..., We use a three-stage annotation pipeline. The first stage focuses on collecting human-written event descriptions using event cloze tasks, but only for a small number of videos. In the second stage, we augment the data for all videos using neural event description generators trained on the data collected from the first stage. In the third stage, we condense CEGs by collecting binary causal relation labels for all pairs of events from humans.,
The CLEVER dataset is a dataset for video reasoning, where videos are presented to the model, along with a set of related questions, and the model's outputs are the answers to these questions.
pengxiang/clevrer dataset hosted on Hugging Face and contributed by the HF Datasets community
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
This repository contains the data for the paper CLEVER: A Curated Benchmark for Formally Verified Code Generation. The benchmark can be found on GitHub: https://github.com/trishullab/clever
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Clever population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Clever across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.
Key observations
In 2023, the population of Clever was 3,015, a 0.36% decrease year-by-year from 2022. Previously, in 2022, Clever population was 3,026, an increase of 0.23% compared to a population of 3,019 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Clever increased by 1,931. In this period, the peak population was 3,026 in the year 2022. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Clever Population by Year. You can refer the same here
This dataset provides information about the number of properties, residents, and average property values for Apache cross streets in Clever, MO.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual black student percentage from 2012 to 2023 for Clever High School vs. Missouri and Clever R-V School District
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Here we create two datasets (from existing datasets: CLEVRER, VisualGenome) for the Object Counting instruction tuning task.
CLEVRER, a video dataset
CLEVRER has QA pairs for each 5000 training videos. {'video_filename': int, 'scene_index': str (same as filename), 'questions': list [{'question_type': , 'question_subtype': , 'question_text': , 'answer_text': , 'program'(question attributes):}]}
We select 'descriptive' type, 'count' subtype questions, they are object counting task… See the full description on the dataset page: https://huggingface.co/datasets/jdsannchao/ObjectCount.
This dataset provides information about the number of properties, residents, and average property values for Willoughby Road cross streets in Clever, MO.
This dataset provides information about the number of properties, residents, and average property values for Holder Road cross streets in Clever, MO.
This dataset provides information about the number of properties, residents, and average property values for Brownfeather Road cross streets in Clever, MO.
https://webtechsurvey.com/termshttps://webtechsurvey.com/terms
A complete list of live websites using the Clever Advertising technology, compiled through global website indexing conducted by WebTechSurvey.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Video sources
In the json files, src indicates the video sources which can be downloaded as follows.
video-vqa-webvid_qa: WebVid video-conversation-videochat2: VideoChat2 video-classification-ssv2: SSv2 video-reasoning-clevrer_qa: CLEVRER video-vqa-tgif_frame_qa: TGIF video-reasoning-next_qa: NExTQA video-conversation-videochat1: VideoChat video-vqa-tgif_transition_qa: TGIF video-reasoning-clevrer_mc: CLEVRER video-vqa-ego_qa: EgoQA video-classification-k710:… See the full description on the dataset page: https://huggingface.co/datasets/pritamqu/self-alignment.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Non-Hispanic population of Clever by race. It includes the distribution of the Non-Hispanic population of Clever across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Clever across relevant racial categories.
Key observations
Of the Non-Hispanic population in Clever, the largest racial group is White alone with a population of 2,778 (94.11% of the total Non-Hispanic population).
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Clever Population by Race & Ethnicity. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual two or more races student percentage from 2016 to 2023 for Clever R-V School District vs. Missouri
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Please move the five folders into the CLeVeR_code directory, then refer to the Readme.pdf file to verify the experimental results.
https://sem1.theseowheel.com/company/legal/terms-of-service/https://sem1.theseowheel.com/company/legal/terms-of-service/
clever.com is ranked #435 in US with 22.92M Traffic. Categories: Distance Learning, Education. Learn more about website traffic, market share, and more!
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Clever by race. It includes the population of Clever across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Clever across relevant racial categories.
Key observations
The percent distribution of Clever population by race (across all racial categories recognized by the U.S. Census Bureau): 95.49% are white, 0.35% are Black or African American, 0.24% are American Indian and Alaska Native, 1.47% are Asian, 0.10% are some other race and 2.34% are multiracial.
https://i.neilsberg.com/ch/clever-mo-population-by-race.jpeg" alt="Clever population by race">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Racial categories include:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Clever Population by Race & Ethnicity. You can refer the same here
This dataset provides information about the number of properties, residents, and average property values for Kennedy Avenue cross streets in Clever, MO.
Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
Building machines that can reason about physical events and their causal relationships is crucial for flexible interaction with the physical world. However, most existing physical and causal reasoning benchmarks are exclusively based on synthetically generated events and synthetic natural language descriptions of causal relationships. This design brings up two issues. First, there is a lack of diversity in both event types and natural language descriptions; second, causal relationships based on manually-defined heuristics are different from human judgments. To address both shortcomings, we present the CLEVRER-Humans benchmark, a video reasoning dataset for causal judgment of physical events with human labels. We employ two techniques to improve data collection efficiency: first, a novel iterative event cloze task to elicit a new representation of events in videos, which we term Causal Event Graphs (CEGs); second, a data augmentation technique based on neural language generative models. ..., We use a three-stage annotation pipeline. The first stage focuses on collecting human-written event descriptions using event cloze tasks, but only for a small number of videos. In the second stage, we augment the data for all videos using neural event description generators trained on the data collected from the first stage. In the third stage, we condense CEGs by collecting binary causal relation labels for all pairs of events from humans.,