Dataset Card for "ai2_arc"

  Dataset Summary

A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to… See the full description on the dataset page: https://huggingface.co/datasets/allenai/ai2_arc.

lilac/ARC-Easy

This dataset is a Lilac processed dataset. Original dataset: https://huggingface.co/datasets/allenai/ai2_arc To download the dataset to a local directory: lilac download lilacai/lilac-ARC-Easy

or from python with: ll.download("lilacai/lilac-ARC-Easy")

The AI2’s Reasoning Challenge (ARC) dataset is a multiple-choice question-answering dataset, containing questions from science exams from grade 3 to grade 9. The dataset is split in two partitions: Easy and Challenge, where the latter partition contains the more difficult questions that require reasoning. Most of the questions have 4 answer choices, with <1% of all the questions having either 3 or 5 answer choices. ARC includes a supporting KB of 14.3M unstructured text passages.

            Source: Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

Euan-M-Campbell/ai2-arc-easy-mini dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is a deduplicated subset of ARC-Easy, as used in the paper How Much Can We Forget about Data Contamination?. The deduplication was performed using this script. The data fields are the same as in https://huggingface.co/datasets/allenai/ai2_arc, with the additional "split-id" column that can be used to partition the benchmark questions into different subsets. The dataset can be used as a plug-in replacement for ARC-Easy if you want to work with the deduplicated benchmark questions.… See the full description on the dataset page: https://huggingface.co/datasets/sbordt/forgetting-contamination-arc-easy.

ARC Direct Answer Questions (ARC-DA) dataset consists of 2,985 grade-school level, direct-answer ("open response", "free form") science questions derived from the ARC multiple-choice question set released as part of the AI2 Reasoning Challenge in 2018. How the dataset was built These questions were derived from the ARC multiple-choice question set released as part of the AI2 Reasoning Challenge in 2018. The ARC Easy and ARC Challenge set questions in the original dataset were combined and then filtered/modified by the following process:

fair-forward/arc-easy-autotranslated dataset hosted on Hugging Face and contributed by the HF Datasets community

Thanmay/arc-easy-ml dataset hosted on Hugging Face and contributed by the HF Datasets community

yangzhang33/E2H-ARC-Easy dataset hosted on Hugging Face and contributed by the HF Datasets community

INSAIT-Institute/arc-easy-bgeval dataset hosted on Hugging Face and contributed by the HF Datasets community

Gamma analysis on patient plans of simple targets for full and partial (*) arc plans.

Generally speaking, the stakes are people, property, activities, cultural or environmental heritage elements, threatened by a hazard and likely to be affected or damaged by it. The sensitivity of an issue to a hazard is called “vulnerability”. This object class brings together all the issues that have been addressed in the RPP study. An issue is a dated object whose consideration depends on the purpose of the RPP and its vulnerability to the hazards studied. A PPR issue can therefore be considered (or not) depending on the type or types of hazard being addressed. These elements form the basis of knowledge of the land cover necessary for the development of the RPP, in or near the study area, at the time of the analysis of the issues. The data on issues represent a (figible and non-exhaustive) photograph of assets and individuals exposed to hazards at the time of the development of the risk prevention plan. This data is not updated after approval of the RPP. In practice they are no longer used: the issues are recalculated as necessary with up-to-date data sources.

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.

This dataset contains the boundaries at the different stages of the development of the PPRT. The characteristic of these perimeters is to be the consequence of an official act and to produce their effects from a specified date. This is the following: — prescribed scope contained in the prescription order of a PPR (natural or technological); — scope of risk exposure that corresponds to the scope regulated by the approved RPP. This approved perimeter is a utility easement (PM3 for PPRTs); — scope of study which corresponds to the envelope in which the hazards were studied.

Dataset Card for Uhura-Arc-Easy

  Dataset Summary

Uhura-ARC-Easy is a widely recognized scientific question answering benchmark composed of multiple-choice science questions derived from grade-school examinations that test various styles of knowledge and reasoning. The original English version of the benchmark originates from Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge (Clark et al., 2018) and is divided into "Challenge" and "Easy"… See the full description on the dataset page: https://huggingface.co/datasets/masakhane/uhura-arc-easy.

Robotic Arc Welding System Market Outlook

The global robotic arc welding system market size was valued at approximately USD 3.8 billion in 2023 and is projected to reach USD 8.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.6% during the forecast period. This robust growth is fueled by the increasing adoption of automated welding processes to enhance productivity and precision across various industries.

One of the primary growth factors for the robotic arc welding system market is the rising demand for high-quality welding in industries such as automotive, aerospace, and construction. The automotive sector, in particular, is witnessing significant investments in automation to meet the growing consumer demand for vehicles and to comply with stringent safety and emission regulations. Moreover, the need for consistency and precision in welding, which is critical in manufacturing processes, is propelling the adoption of robotic arc welding systems. Additionally, advancements in robotic technology, such as the integration of artificial intelligence and machine learning, are enhancing the capabilities and efficiency of these systems, further driving market growth.

Another key driver is the increasing labor costs and shortage of skilled welders, which are compelling industries to shift towards automation. The use of robotic arc welding systems reduces reliance on human labor, minimizes errors, and ensures high-quality welds. This not only enhances productivity but also leads to significant cost savings in the long run. Furthermore, the development of easy-to-program and user-friendly robotic systems is making it easier for small and medium-sized enterprises (SMEs) to adopt automation, thereby expanding the market's reach.

Environmental regulations and safety standards are also playing a crucial role in driving the market. Robotic arc welding systems minimize the exposure of workers to hazardous fumes and reduce the risk of workplace accidents, thereby ensuring a safer working environment. Additionally, these systems contribute to energy efficiency and lower wastage, aligning with the global push towards sustainable manufacturing practices. The integration of Internet of Things (IoT) and real-time monitoring capabilities allows for predictive maintenance and enhances the overall efficiency of welding operations.

Regionally, the Asia Pacific market is expected to exhibit significant growth due to the rapid industrialization and expansion of the manufacturing sector in countries like China, India, and Japan. North America and Europe are also key markets, driven by the presence of established automotive and aerospace industries and a high level of technological adoption. Latin America and the Middle East & Africa are anticipated to witness moderate growth, supported by investments in infrastructure and industrial development.

Robotic Welding Service is increasingly becoming a crucial component in the manufacturing sector, offering unparalleled precision and efficiency. As industries strive to enhance productivity and maintain high-quality standards, the demand for robotic welding services is on the rise. These services provide a comprehensive solution that includes not only the deployment of robotic systems but also the integration of advanced technologies such as AI and IoT for optimized operations. Companies are leveraging these services to reduce operational costs, improve safety, and ensure consistent welding quality. The ability to customize robotic welding services to meet specific industry needs further underscores their growing importance in the global market.

Type Analysis

The robotic arc welding system market by type is segmented into hardware, software, and services. The hardware segment comprises the physical components of the welding robots, including robotic arms, controllers, and welding equipment. This segment accounts for the largest share of the market, driven by the continuous advancements in robotic technologies and the increasing deployment of robotic systems across various industries. The integration of advanced sensors and actuators in hardware components is enhancing the precision and efficiency of robotic welding, thereby driving demand.

The software segment includes programming and simulation software that controls the robotic welding systems. This segment is witnessing rapid growth due to the increasing need for sophisticated software solutions th

LARC is a dataset built from ARC (Abstraction and Reasoning Corpus). ARC is a set of tasks that tests an agent's ability to flexibly solve novel problems. While most ARC tasks are easy for humans, they are challenging for state-of-the-art AI. LARC or Language-annotated ARC, is a collection of natural language descriptions by a group of human participants, unfamiliar both with ARC and with each other, who instruct each other on how to solve ARC tasks. LARC contains successful instructions for 88% of the ARC tasks.

Abstract: Arc Basalt Simulator (ABS) is a petrologic and geochemical simulation program for basalt to high-Mg andesite genesis in and above a subduction zone. ABS consists of three different calculation modules: (a) slab dehydration and melting along an appropriate subduction zone P-T path, (b) reaction of mantle peridotite with slab-derived melt or hydrous fluid migrating through the mantle wedge, and (c) melting of mantle wedge peridotite due to fluxing by this liquid. ABS is intended to be easy to use, requiring only a PC or Mac that can run Microsoft Excel. The download includes two files, the macro-enabled spreadsheet (.xlsm) and the tutorial (.pdf). Other Description: [1] Kimura J.-I., van Keken P., Hacker, B.R., Kawabata H., Yoshida, T., and Stern R.J. (2009) Arc Basalt Simulator (ABS) version 2, a simulation model for slab dehydration, fluid-mantle reaction, and fluid fluxed mantle melting for arc basalts: modeling scheme and application. Geochemistry Geophysics Geosystems (G3), 7, doi:10.1029/2008GC002217.; [2] Kimura J.-I., Adam, J.R.K., Rowe, M., Nakano, N., Katakuse M., van Keken P., Hacker B., and Stern R.J. (2010) Origin of cross-chain geochemical variation in Quaternary lavas from northern Izu arc: A quantitative mass balance approach on source identification and mantle wedge processes. Geochemistry Geophysics Geosystems (G3), doi:10.1029/2010GC003050.

This layer contains the perimeters of risk exposure of Arc-sur-Tille’s natural risk prevention plan

Arc-easy test set rewritten with gpt-4o 20x over.