Dataset Card for example-preference-dataset

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/example-preference-dataset/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/distilabel-internal-testing/example-generate-preference-dataset.

Context

This dataset was created only for making examples every columns has generated with random values. If you wanna create a dataset similar like this review this notebook

Content

There are five columns 'Country' = 'Bolivia', :'Argentina','Paraguay','Chile','Brazil','Peru' 'Temperature' 'Humidity' 'Pm10' 'Date'

This dataset was collected from an industrial control system running the Modbus protocol. It is used to train a deep adversarial learning model. This model is used to generate fuzzing data in the same format as the real one. The data is a sequence of hexadecimal numbers. The followed generated data is produced by the already trained model.

Generate

## Overview

Generate is a dataset for object detection tasks - it contains Objects HRDh annotations for 1,172 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The code, strainenergy_v4_1.m, was used for generating and processing the dataset for load-displacement and stress-strain. Software Matlab version 6.1 was used for running the code. The specific variables of the parameters used to generate the current dataset are as follows:• ip1: input file containing the load-displacement data• diameter: fascicle diameter• laststrainpt: an estimate of the strain at rupture, r• orderpoly: an integral value from 2-7 which represents the order of the polynomial for fitting to the data from O to q• loadat1percent: y/n; to determine the value of the load (set at 1% of the maximum load) at which the specimen became taut. ‘y’ denotes yes; ‘n’ denotes no.The logfile.txt, contains the parameters used for deriving the values of the respective mechanical properties.

fauzanrrizky/generate-quiz-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset includes random number generated through various methods.Method 1: shuf https://www.mankier.com/1/shufCommands used to generate dataset files: $ shuf -i 1-1000000000 -n1000000 -o random-shuf.txt$ shuf -i 1-1000000000000 -n1000000 -o random-shuf-1-1000000000000.txt$ jot -r 1000000 1 1000000000000 > random-jot-1-1000000000000.txt

Dataset Card for my-dataset-generate

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/Bipul8765/my-dataset-generate/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/Bipul8765/my-dataset-generate.

The invoice dataset provided is a mock dataset generated using the Python Faker library. It has been designed to mimic the format of data collected from an online store. The dataset contains various fields, including first name, last name, email, product ID, quantity, amount, invoice date, address, city, and stock code. All of the data in the dataset is randomly generated and does not represent actual individuals or products. The dataset can be used for various purposes, including testing algorithms or models related to invoice management, e-commerce, or customer behavior analysis. The data in this dataset can be used to identify trends, patterns, or anomalies in online shopping behavior, which can help businesses to optimize their online sales strategies.

The CREATE database is composed of 14 hours of multimodal recordings from a mobile robotic platform based on the iRobot Create.

Search strings used to generate citation counts for three data sets in WoS, publishers' full text websites, and Google Scholar.

With the wide application of large models in various fields, the demand for high-quality data sets in the tourism industry is increasing to support the improvement of the model 's ability to understand and generate tourism information. This dataset focuses on textual data in the tourism domain and is designed to support fine-tuning tasks for tourism-oriented large models, aiming to enhance the model's ability to understand and generate tourism-related information. The diversity and quality of the dataset are critical to the model's performance. Therefore, this study combines web scraping and manual annotation techniques, along with data cleaning, denoising, and stopword removal, to ensure high data quality and accuracy. Additionally, automated annotation tools are used to generate instructions and perform consistency checks on the texts. The LLM-Tourism dataset primarily relies on data from Ctrip and Baidu Baike, covering five Northwestern Chinese provinces: Gansu, Ningxia, Qinghai, Shaanxi, and Xinjiang, containing 53,280 pairs of structured data in JSON format. The creation of this dataset will not only improve the generation accuracy of tourism large models but also contribute to the sharing and application of tourism-related datasets in the field of large models.

Title: Rule-based Synthetic Data for Japanese GEC. Dataset Contents:This dataset contains two parallel corpora intended for the training and evaluating of models for the NLP (natural language processing) subtask of Japanese GEC (grammatical error correction). These are as follows:Synthetic Corpus - synthesized_data.tsv. This corpus file contains 2,179,130 parallel sentence pairs synthesized using the process described in [1]. Each line of the file consists of two sentences delimited by a tab. The first sentence is the erroneous sentence while the second is the corresponding correction.These paired sentences are derived from data scraped from the keyword-lookup site

Parameter values used to generate expected value data sets.

Connector Generate Dataset

## Overview

Connector Generate Dataset is a dataset for object detection tasks - it contains Defect annotations for 255 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data- generating models are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case, we propose a framework for the simulation of random-effect network meta-analyses including multi-arm trials with binary outcome. The only of the common data-generating models which is directly applicable to a random-effects network setting uses strongly restrictive assumptions. To overcome these limitations, we modify this approach and derive a related simulation procedure using odds ratios as effect measure. The performance of this procedure is evaluated with synthetic data and in an empirical example.

AI Generate Detection

## Overview

AI Generate Detection is a dataset for classification tasks - it contains AI Human annotations for 9,900 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Dog Tail After Generate 1 Class Tail

## Overview

Dataset Dog Tail  After Generate  1 Class Tail is a dataset for object detection tasks - it contains Dogs 6dBV annotations for 2,252 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Data sets used to prepare illustrative figures for the overview article “Multiscale Modeling of Background Ozone” Overview

The CMAQ model output datasets used to create illustrative figures for this overview article were generated by scientists in EPA/ORD/CEMM and EPA/OAR/OAQPS.

The EPA/ORD/CEMM-generated dataset consisted of hourly CMAQ output from two simulations. The first simulation was performed for July 1 – 31 over a 12 km modeling domain covering the Western U.S. The simulation was configured with the Integrated Source Apportionment Method (ISAM) to estimate the contributions from 9 source categories to modeled ozone. ISAM source contributions for July 17 – 31 averaged over all grid cells located in Colorado were used to generate the illustrative pie chart in the overview article. The second simulation was performed for October 1, 2013 – August 31, 2014 over a 108 km modeling domain covering the northern hemisphere. This simulation was also configured with ISAM to estimate the contributions from non-US anthropogenic sources, natural sources, stratospheric ozone, and other sources on ozone concentrations. Ozone ISAM results from this simulation were extracted along a boundary curtain of the 12 km modeling domain specified over the Western U.S. for the time period January 1, 2014 – July 31, 2014 and used to generate the illustrative time-height cross-sections in the overview article.

The EPA/OAR/OAQPS-generated dataset consisted of hourly gridded CMAQ output for surface ozone concentrations for the year 2016. The CMAQ simulations were performed over the northern hemisphere at a horizontal resolution of 108 km. NO2 and O3 data for July 2016 was extracted from these simulations generate the vertically-integrated column densities shown in the illustrative comparison to satellite-derived column densities.

CMAQ Model Data

The data from the CMAQ model simulations used in this research effort are very large (several terabytes) and cannot be uploaded to ScienceHub due to size restrictions. The model simulations are stored on the /asm archival system accessible through the atmos high-performance computing (HPC) system. Due to data management policies, files on /asm are subject to expiry depending on the template of the project. Files not requested for extension after the expiry date are deleted permanently from the system. The format of the files used in this analysis and listed below is ioapi/netcdf. Documentation of this format, including definitions of the geographical projection attributes contained in the file headers, are available at https://www.cmascenter.org/ioapi/

Documentation on the CMAQ model, including a description of the output file format and output model species can be found in the CMAQ documentation on the CMAQ GitHub site at https://github.com/USEPA/CMAQ.

This dataset is associated with the following publication: Hogrefe, C., B. Henderson, G. Tonnesen, R. Mathur, and R. Matichuk. Multiscale Modeling of Background Ozone: Research Needs to Inform and Improve Air Quality Management. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 1-6, (2020).

Generate Ray

## Overview

Generate Ray is a dataset for object detection tasks - it contains 0 1 2 3 4 KH9O annotations for 279 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).