Utilize our machine learning datasets to develop and validate your models. Our datasets are designed to support a variety of machine learning applications, from image recognition to natural language processing and recommendation systems. You can access a comprehensive dataset or tailor a subset to fit your specific requirements, using data from a combination of various sources and websites, including custom ones. Popular use cases include model training and validation, where the dataset can be used to ensure robust performance across different applications. Additionally, the dataset helps in algorithm benchmarking by providing extensive data to test and compare various machine learning algorithms, identifying the most effective ones for tasks such as fraud detection, sentiment analysis, and predictive maintenance. Furthermore, it supports feature engineering by allowing you to uncover significant data attributes, enhancing the predictive accuracy of your machine learning models for applications like customer segmentation, personalized marketing, and financial forecasting.

This dataset consists of imagery, imagery footprints, associated ice seal detections and homography files associated with the KAMERA Test Flights conducted in 2019. This dataset was subset to include relevant data for detection algorithm development. This dataset is limited to data collected during flights 4, 5, 6 and 7 from our 2019 surveys.

This dataset is an extension of my previous work on creating a dataset for natural language processing tasks. It leverages binary representation to characterise various machine learning models. The attributes in the dataset are derived from a dictionary, which was constructed from a corpus of prompts typically provided to a large language model (LLM). These prompts reference specific machine learning algorithms and their implementations. For instance, consider a user asking an LLM or a generative AI to create a Multi-Layer Perceptron (MLP) model for a particular application. By applying this concept to multiple machine learning models, we constructed our corpus. This corpus was then transformed into the current dataset using a bag-of-words approach. In this dataset, each attribute corresponds to a word from our dictionary, represented as a binary value: 1 indicates the presence of the word in a given prompt, and 0 indicates its absence. At the end of each entry, there is a label. Each entry in the dataset pertains to a single class, where each class represents a distinct machine learning model or algorithm. This dataset is intended for multi-class classification tasks, not multi-label classification, as each entry is associated with only one label and does not belong to multiple labels simultaneously. This dataset has been utilised with a Convolutional Neural Network (CNN) using the Keras Automodel API, achieving impressive training and testing accuracy rates exceeding 97%. Post-training, the model's predictive performance was rigorously evaluated in a production environment, where it continued to demonstrate exceptional accuracy. For this evaluation, we employed a series of questions, which are listed below. These questions were intentionally designed to be similar to ensure that the model can effectively distinguish between different machine learning models, even when the prompts are closely related.

KNN How would you create a KNN model to classify emails as spam or not spam based on their content and metadata? How could you implement a KNN model to classify handwritten digits using the MNIST dataset? How would you use a KNN approach to build a recommendation system for suggesting movies to users based on their ratings and preferences? How could you employ a KNN algorithm to predict the price of a house based on features such as its location, size, and number of bedrooms etc? Can you create a KNN model for classifying different species of flowers based on their petal length, petal width, sepal length, and sepal width? How would you utilise a KNN model to predict the sentiment (positive, negative, or neutral) of text reviews or comments? Can you create a KNN model for me that could be used in malware classification? Can you make me a KNN model that can detect a network intrusion when looking at encrypted network traffic? Can you make a KNN model that would predict the stock price of a given stock for the next week? Can you create a KNN model that could be used to detect malware when using a dataset relating to certain permissions a piece of software may have access to?

Decision Tree Can you describe the steps involved in building a decision tree model to classify medical images as malignant or benign for cancer diagnosis and return a model for me? How can you utilise a decision tree approach to develop a model for classifying news articles into different categories (e.g., politics, sports, entertainment) based on their textual content? What approach would you take to create a decision tree model for recommending personalised university courses to students based on their academic strengths and weaknesses? Can you describe how to create a decision tree model for identifying potential fraud in financial transactions based on transaction history, user behaviour, and other relevant data? In what ways might you apply a decision tree model to classify customer complaints into different categories determining the severity of language used? Can you create a decision tree classifier for me? Can you make me a decision tree model that will help me determine the best course of action across a given set of strategies? Can you create a decision tree model for me that can recommend certain cars to customers based on their preferences and budget? How can you make a decision tree model that will predict the movement of star constellations in the sky based on data provided by the NASA website? How do I create a decision tree for time-series forecasting?

Random Forest Can you describe the steps involved in building a random forest model to classify different types of anomalies in network traffic data for cybersecurity purposes and return the code for me? In what ways could you implement a random forest model to predict the severity of traffic congestion in urban areas based on historical traffic patterns, weather...

Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological innovation, Asia Pacific is expected to exhibit the highest CAGR during the forecast period, fueled by the digital transformation of emerging economies and the proliferation of AI applications across various industry sectors.

Data Type Analysis

The AI training dataset market is segmented by data type into Text, Image/Video, Audio, and Others, each playing a crucial role in powering different AI applications. Text da

Xverum’s AI & ML Training Data provides one of the most extensive datasets available for AI and machine learning applications, featuring 800M B2B profiles with 100+ attributes. This dataset is designed to enable AI developers, data scientists, and businesses to train robust and accurate ML models. From natural language processing (NLP) to predictive analytics, our data empowers a wide range of industries and use cases with unparalleled scale, depth, and quality.

What Makes Our Data Unique?

Scale and Coverage: - A global dataset encompassing 800M B2B profiles from a wide array of industries and geographies. - Includes coverage across the Americas, Europe, Asia, and other key markets, ensuring worldwide representation.

Rich Attributes for Training Models: - Over 100 fields of detailed information, including company details, job roles, geographic data, industry categories, past experiences, and behavioral insights. - Tailored for training models in NLP, recommendation systems, and predictive algorithms.

Compliance and Quality: - Fully GDPR and CCPA compliant, providing secure and ethically sourced data. - Extensive data cleaning and validation processes ensure reliability and accuracy.

Annotation-Ready: - Pre-structured and formatted datasets that are easily ingestible into AI workflows. - Ideal for supervised learning with tagging options such as entities, sentiment, or categories.

How Is the Data Sourced? - Publicly available information gathered through advanced, GDPR-compliant web aggregation techniques. - Proprietary enrichment pipelines that validate, clean, and structure raw data into high-quality datasets. This approach ensures we deliver comprehensive, up-to-date, and actionable data for machine learning training.

Primary Use Cases and Verticals

Natural Language Processing (NLP): Train models for named entity recognition (NER), text classification, sentiment analysis, and conversational AI. Ideal for chatbots, language models, and content categorization.

Predictive Analytics and Recommendation Systems: Enable personalized marketing campaigns by predicting buyer behavior. Build smarter recommendation engines for ecommerce and content platforms.

B2B Lead Generation and Market Insights: Create models that identify high-value leads using enriched company and contact information. Develop AI systems that track trends and provide strategic insights for businesses.

HR and Talent Acquisition AI: Optimize talent-matching algorithms using structured job descriptions and candidate profiles. Build AI-powered platforms for recruitment analytics.

How This Product Fits Into Xverum’s Broader Data Offering Xverum is a leading provider of structured, high-quality web datasets. While we specialize in B2B profiles and company data, we also offer complementary datasets tailored for specific verticals, including ecommerce product data, job listings, and customer reviews. The AI Training Data is a natural extension of our core capabilities, bridging the gap between structured data and machine learning workflows. By providing annotation-ready datasets, real-time API access, and customization options, we ensure our clients can seamlessly integrate our data into their AI development processes.

Why Choose Xverum? - Experience and Expertise: A trusted name in structured web data with a proven track record. - Flexibility: Datasets can be tailored for any AI/ML application. - Scalability: With 800M profiles and more being added, you’ll always have access to fresh, up-to-date data. - Compliance: We prioritize data ethics and security, ensuring all data adheres to GDPR and other legal frameworks.

Ready to supercharge your AI and ML projects? Explore Xverum’s AI Training Data to unlock the potential of 800M global B2B profiles. Whether you’re building a chatbot, predictive algorithm, or next-gen AI application, our data is here to help.

Contact us for sample datasets or to discuss your specific needs.

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

SYNERGY is a free and open dataset on study selection in systematic reviews, comprising 169,288 academic works from 26 systematic reviews. Only 2,834 (1.67%) of the academic works in the binary classified dataset are included in the systematic reviews. This makes the SYNERGY dataset a unique dataset for the development of information retrieval algorithms, especially for sparse labels. Due to the many available variables available per record (i.e. titles, abstracts, authors, references, topics), this dataset is useful for researchers in NLP, machine learning, network analysis, and more. In total, the dataset contains 82,668,134 trainable data points. The easiest way to get the SYNERGY dataset is via the synergy-dataset Python package. See https://github.com/asreview/synergy-dataset for all information.

This repository contains the multiple instance learning datasets previously stored at miproblems.org. As I am now longer maintaining the website, I moved the datasets to Figshare. A detailed description of the files is found in readme.pdf

If you use these datasets, please cite this Figshare resource rather than linking to miproblems.org, which will be offline soon.

The diamond is 58 times harder than any other mineral in the world, and its elegance as a jewel has long been appreciated. Forecasting diamond prices is challenging due to nonlinearity in important features such as carat, cut, clarity, table, and depth. Against this backdrop, the study conducted a comparative analysis of the performance of multiple supervised machine learning models (regressors and classifiers) in predicting diamond prices. Eight supervised machine learning algorithms were evaluated in this work including Multiple Linear Regression, Linear Discriminant Analysis, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosted Regression and Classification Trees, and Multi-Layer Perceptron. The analysis is based on data preprocessing, exploratory data analysis (EDA), training the aforementioned models, assessing their accuracy, and interpreting their results. Based on the performance metrics values and analysis, it was discovered that eXtreme Gradient Boosting was the most optimal algorithm in both classification and regression, with a R2 score of 97.45% and an Accuracy value of 74.28%. As a result, eXtreme Gradient Boosting was recommended as the optimal regressor and classifier for forecasting the price of a diamond specimen. Methods Kaggle, a data repository with thousands of datasets, was used in the investigation. It is an online community for machine learning practitioners and data scientists, as well as a robust, well-researched, and sufficient resource for analyzing various data sources. On Kaggle, users can search for and publish various datasets. In a web-based data-science environment, they can study datasets and construct models.

Context

There are lots of datasets available for different machine learning tasks like NLP, Computer vision etc. However I couldn't find any dataset which catered to the domain of software testing. This is one area which has lots of potential for application of Machine Learning techniques specially deep-learning.

This was the reason I wanted such a dataset to exist. So, I made one.

Content

New version [28th Nov'20]- Uploaded testing related questions and related details from stack-overflow. These are query results which were collected from stack-overflow by using stack-overflow's query viewer. The result set of this query contained posts which had the words "testing web pages".

New version[27th Nov'20] - Created a csv file containing pairs of test case titles and test case description.

This dataset is very tiny (approximately 200 rows of data). I have collected sample test cases from around the web and created a text file which contains all the test cases that I have collected. This text file has sections and under each section there are numbered rows of test cases.

Acknowledgements

I would like to thank websites like guru99.com, softwaretestinghelp.com and many other such websites which host great many sample test cases. These were the source for the test cases in this dataset.

Inspiration

My Inspiration to create this dataset was the scarcity of examples showcasing the implementation of machine learning on the domain of software testing. I would like to see if this dataset can be used to answer questions similar to the following--> * Finding semantic similarity between different test cases ranging across products and applications. * Automating the elimination of duplicate test cases in a test case repository. * Cana recommendation system be built for suggesting domain specific test cases to software testers.

Data and code for "Dawson, D.E.; Lau, C.; Pradeep, P.; Sayre, R.R.; Judson, R.S.; Tornero-Velez, R.; Wambaugh, J.F. A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species. Toxics 2023, 11, 98. https://doi.org/10.3390/toxics11020098" Includes a link to R-markdown file allowing the application of the model to novel chemicals. This dataset is associated with the following publication: Dawson, D., C. Lau, P. Pradeep, R. Sayre, R. Judson, R. Tornero-Velez, and J. Wambaugh. A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species. Toxics. MDPI, Basel, SWITZERLAND, 11(2): 98, (2023).

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to assess status and trends of bat populations while developing innovative and community-driven conservation solutions using its unique data and technology infrastructure. To support scalability and transparency in the NABat acoustic data pipeline, we developed a fully-automated machine-learning algorithm. This dataset includes audio files of bat echolocation calls that were considered to develop V1.0 of the NABat machine-learning algorithm, however the test set (i.e., holdout dataset) has been excluded from this release. These recordings were collected by various bat monitoring partners across North America using ultrasonic acoustic recorders for stationary acoustic and mobile acoustic surveys. For more information on how these surveys may be conducted, see Chapters 4 and 5 of “A Plan for the North American Bat Monitoring Program” (https://doi.org/10.2737/SRS-GTR-208). These data were then post-processed by bat monitoring partners to remove noise files (or those that do not contain recognizable bat calls) and apply a species label to each file. There is undoubtedly variation in the steps that monitoring partners take to apply a species label, but the steps documented in “A Guide to Processing Bat Acoustic Data for the North American Bat Monitoring Program” (https://doi.org/10.3133/ofr20181068) include first processing with an automated classifier and then manually reviewing to confirm or downgrade the suggested species label. Once a manual ID label was applied, audio files of bat acoustic recordings were submitted to the NABat database in Waveform Audio File format. From these available files in the NABat database, we considered files from 35 classes (34 species and a noise class). Files for 4 species were excluded due to low sample size (Corynorhinus rafinesquii, N=3; Eumops floridanus, N =3; Lasiurus xanthinus, N = 4; Nyctinomops femorosaccus, N =11). From this pool, files were randomly selected until files for each species/grid cell combination were exhausted or the number of recordings reach 1250. The dataset was then randomly split into training, validation, and test sets (i.e., holdout dataset). This data release includes all files considered for training and validation, including files that had been excluded from model development and testing due to low sample size for a given species or because the threshold for species/grid cell combinations had been met. The test set (i.e., holdout dataset) is not included. Audio files are grouped by species, as indicated by the four-letter species code in the name of each folder. Definitions for each four-letter code, including Family, Genus, Species, and Common name, are also included as a dataset in this release.

This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations. Performance was measured as root-mean squared errors relative to temperature observations during the test period. Test data include compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM Ecological Research Program (https://lter.limnology.wisc.edu/), the Minnesota department of Natural Resources, and the Global Lake Ecological Observatory Network (gleon.org). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

The benchmarking datasets used for deepBlink. The npz files contain train/valid/test splits inside and can be used directly. The files belong to the following challenges / classes:- ISBI Particle tracking challenge: microtubule, vesicle, receptor- Custom synthetic (based on http://smal.ws): particle- Custom fixed cell: smfish- Custom live cell: suntagThe csv files are to determine which image in the test splits correspond to which original image, SNR, and density.

Explore our public notebook content!

Meta Kaggle Code is an extension to our popular Meta Kaggle dataset. This extension contains all the raw source code from hundreds of thousands of public, Apache 2.0 licensed Python and R notebooks versions on Kaggle used to analyze Datasets, make submissions to Competitions, and more. This represents nearly a decade of data spanning a period of tremendous evolution in the ways ML work is done.

Why we’re releasing this dataset

By collecting all of this code created by Kaggle’s community in one dataset, we hope to make it easier for the world to research and share insights about trends in our industry. With the growing significance of AI-assisted development, we expect this data can also be used to fine-tune models for ML-specific code generation tasks.

Meta Kaggle for Code is also a continuation of our commitment to open data and research. This new dataset is a companion to Meta Kaggle which we originally released in 2016. On top of Meta Kaggle, our community has shared nearly 1,000 public code examples. Research papers written using Meta Kaggle have examined how data scientists collaboratively solve problems, analyzed overfitting in machine learning competitions, compared discussions between Kaggle and Stack Overflow communities, and more.

The best part is Meta Kaggle enriches Meta Kaggle for Code. By joining the datasets together, you can easily understand which competitions code was run against, the progression tier of the code’s author, how many votes a notebook had, what kinds of comments it received, and much, much more. We hope the new potential for uncovering deep insights into how ML code is written feels just as limitless to you as it does to us!

Sensitive data

While we have made an attempt to filter out notebooks containing potentially sensitive information published by Kaggle users, the dataset may still contain such information. Research, publications, applications, etc. relying on this data should only use or report on publicly available, non-sensitive information.

Joining with Meta Kaggle

The files contained here are a subset of the KernelVersions in Meta Kaggle. The file names match the ids in the KernelVersions csv file. Whereas Meta Kaggle contains data for all interactive and commit sessions, Meta Kaggle Code contains only data for commit sessions.

File organization

The files are organized into a two-level directory structure. Each top level folder contains up to 1 million files, e.g. - folder 123 contains all versions from 123,000,000 to 123,999,999. Each sub folder contains up to 1 thousand files, e.g. - 123/456 contains all versions from 123,456,000 to 123,456,999. In practice, each folder will have many fewer than 1 thousand files due to private and interactive sessions.

The ipynb files in this dataset hosted on Kaggle do not contain the output cells. If the outputs are required, the full set of ipynbs with the outputs embedded can be obtained from this public GCS bucket: kaggle-meta-kaggle-code-downloads. Note that this is a "requester pays" bucket. This means you will need a GCP account with billing enabled to download. Learn more here: https://cloud.google.com/storage/docs/requester-pays

Questions / Comments

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Happy Kaggling!

Trained models for multi-task multi-dataset learning for sequence tagging in tweets. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py See https://github.com/socialmediaie/SocialMediaIE and https://socialmediaie.github.io for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929

three of these models are available:

Overview: FileMarket's dataset offers 10,000 high-resolution images of professional models, captured in a controlled studio environment by experienced photographers. Each image is expertly lit to ensure clarity and consistency across all photos, making this dataset an invaluable resource for various AI-driven applications.

What Makes This Data Unique? This dataset stands out due to its meticulous attention to quality. Each model is photographed from multiple angles, providing a comprehensive view that is ideal for AI training. The diversity of models, encompassing various ethnicities, ages, and body types, ensures that the data is representative and inclusive. The consistency in lighting and background across all images reduces the need for additional preprocessing, making the data immediately usable for machine learning and deep learning projects.

Data Sourcing: The images in this dataset were sourced exclusively from professional studio shoots. The controlled environment ensures that each image meets the highest standards, with consistent lighting, background, and quality. The photographers involved have extensive experience in fashion and commercial photography, guaranteeing that every image is of premium quality.

Primary Use-Cases: This dataset is versatile and can be effectively used in several AI and machine learning contexts, including:

Object Detection Data: The clear and consistent images make this dataset ideal for training models in object detection, specifically in identifying human figures and facial features. Machine Learning (ML) Data: The diversity and high quality of the images are perfect for feeding into machine learning algorithms, particularly those focused on human recognition and categorization. Deep Learning (DL) Data: The multi-angle shots of models offer a rich dataset for deep learning models that require a variety of perspectives to improve accuracy, such as in 3D reconstruction and pose estimation. Biometric Data: The detailed and varied images are suitable for training biometric systems, enhancing their ability to recognize and verify individuals across different conditions and contexts. Broader Data Offering: This dataset integrates seamlessly with other FileMarket offerings, allowing data buyers to combine it with other data types, such as text or video data, for more comprehensive AI training models. Whether for enhancing virtual try-on technologies for clothing and makeup or improving the accuracy of biometric systems, this dataset serves as a cornerstone in developing robust AI applications.