dataset
In plain wordsAn organized collection of examples gathered for a purpose — such as photos with their captions, or questions with answers — used to teach or test an AI system.
A bounded, usually versioned collection of data records that share a format and were assembled for a stated purpose such as training, fine-tuningfine-tuning, n.: Taking an AI model that has already learned a lot and training it a little more on a smaller, focused set of examples so it gets better at one job or behaves a certain way. or evaluating models. A machine-learning dataset typically specifies its examples (images, documents, dialogues, table rows), any labelslabel, n.: The correct answer attached to an example in a dataset — 'cat' on a photo of a cat, 'positive' on an upbeat review — that a learning system is trained to predict. or annotationsannotation, n.: Adding information to raw data by hand or with software — marking objects in photos, tagging names in sentences, transcribing speech or ranking chatbot answers — so machines can learn from it., a schema or file format, and predefined splits into trainingtraining set, n.: The examples a machine-learning model learns from, like the worked problems a student practices on, kept separate from the examples later used to check how well it learned., validationvalidation set, n.: A set of examples kept out of training and used during development to compare model versions and settings, like a practice exam taken before the real one. and testtest set, n.: A set of examples kept completely apart from training and used at the end to measure how well a model works on data it has never seen, like a sealed final exam. portions so that results are comparable across studies. Well-maintained datasets also document their sources, collection method, license and known limitations (see datasheet for datasetsdatasheet for datasets, n.: A standard document that travels with a dataset, describing why it was made, what is in it, how it was collected and labeled, and what it should and should not be used for.). Landmark datasets such as MNISTMNIST, prop. n.: A famous collection of 70,000 small images of handwritten digits used to test image-recognition programs. Getting a model to read MNIST digits is the 'hello world' of machine learning., ImageNetImageNet, prop. n.: A huge collection of more than 14 million labeled photos, sorted into object categories, whose yearly recognition contest from 2010 to 2017 sparked the deep learning boom., COCOCOCO, prop. n.: A widely used collection of everyday photos in which people outlined and named the objects, described each picture in sentences and marked body joints; it is used to train and compare vision systems. and the web corpora derived from Common CrawlCommon Crawl, prop. n.: A nonprofit's free archive of the public web, begun in 2008 and now refreshed with snapshots of billions of pages every month or two. Most large language models learned much of what they know from text taken from it. have repeatedly defined what the field works on, which is why a flaw in a popular dataset propagates into many models and papers.
The dataset has 50,000 training images and a 10,000-image held-out test split.
Origin
The two-word 'data set' was in computing use by the 1960s (IBM's DOS/360 and OS/360 operating systems used it to mean a file with a record organization) and in statistics for a body of observations. The solid spelling 'dataset' later became the usual form in machine learning, as benchmark collections became research objects in their own right.
Usage note
Both 'dataset' and 'data set' are correct; legal texts such as the EU AI Act use 'data set'. A dataset is not the same as a benchmarkbenchmark, n.: A fixed, shared test — a set of questions or tasks with an agreed way of scoring — used to compare AI systems with one another and to track progress over time., which adds a task, a metric and an evaluation protocol, though many datasets double as benchmarks.
Related
- datadata, n.: Recorded facts, measurements, text, images or sounds that a computer can store and process. AI systems learn their skills from large amounts of data rather than from rules written by hand.
- corpuscorpus, n.: A large collection of written or spoken language, gathered so that people or computers can study it or learn from it — like a library assembled for a language model.
- training datatraining data, n.: The examples an AI system learns from. Whatever patterns, skills, gaps and biases are in the training data tend to show up in the finished system.
- datasheet for datasetsdatasheet for datasets, n.: A standard document that travels with a dataset, describing why it was made, what is in it, how it was collected and labeled, and what it should and should not be used for.
- golden datasetgolden dataset, n.: A small, carefully checked set of example questions with known good answers, used as a trusted yardstick to test an AI system every time it changes.
- data provenancedata provenance, n.: The documented history of a piece of data: where it came from, who created and changed it, under what permissions, and how it was processed before reaching an AI system.
- Hugging FaceHugging Face, prop. n.: A company whose website is the main public library for sharing AI models and datasets — often called the GitHub of machine learning — and which makes widely used free AI software.
- benchmarkbenchmark, n.: A fixed, shared test — a set of questions or tasks with an agreed way of scoring — used to compare AI systems with one another and to track progress over time.
Mentioned in
datadata, n.: Recorded facts, measurements, text, images or sounds that a computer can store and process. AI systems learn their skills from large amounts of data rather than from rules written by hand., training datatraining data, n.: The examples an AI system learns from. Whatever patterns, skills, gaps and biases are in the training data tend to show up in the finished system., COCOCOCO, prop. n.: A widely used collection of everyday photos in which people outlined and named the objects, described each picture in sentences and marked body joints; it is used to train and compare vision systems., corpuscorpus, n.: A large collection of written or spoken language, gathered so that people or computers can study it or learn from it — like a library assembled for a language model., exampleexample, n.: A single item of data used to train or test an AI model, such as one photo with its label, one email marked spam or not, or one question with its answer., Fei-Fei LiFei-Fei Li, prop. n.: A Chinese-American computer scientist whose ImageNet project, millions of hand-labeled photos, gave deep learning the proving ground that launched its boom; she now champions 'human-centered' AI., ImageNetImageNet, prop. n.: A huge collection of more than 14 million labeled photos, sorted into object categories, whose yearly recognition contest from 2010 to 2017 sparked the deep learning boom., MNISTMNIST, prop. n.: A famous collection of 70,000 small images of handwritten digits used to test image-recognition programs. Getting a model to read MNIST digits is the 'hello world' of machine learning., Software 2.0Software 2.0, n.: Andrej Karpathy's 2017 name for software whose behavior is learned from examples by a neural network instead of being written line by line by programmers., task (machine learning)task (machine learning), n.: The job an AI model is meant to do, such as translating sentences, spotting spam or answering questions, defined by its inputs, its expected outputs and how success is scored., Brown CorpusBrown Corpus, prop. n.: The first large collection of English text stored on computer for research: about a million words from 500 American publications of 1961, assembled at Brown University in the 1960s., datasheet for datasetsdatasheet for datasets, n.: A standard document that travels with a dataset, describing why it was made, what is in it, how it was collected and labeled, and what it should and should not be used for., LibriSpeechLibriSpeech, prop. n.: A free collection of about 1,000 hours of volunteers reading English audiobooks, matched with the book text; it became the standard yardstick for speech-recognition systems., Open X-EmbodimentOpen X-Embodiment, prop. n.: A large shared collection of robot training data, pooled by dozens of research labs in 2023 and covering many kinds of robots, so that one AI model could learn from all of them.