torch.utils.data.Dataset
E431012
`torch.utils.data.Dataset` is a core PyTorch abstraction that defines the interface for custom data loading, enabling indexed access to samples and integration with data loaders for efficient batching and shuffling.
All labels observed (1)
| Label | Occurrences |
|---|---|
| torch.utils.data.Dataset canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4326051 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: torch.utils.data.Dataset Context triple: [torchtext, implements, torch.utils.data.Dataset]
-
A.
torchvision (ecosystem)
torchvision is a PyTorch-based computer vision library providing datasets, model architectures, and image transformations commonly used for training and evaluating deep learning models.
-
B.
CIFAR-10
CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
-
C.
torchtext (ecosystem)
torchtext is a PyTorch library that provides tools, datasets, and utilities for building and processing text data in natural language processing workflows.
-
D.
MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
-
E.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: torch.utils.data.Dataset Target entity description: `torch.utils.data.Dataset` is a core PyTorch abstraction that defines the interface for custom data loading, enabling indexed access to samples and integration with data loaders for efficient batching and shuffling.
-
A.
torchvision (ecosystem)
torchvision is a PyTorch-based computer vision library providing datasets, model architectures, and image transformations commonly used for training and evaluating deep learning models.
-
B.
CIFAR-10
CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
-
C.
torchtext (ecosystem)
torchtext is a PyTorch library that provides tools, datasets, and utilities for building and processing text data in natural language processing workflows.
-
D.
MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
-
E.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
PyTorch class
ⓘ
data abstraction ⓘ |
| allows |
lazy loading of samples
ⓘ
on-the-fly data preprocessing ⓘ |
| baseClassFor |
torch.utils.data.ConcatDataset
NERFINISHED
ⓘ
torch.utils.data.IterableDataset ⓘ torch.utils.data.Subset ⓘ torch.utils.data.TensorDataset ⓘ torchvision.datasets.VisionDataset ⓘ |
| belongsTo | deep learning ecosystem ⓘ |
| canBeWrappedBy | torch.utils.data.DataLoader ⓘ |
| category | map-style dataset abstraction ⓘ |
| compatibleWith |
torch.utils.data.BatchSampler
NERFINISHED
ⓘ
torch.utils.data.Sampler ⓘ |
| definedInLanguage | Python NERFINISHED ⓘ |
| designGoal |
decouple data loading from model definition
ⓘ
support large datasets that do not fit in memory ⓘ |
| documentedAt | https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset ⓘ |
| enables |
custom sampling strategies via Sampler
ⓘ
deterministic data ordering when needed ⓘ efficient batching ⓘ parallel data loading ⓘ shuffling of data ⓘ |
| introducedBy | PyTorch data API NERFINISHED ⓘ |
| license | BSD-style (via PyTorch) ⓘ |
| partOf |
PyTorch
NERFINISHED
ⓘ
torch.utils.data module ⓘ |
| pattern | abstract base class pattern ⓘ |
| providesInterfaceFor |
custom datasets
ⓘ
data loading ⓘ |
| requiresImplementationOf |
__getitem__
ⓘ
__len__ ⓘ |
| supports |
__getitem__ method
ⓘ
__len__ method ⓘ arbitrary Python objects as samples ⓘ indexed access to samples ⓘ |
| typicalReturnTypeOfGetItem | (sample, target) tuple ⓘ |
| usedBy |
production ML systems
ⓘ
research codebases ⓘ |
| usedFor |
reinforcement learning datasets
ⓘ
supervised learning datasets ⓘ unsupervised learning datasets ⓘ |
| usedIn |
evaluation loops
ⓘ
inference pipelines ⓘ training loops ⓘ |
| usedWith | torch.utils.data.DataLoader NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: torch.utils.data.Dataset Description of subject: `torch.utils.data.Dataset` is a core PyTorch abstraction that defines the interface for custom data loading, enabling indexed access to samples and integration with data loaders for efficient batching and shuffling.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
torchtext