Hugging Face Datasets
E435888
Python library
data processing framework
machine learning tool
open-source software
software library
Hugging Face Datasets is an open-source library that provides a large collection of ready-to-use datasets and efficient data loading tools for machine learning and natural language processing workflows.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Hugging Face Datasets canonical | 2 |
| Datasets library | 1 |
| https://github.com/huggingface/datasets | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4389242 — 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: Hugging Face Datasets Context triple: [Hugging Face Transformers, compatibleWith, Hugging Face Datasets]
-
A.
Common Voice dataset
The Common Voice dataset is a large, open-source multilingual speech corpus created by Mozilla to support and democratize voice recognition research and technology.
-
B.
Open Data Index
Open Data Index is a global initiative that evaluates and ranks the openness and accessibility of government data across countries.
-
C.
Dataverse
Dataverse is Microsoft's cloud-based data platform that securely stores, manages, and structures business data for use across Power Platform applications and services.
-
D.
Harvard Dataverse
Harvard Dataverse is a research data repository platform that enables scholars to share, publish, and preserve datasets, primarily used by the academic community.
-
E.
Open Data Lab
Open Data Lab is a World Wide Web Foundation initiative that supports the use of open data to drive social impact, innovation, and better governance, particularly in developing countries.
- 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: Hugging Face Datasets Target entity description: Hugging Face Datasets is an open-source library that provides a large collection of ready-to-use datasets and efficient data loading tools for machine learning and natural language processing workflows.
-
A.
Common Voice dataset
The Common Voice dataset is a large, open-source multilingual speech corpus created by Mozilla to support and democratize voice recognition research and technology.
-
B.
Open Data Index
Open Data Index is a global initiative that evaluates and ranks the openness and accessibility of government data across countries.
-
C.
Dataverse
Dataverse is Microsoft's cloud-based data platform that securely stores, manages, and structures business data for use across Power Platform applications and services.
-
D.
Harvard Dataverse
Harvard Dataverse is a research data repository platform that enables scholars to share, publish, and preserve datasets, primarily used by the academic community.
-
E.
Open Data Lab
Open Data Lab is a World Wide Web Foundation initiative that supports the use of open data to drive social impact, innovation, and better governance, particularly in developing countries.
- F. None of above. chosen
Statements (52)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
data processing framework ⓘ machine learning tool ⓘ open-source software ⓘ software library ⓘ |
| compatibleWith |
Hugging Face Tokenizers
ⓘ
Hugging Face Transformers ⓘ JAX ⓘ NumPy ⓘ Pandas ⓘ
linked to:
pandas
PyTorch ⓘ TensorFlow ⓘ |
| developer | Hugging Face ⓘ |
| documentation | https://huggingface.co/docs/datasets ⓘ |
| feature |
arrow-based storage
ⓘ
dataset caching ⓘ dataset card metadata ⓘ dataset streaming from hub ⓘ dataset versioning ⓘ filtering ⓘ integration with Hugging Face Hub ⓘ load from hub ⓘ map-style transforms ⓘ memory-mapped datasets ⓘ on-the-fly preprocessing ⓘ parquet export ⓘ push to hub ⓘ ready-to-use datasets ⓘ sharding and batching ⓘ streaming data loading ⓘ train-test-validation splits ⓘ |
| hasComponent |
datasets library
ⓘ
datasets server-side tooling ⓘ |
| license | Apache License 2.0 ⓘ |
| offers |
community-contributed datasets
ⓘ
curated benchmark datasets ⓘ dataset loading scripts ⓘ |
| partOf | Hugging Face ecosystem ⓘ |
| programmingLanguage |
Python
ⓘ
Rust ⓘ |
| repository |
https://github.com/huggingface/datasets
ⓘ
linked to:
Hugging Face Datasets
|
| supportsTask |
audio processing
ⓘ
computer vision ⓘ machine learning ⓘ multimodal learning ⓘ natural language processing ⓘ |
| typicalUse |
benchmarking
ⓘ
data preprocessing ⓘ dataset exploration ⓘ model evaluation ⓘ model training ⓘ |
| usesFormat | Apache Arrow ⓘ |
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: Hugging Face Datasets Description of subject: Hugging Face Datasets is an open-source library that provides a large collection of ready-to-use datasets and efficient data loading tools for machine learning and natural language processing workflows.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Hugging Face Datasets
linked to: Hugging Face Datasets