datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Semantic-Scholar-PapersSemantic-SVG-Benchmark
Semantic SVG Benchmark
A benchmark of 203 SVG files annotated with human-written semantic object-decomposition trees:
every rendered shape (<path>, <rect>, <circle>, …) in each SVG is assigned to a named semantic
object (e.g. judge, gavel), and objects may be further decomposed into parts
(e.g. Bamboo planter → pot, bamboo). It is the evaluation benchmark of
Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing
(EMNLP 2026). The annotations are ours; the SVGs… See the full description on the dataset page: https://huggingface.co/datasets/KU-MIIL/Semantic-SVG-Benchmark.semantic_search_qualityThis is a synthetic dataset containing documents from Wikipedia, Cosmopedia, and CNN DailyMail news datasets containing search keywords that you can use for checking the quality of your semantic search engine
We have also released a library for generating your own synthetic dataset on your own data so you can perform tests.
To build your own datasets, refer Semantic Synth
Semantic-Search-V1-14K
Dataset Card for "Semantic-Search-V1-14K"
More Information needed
SemanticScholarCSFullTextWithOpenAlexTopicssemantic_sentence_similarity_ES
Dataset Card for "semantic_sentence_similarity_ES"
This dataset is based on https://huggingface.co/datasets/PlanTL-GOB-ES/sts-es, which includes the datasets presented at the SemEval 2014 and 2015 shared tasks on sentence similarity (see the link for more info about the citations). It also includes data from SemEval 2017.
semanticsegmentationandposeestimationfromrgbdRGB-D dataset for instance segmentation (from RGB or depth) and pose estimation of individual objects. Data has been generated by randomizing bin contents in Webots.
Each instance contains a mask image as well meta data containing labels, position, and size of each object.
You can create your own data by opening webots_grasp.wbt in the world directory using Webots.
semantic-scholar-manajemen-proyek
Semantic Scholar: Manajemen Proyek
Batch Fetched from Semantic Scholar API, with parameters:
query = '"manajemen proyek" | "project management" | "metodologi manajemen proyek" | "teknik manajemen proyek" | "alat manajemen proyek" | "keterampilan manajemen proyek" | "tantangan manajemen proyek" | "risiko manajemen proyek" | "studi kasus manajemen proyek" | "tren manajemen proyek" | "manajemen proyek di berbagai industri" | "manajemen proyek dalam organisasi" | "manajemen proyek… See the full description on the dataset page: https://huggingface.co/datasets/derhan/semantic-scholar-manajemen-proyek.semantic-scholar-teknik-sipil
Semantic Scholar: Teknik Sipil
Batch fetched from Semantic Scholar API, with parameters:
query = '"manajemen proyek" | "manajemen konstruksi" | "manajemen proyek konstruksi" | "proyek konstruksi" | "proyek" | "konstruksi" | "analisa struktur beton" | "desain jembatan" | "ketahanan gempa" | "pemodelan struktur" | "material komposit" | "mekanika tanah" | "pondasi dalam" | "stabilitas lereng" | "perbaikan tanah" | "geoteknik lingkungan" | "rekayasa lalu lintas" | "perencanaan… See the full description on the dataset page: https://huggingface.co/datasets/derhan/semantic-scholar-teknik-sipil.SemanticStress
Dataset Card
Purpose
SemanticStress evaluates whether vision-language models choose image-faithful captions over semantically stressed alternatives that alter one controlled aspect of meaning.
Code And Reconstruction
The anonymous code release is available at https://anonymous.4open.science/r/SemanticStress-1234/.
This Hugging Face Dataset is a metadata-only release. It intentionally omits image bytes, private local paths, and original source/base captions. To… See the full description on the dataset page: https://huggingface.co/datasets/anonymous-submission-2026/SemanticStress.SemanticScholarCSFullTextWithOpenAlexTopicsSemibalancedsemantic-scholar-casesemantic_scholar_topic_and_ethics_filteredsemantic-scholarA dataset of ~35.1 Million paper abstracts from Semantic Scholar embedded using https://huggingface.co/google/embeddinggemma-300m.