datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
en_wikidata_5M_entities
en_wikidata_5M_entities
Hugging Face dataset card for a large, English-only Wikidata slice with optional Wikipedia links and Wikimedia Commons image URLs.
One file, five million entities.
Filename: en_wikidata_5M_entities.jsonl.gz
TL;DR
Format: JSON Lines, gzip-compressed (.jsonl.gz)
Rows: 5,000,000 entities (one JSON object per line)
Language: English labels/descriptions
Fields: qid, label, description, enwiki_title, wikipedia_url, images (list of URLs), has_image… See the full description on the dataset page: https://huggingface.co/datasets/Vijaysr4/en_wikidata_5M_entities.ben-entities
BEN Entities
Full BEN entity extraction results exported from MongoDB as Hub-native
jsonl.gz shards.
Each row contains only document_id and entities. Scores are filtered with
threshold 0.6 and rounded to two decimals.
Configs
pubmed from Mongo collection pubmed_ncbi
pmc from Mongo collection pmc_xml
uspto from Mongo collection patent_uspto
clinical_trial from Mongo collection clinical_trial_gov
Usage
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/BioMedBigDataCenter/ben-entities.sentinel-kr-sensitive-entities-synthetic-v3
Sentinel KR Sensitive Entities (Synthetic) v3
Overview
Sentinel KR Sensitive Entities (Synthetic) v3 is a Korean synthetic (AI-generated) dataset for whitelist-only sensitive-entity detection in DLP / LLM guardrail scenarios.
All sensitive values in this dataset (e.g., phone numbers, emails, IDs, tokens, keys) are artificially generated by AI and do not come from real individuals, real incidents, or collected private datasets.
Any resemblance to real persons or real… See the full description on the dataset page: https://huggingface.co/datasets/BoB14TeamSentinel/sentinel-kr-sensitive-entities-synthetic-v3.qkg-primekg-entities-with-cui
Data Card: qkg-primekg-entities-with-cui
Summary
qkg-primekg-entities-with-cui.jsonl is the QKG entity table derived from PrimeKG and enriched with UMLS CUI annotations. It provides the entity inventory used by the QKG runtime for entity lookup and UMLS-
backed synonym matching.
This artifact is intended to be loaded into MongoDB collection:
primeKG.entities
Paper
This artifact is released with the paper:
Yao Wang, Zixu Geng, and Jun Yan. Quantum… See the full description on the dataset page: https://huggingface.co/datasets/HKAI-Sci/qkg-primekg-entities-with-cui.en_wikidata_5M_entities
en_wikidata_5M_entities
Hugging Face dataset card for a large, English-only Wikidata slice with optional Wikipedia links and Wikimedia Commons image URLs.
One file, five million entities.
Filename: en_wikidata_5M_entities.jsonl.gz
TL;DR
Format: JSON Lines, gzip-compressed (.jsonl.gz)
Rows: 5,000,000 entities (one JSON object per line)
Language: English labels/descriptions
Fields: qid, label, description, enwiki_title, wikipedia_url, images (list of URLs)… See the full description on the dataset page: https://huggingface.co/datasets/dhruv-anand-aintech/en_wikidata_5M_entities.qa_entitiesfinancial-entities-values-augmentedThis dataset is contains 200 sentences taken from German financial statements. In each sentence financial entities and financial values are annotated. Additionally there is an augmented version of this dataset where the financial entities in each sentence have been replaced by several other financial entities which are hardly/not covered in the original dataset. The augmented version consists of 7287 sentences.
resume_entitiesentities-laptopDataset that can be used to fine-tune models to extract entities in a specific format.
ben-entities-sample
BEN Entities PubMed Pilot
10-part PubMed NCBI BEN pilot dataset.
Use it with:
from datasets import load_dataset
ds = load_dataset("john-dope/ben-entities-sample", name="pubmed", split="train")
print(ds[0]["document_id"])
ABP_Entitieshebrew-wiki-entitiestw-judgment-entities-extraction-datasetNMB-Plus-Named-Entities
Dataset Card for Bias Named Entity Recognization
Dataset Summary
This is a Named Entity Recognition (NER) dataset designed to identify and classify entities in text. The dataset follows the BIO tagging scheme, with annotations for bias-related terms and general named entities. This dataset is useful for training models that detect bias indicators in text and classify different entity types.
Supported Tasks and Leaderboards
Named Entity Recognition (NER):… See the full description on the dataset page: https://huggingface.co/datasets/vector-institute/NMB-Plus-Named-Entities.