datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
dbpedia_14
Dataset Card for DBpedia14
Dataset Summary
The DBpedia ontology classification dataset is constructed by picking 14 non-overlapping classes
from DBpedia 2014. They are listed in classes.txt. From each of thse 14 ontology classes, we
randomly choose 40,000 training samples and 5,000 testing samples. Therefore, the total size
of the training dataset is 560,000 and testing dataset 70,000.
There are 3 columns in the dataset (same for train and test splits), corresponding to… See the full description on the dataset page: https://huggingface.co/datasets/fancyzhx/dbpedia_14.DBPedia_test_top_250_only_w_correct-v2
DBPediaHardNegatives
An MTEB dataset
Massive Text Embedding Benchmark
DBpedia-Entity is a standard test collection for entity search over the DBpedia knowledge base. The hard negative version has been created by pooling the 250 top documents per query from BM25, e5-multilingual-large and e5-mistral-instruct.
Task category
t2t
Domains
Written, Encyclopaedic
Reference
https://github.com/iai-group/DBpedia-Entity/
How to evaluate on this task
You can evaluate… See the full description on the dataset page: https://huggingface.co/datasets/mteb/DBPedia_test_top_250_only_w_correct-v2.dbpedia-entities-openai-1M1M OpenAI Embeddings -- 1536 dimensions
Created: June 2023.
Text used for Embedding: title (string) + text (string)
Embedding Model: text-embedding-ada-002
First used for the pgvector vs VectorDB (Qdrant) benchmark: https://nirantk.com/writing/pgvector-vs-qdrant/
Citation
@dataset{dbpedia-entities-openai-1M,
doi = {10.57967/hf/6768},
url = {https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M},
author = {{Kumar Shivendu} and {Nirant Kasliwal}},
title =… See the full description on the dataset page: https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M.dbpedia
DBPedia
An MTEB dataset
Massive Text Embedding Benchmark
DBpedia-Entity is a standard test collection for entity search over the DBpedia knowledge base
Task category
t2t
Domains
Written, Encyclopaedic
Reference
https://github.com/iai-group/DBpedia-Entity/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["DBPedia"])
evaluator = mteb.MTEB(task)
model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/dbpedia.dbpedia-entities-openai3-text-embedding-3-large-1536-1M1M OpenAI Embeddings: text-embedding-3-large 1536 dimensions
Created: February 2024.
Text used for Embedding: title (string) + text (string)
Embedding Model: OpenAI text-embedding-3-large
This dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity, extracted by @KShivendu_ here
dbpedia_openai_1m
DBpedia OpenAI 1M Dataset
A comprehensive vector database resource containing 1,000,000 DBpedia entity descriptions with pre-computed OpenAI text-embedding-ada-002 embeddings (1536-D). This dataset is optimized for large-scale similarity search, retrieval tasks, and distributed vector database deployments.
Dataset Overview
Size: 1,000,000 base vectors + 10,000 query vectors
Embedding Model: OpenAI text-embedding-ada-002
Dimensions: 1536
Source:… See the full description on the dataset page: https://huggingface.co/datasets/maknee/dbpedia_openai_1m.dbpedia-entities-openai3-text-embedding-3-large-3072-1M1M OpenAI Embeddings: text-embedding-3-large 3072 dimensions + ada-002 1536 dimensions — parallel dataset
Created: February 2024.
Text used for Embedding: title (string) + text (string)
Embedding Model: text-embedding-3-large
This dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity, extracted by @KShivendu_ here
dbpedia-openai-3-large-1M1 million OpenAI Embeddings - 3072 dimensions
Created: February 2024.
Text used for Embedding: title (string) + text (string)
Embedding Model: text-embedding-3-large
Credits:
This dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity
dbpedia-entity
Dataset Card for BEIR Benchmark
dbpedia-entity is one of the datasets from the Entity Retrieval task within BEIR, measuring the retrieval of DbPedia articles for a given query entity.
Dataset Summary
BEIR is a heterogeneous benchmark built from 18 diverse datasets representing 9 information retrieval tasks.
Fact-checking: FEVER, Climate-FEVER, SciFact
Question-Answering: NQ, HotpotQA, FiQA-2018
Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
News Retrieval: TREC-NEWS… See the full description on the dataset page: https://huggingface.co/datasets/BeIR/dbpedia-entity.DBPedia_ClassesAbout Dataset
DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in Wikipedia.
This is an extract of the data (after cleaning, kernel included) that provides taxonomic, hierarchical categories ("classes") for 342,782 wikipedia articles. There are 3 levels, with 9, 70 and 219 classes respectively.
A version of this dataset is a popular baseline for NLP/text classification tasks. This version of the dataset is much tougher… See the full description on the dataset page: https://huggingface.co/datasets/DeveloperOats/DBPedia_Classes.dbpedia-entity-decontaminated
dbpedia-entity (Decontaminated)
A decontaminated version of the dbpedia-entity dataset from the BEIR benchmark, with samples found in the mgte-en pre-training dataset removed.
Decontamination methodology
Contamination was detected using a two-pass approach against the full mgte-en dataset (484 GB, 1,235 parquet files):
Pass 1: Exact hash matching
All texts (queries and corpus documents) were normalized (lowercased, unicode NFKD, whitespace collapsed) and hashed… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/dbpedia-entity-decontaminated.dbpedia-core-en-latestdbpedia-vn
DBPedia-VN
An MTEB dataset
Massive Text Embedding Benchmark
A translated dataset from DBpedia-Entity is a standard test collection for entity search over the DBpedia knowledge base The process of creating the VN-MTEB (Vietnamese Massive Text Embedding Benchmark) from English samples involves a new automated system: - The system uses large language models (LLMs), specifically Coherence's Aya model, for translation. - Applies advanced embedding models to filter the translations. - Use… See the full description on the dataset page: https://huggingface.co/datasets/GreenNode/dbpedia-vn.dbpedia-entity-qrels
Dataset Card for BEIR Benchmark
Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
Fact-checking: FEVER, Climate-FEVER, SciFact
Question-Answering: NQ, HotpotQA, FiQA-2018
Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
News Retrieval: TREC-NEWS, Robust04
Argument Retrieval: Touche-2020, ArguAna
Duplicate Question Retrieval: Quora, CqaDupstack
Citation-Prediction: SCIDOCS
Tweet… See the full description on the dataset page: https://huggingface.co/datasets/BeIR/dbpedia-entity-qrels.dbpedia-labels-en
DBpedia English Labels
Dataset Description
Entity labels from DBpedia (English)
Original Source: https://downloads.dbpedia.org/repo/dbpedia/generic/labels/2022.12.01/labels_lang=en.ttl.bz2
Dataset Summary
This dataset contains RDF triples from DBpedia English Labels converted to HuggingFace dataset format
for easy use in machine learning pipelines.
Format: Originally turtle, converted to HuggingFace Dataset
Size: 1.0 GB (extracted)
Entities: ~9.5M
Triples:… See the full description on the dataset page: https://huggingface.co/datasets/CleverThis/dbpedia-labels-en.dbpedia-entity-generated-queries
Dataset Card for BEIR Benchmark
Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
Fact-checking: FEVER, Climate-FEVER, SciFact
Question-Answering: NQ, HotpotQA, FiQA-2018
Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
News Retrieval: TREC-NEWS, Robust04
Argument Retrieval: Touche-2020, ArguAna
Duplicate Question Retrieval: Quora, CqaDupstack
Citation-Prediction: SCIDOCS
Tweet… See the full description on the dataset page: https://huggingface.co/datasets/BeIR/dbpedia-entity-generated-queries.dbpedia-entities-openai3-text-embedding-3-small-1536-100Kdbpedia-entities-openai3-text-embedding-3-large-1536-100Kdbpedia-openai-1M-text-embedding-3-large-3072ddbpedia-openai-1M-text-embedding-3-large-1536ddbpedia-entities-efficient-splade-100K
DBPedia SPLADE + OpenAI: 100,000 SPLADE Sparse Vectors + OpenAI Embedding
This dataset has both OpenAI and SPLADE vectors for 100,000 DBPedia entries. This adds SPLADE Vectors to KShivendu/dbpedia-entities-openai-1M/
Model id used to make these vectors:
model_id = "naver/efficient-splade-VI-BT-large-doc"
For processing the query, use this:
model_id = "naver/efficient-splade-VI-BT-large-query"
If you'd like to extract the indices and weights/values from the vectors, you can do so… See the full description on the dataset page: https://huggingface.co/datasets/nirantk/dbpedia-entities-efficient-splade-100K.DBpediaClassification
DBpediaClassification
An MTEB dataset
Massive Text Embedding Benchmark
DBpedia14 is a dataset of English texts from Wikipedia articles, categorized into 14 non-overlapping classes based on their DBpedia ontology.
Task category
t2c
Domains
Encyclopaedic, Written
Reference
https://arxiv.org/abs/1509.01626
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/DBpediaClassification.dbpedia-entities-openai3-text-embedding-3-large-3072-100Kdbpedia_14_indexed_sampleThis is a sample from the dbpedia_14_indexed dataset. This dataset contains 10% of the original dataset, randomly sampled by class. I alse removed the embedding columns, as well as merged the title and content columns into a single "text" column.
Labels follow the following map:
id
label
0
Company
1
EducationalInstitution
2
Artist
3
Athlete
4
OfficeHolder
5
MeanOfTransportation
6
Building
7
NaturalPlace
8
Village
9
Animal
10
Plant
11
Album
12
Film
13… See the full description on the dataset page: https://huggingface.co/datasets/joao-luz/dbpedia_14_indexed_sample.dbpedia-openai-1M-text-embedding-3-large-1024ddbpedia_14_indexedThis is the same dataset as dbpedia_14. The only differences are
Addition of a unique identifier, uid
Addition of the indices, that is 3 columns with the embeddings of 3 different sentence-transformers
all-mpnet-base-v2
multi-qa-mpnet-base-dot-v1
all-MiniLM-L12-v2
Renaming of the label column to labels for easier compatibility with the transformers library
task633_dbpedia_14_answer_generation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task633_dbpedia_14_answer_generation
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task633_dbpedia_14_answer_generation.dbpedia_core_en_steaming_downloaddbpedia-biomedical
DBpedia Categories
Dataset Description
Category relationships from DBpedia (English)
Original Source: https://downloads.dbpedia.org/repo/dbpedia/generic/categories/2022.12.01/categories_lang=en_articles.ttl.bz2
Dataset Summary
This dataset contains RDF triples from DBpedia Categories converted to HuggingFace dataset format
for easy use in machine learning pipelines.
Format: Originally turtle, converted to HuggingFace Dataset
Size: 3.0 GB (extracted)
Entities:… See the full description on the dataset page: https://huggingface.co/datasets/CleverThis/dbpedia-biomedical.dbpedia-infoboxes