datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
fineweb-edu-indexThis dataset contains the embeddings for the full fineweb-edu, embedded with the Cohere Embed V3 model.
You can search on this dataset with just 500MB of memory using DiskVectorIndex.
Installation & Usage
Get your free Cohere API key from cohere.com. You must set this API key as an environment variable:
export COHERE_API_KEY=your_api_key
Install the package:
pip install DiskVectorIndex
You can then search via:
from DiskVectorIndex import DiskVectorIndex
index =… See the full description on the dataset page: https://huggingface.co/datasets/fineweb-retrieval/fineweb-edu-index.cosqaEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/cosqa.appsEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/apps.multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR
PreFLMR M2KR Dataset Card
Dataset details
Dataset type:
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:… See the full description on the dataset page: https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR.flickr_1k_test_image_text_retrieval
Flickr30k (1K test set)
Original paper: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
1K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip
Bibtex:
@article{young2014image,
title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions}… See the full description on the dataset page: https://huggingface.co/datasets/nlphuji/flickr_1k_test_image_text_retrieval.webfaq-retrievalWebFAQ Retrieval Dataset
Overview |
Details |
Structure |
Examples |
Considerations |
License |
Citation |
Contact |
Acknowledgement
Overview
The WebFAQ Retrieval Dataset is a carefully filtered and curated subset of the broader WebFAQ Q&A Dataset.It is purpose-built for Information Retrieval (IR) tasks, such as training and evaluating dense or sparse retrieval models in multiple languages.
Each of the… See the full description on the dataset page: https://huggingface.co/datasets/PaDaS-Lab/webfaq-retrieval.stackoverflow-qaEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/stackoverflow-qa.CodeSearchNetEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/CodeSearchNet.mscoco_2014_5k_test_image_text_retrieval
MSCOCO (5K test set)
Original paper: Microsoft COCO: Common Objects in Context
Homepage: https://cocodataset.org/#home
5K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip
Bibtex:
@inproceedings{lin2014microsoft,
title={Microsoft coco: Common objects in context},
author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence}… See the full description on the dataset page: https://huggingface.co/datasets/nlphuji/mscoco_2014_5k_test_image_text_retrieval.CodeSearchNet-ccrEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/CodeSearchNet-ccr.colbert-retrieval-mined-examples
Cantivia internal retrieval training artifacts
Proprietary. All rights reserved.
This repository stores working artifacts of Cantivia's reranker training pipeline: mined candidate
pools, hard negatives, group caches, relevance scores and their manifests. It is published only so
Cantivia's own jobs can reach it; it is not a public dataset, and nothing here is released for
download, redistribution, derivative works or model training.
Licence
Cantivia's own… See the full description on the dataset page: https://huggingface.co/datasets/Feargal/colbert-retrieval-mined-examples.FinQA_retrievalcodefeedback-stEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/codefeedback-st.synthetic-text2sqlEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/synthetic-text2sql.codetrans-dlEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/codetrans-dl.IndicDiarBench-speaker-retrieval
Indic DiarBench speaker retrieval (MTEB)
Indic DiarBench reshaped for speaker retrieval across the 22 scheduled languages
of India: given a clip of one speaker, find other clips of that same speaker.
Source: sarvamai/indic-diarbench at revision 92877ba, cc-by-4.0, official
test split. Turns are cut by their annotated times, restricted to 2 to 15
seconds, and turns overlapping a different speaker are dropped. identity pairs
the recording session with the speaker, because speaker… See the full description on the dataset page: https://huggingface.co/datasets/vnahata/IndicDiarBench-speaker-retrieval.codefeedback-mtEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/codefeedback-mt.codetrans-contestEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/codetrans-contest.OmnilingualASR-retrieval
Omnilingual ASR speech-text retrieval (MTEB)
Read speech paired with its human transcription, for languages that no existing
MTEB audio task covers.
Source: facebook/omnilingual-asr-corpus at revision 8648ba8, cc-by-4.0, official
test split. Recordings are re-encoded from FLAC to Opus at 16 kHz. Repeated
transcripts are dropped, since one would otherwise be relevant to several
recordings while only one is marked correct.
Built by… See the full description on the dataset page: https://huggingface.co/datasets/vnahata/OmnilingualASR-retrieval.kv_retrievalMultiHiertt_retrievalAfriMCQA-qa-retrieval
Afri-MCQA visual QA retrieval (MTEB)
Culturally grounded multiple choice questions about photographs, in 16 African
languages. A query is a question with its photograph; the corpus holds every
distinct answer option in that language, so a question is ranked against the
whole option pool rather than only its own four.
Built from Atnafu/Afri-MCQA at revision 8b8c53d, cc-by-nc-4.0, using
the official dev split, the only one where the correct answer is labelled.
Built by… See the full description on the dataset page: https://huggingface.co/datasets/vnahata/AfriMCQA-qa-retrieval.github-readme-retrieval-multilingual_beirThis is a copy of https://huggingface.co/datasets/jinaai/github-readme-retrieval-multilingual reformatted into the BEIR format. For any further information like license, please refer to the original dataset.
Disclaimer
This dataset may contain publicly available images or text data. All data is provided for research and educational purposes only. If you are the rights holder of any content and have concerns regarding intellectual property or copyright, please contact us at… See the full description on the dataset page: https://huggingface.co/datasets/jinaai/github-readme-retrieval-multilingual_beir.multimodal-tip-of-the-tongue-retrieval-for-scientific-documents
Open-Source Scientific Documents
This repository contains the scientific-paper corpus and generated artifacts for Multimodal Tip-of-the-Tongue Retrieval for Scientific Papers. It brings together source PDFs, extracted paper content, textual and visual clues, query collections, and split definitions. The generation code documents how these artifacts are made.
The PDFs form a shared retrieval corpus. Query and evaluation collections refer to paper identifiers in that corpus; the… See the full description on the dataset page: https://huggingface.co/datasets/kasys/multimodal-tip-of-the-tongue-retrieval-for-scientific-documents.quantized-retrieval-datarubq-retrievalretrieval-datasets-similarities
Summary
Caching the similarity results of different embedding-based retrieval, on different dataset; that is, the similarities between each query
and all the corpus passages.
Method. We collect these results in JSON files, containing the similarities similarities that
were collected running evaluation with (BEIR), on the specific model and data. Full list below.
Usage. This caching can be used to evaluate the benign accuracy of the models, and---more importantly---to explore the… See the full description on the dataset page: https://huggingface.co/datasets/MatanBT/retrieval-datasets-similarities.airbnb-synthetic-retrieval_beirThis is a copy of https://huggingface.co/datasets/jinaai/airbnb-synthetic-retrieval reformatted into the BEIR format. For any further information like license, please refer to the original dataset.
Disclaimer
This dataset may contain publicly available images or text data. All data is provided for research and educational purposes only. If you are the rights holder of any content and have concerns regarding intellectual property or copyright, please contact us at "support-data (at)… See the full description on the dataset page: https://huggingface.co/datasets/jinaai/airbnb-synthetic-retrieval_beir.fineweb-edu-embThis file contains the embeddings for the full fineweb-edu dataset.
The dataset has been deduplicated (using only exact deduplication).
The emb folder contains for each parquet file a new_{parquet_name}.npy and old_{parquet}.npy file. The old refers to text that has been seen in the smaller 10B/100B/350B data samples.
Cohere embed-multilingual-v3.0 model has been used.
The index of the dataset can be found here:
https://huggingface.co/datasets/Cohere/fineweb-edu-index
The corpus can be found… See the full description on the dataset page: https://huggingface.co/datasets/fineweb-retrieval/fineweb-edu-emb.mteb-retrieval-snowflake-arctic-embed-m-v1.5