datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cqadupstack-programmers
CQADupstackProgrammersRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
Task category
t2t
Domains
Programming, Written, Non-fiction
Referencehttp://nlp.cis.unimelb.edu.au/resources/cqadupstack/
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/cqadupstack-programmers.road-issues-detection-dataset
Road Issues Detection Dataset
Dataset Summary
This comprehensive dataset contains 9,660 high-resolution RGB images categorized for road infrastructure issues detection. The dataset focuses on identifying critical urban infrastructure problems including potholes, damaged roads, broken road signs, illegal parking violations, and environmental cleanliness issues. It has been specifically organized and curated for computer vision and machine learning applications in smart… See the full description on the dataset page: https://huggingface.co/datasets/Programmer-RD-AI/road-issues-detection-dataset.MVTecADsmolified-tiny-text-to-code
🤏 smolified-tiny-text-to-code
Intelligence, Distilled.
This is a synthetic training corpus generated by the Smolify Foundry.
It was used to train the corresponding model programmerGodbyte/smolified-tiny-text-to-code.
📦 Asset Details
Origin: Smolify Foundry (Job ID: fe9b19bf)
Records: 1078
Type: Synthetic Instruction Tuning Data
⚖️ License & Ownership
This dataset is a sovereign asset owned by programmerGodbyte.
Generated via Smolify.ai.
cqadupstack-programmers-vn
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(["CQADupstackProgrammers-VN"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repitory.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing… See the full description on the dataset page: https://huggingface.co/datasets/GreenNode/cqadupstack-programmers-vn.genz-slang-pairs-1k
Gen Z Slang Pairs Corpus (1 K)
The Gen Z Slang Pairs Corpus (1 K) contains 1,000 everyday English sentences alongside their Gen Z–style slang rewrites. This dataset is designed for style-transfer, informal-language generation, and paraphrasing research. Use it to train models that transform formal or neutral sentences into expressive, youth‑oriented slang.
Dataset Details
This dataset was generated programmatically using OpenAI GPT-4.1 Nano.
Language: English… See the full description on the dataset page: https://huggingface.co/datasets/Programmer-RD-AI/genz-slang-pairs-1k.cqadupstack-programmers-vn-rawCQADupstack-Programmers-PL
CQADupstack-Programmers-PL
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Stack Exchange Question Duplicate Pairs Dataset
Task category
t2t
Domains
Programming, Written, Non-fiction
Reference
https://huggingface.co/datasets/clarin-knext/cqadupstack-programmers-pl
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(["CQADupstack-Programmers-PL"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/CQADupstack-Programmers-PL.world-seaports-and-airports
World Seaports & Airports
Two public-domain reference datasets covering the world's transport infrastructure.
ports.csv — 3,804 seaports across 195 countries: UN/LOCODE, coordinates,
channel/anchorage/cargo-pier depths (m), max vessel length/beam/draft, harbor type & size,
shelter, pilotage and tug availability. Source: U.S. NGA World Port Index (Pub 150).
airports.csv — 9,640 airports across 237 countries: IATA & ICAO codes, runway count and
longest-runway length (ft/m)… See the full description on the dataset page: https://huggingface.co/datasets/programmer47/world-seaports-and-airports.cqadupstack-programmers-top-20-gen-queries
NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
id (str): unique document id in NFCorpus in the BEIR benchmark (corpus.jsonl).
Questions generated: 20
Code used for generation: evaluate_anserini_docT5query_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR… See the full description on the dataset page: https://huggingface.co/datasets/income/cqadupstack-programmers-top-20-gen-queries.sfia-9-scraped
SFIA-9-Scraped Dataset
This repository contains the SFIA-9-Scraped dataset, a JSON collection of the Skills Framework for the Information Age (SFIA) version 9 categories and levels, scraped for non-commercial research use.
🚀 Dataset Overview
Name: SFIA-9-Scraped
Hugging Face: Programmer-RD-AI/sfia-9-scraped
DOI: 10.57967/hf/5746
Author: Ranuga Disansa Gamage
Revision: 89feeb8
Publisher: Hugging Face
Year: 2025
Use this dataset to build RAG systems, taxonomy-driven… See the full description on the dataset page: https://huggingface.co/datasets/Programmer-RD-AI/sfia-9-scraped.beir-cqadupstack-programmers
CQADupstackProgrammersRetrieval — BEIR, unified schema
A normalised copy of the dataset behind the mteb task CQADupstackProgrammersRetrieval, one of the tasks of the BEIR benchmark as mteb defines it (a member of the aggregate task CQADupstackRetrieval). Same queries, documents
and relevance judgements as the benchmark evaluates — reshaped into one strict schema shared by every dataset
in this collection.
Source
mteb/cqadupstack-programmers @ 6184bc1440d2 (the… See the full description on the dataset page: https://huggingface.co/datasets/Hyukkyu/beir-cqadupstack-programmers.sinhala-english-singlish-translation
Sinhala–English–Singlish Translation Dataset
A parallel corpus of Sinhala sentences, their English translations, and romanized Sinhala (“Singlish”) transliterations.
📋 Table of Contents
Dataset Overview
Installation
Quick Start
Dataset Structure
Usage Examples
Citation
License
Credits
Dataset Overview
Description: 34,500 aligned triplets of
Sinhala (native script)
English (human translation)
Singlish (romanized Sinhala)… See the full description on the dataset page: https://huggingface.co/datasets/Programmer-RD-AI/sinhala-english-singlish-translation.cqadupstack-programmers-fa
Dataset Summary
CQADupstack-programmers-Fa is a Persian (Farsi) dataset developed for the Retrieval task, with a focus on duplicate question detection in community question-answering (CQA) platforms. This dataset is a translated version of the "Programmers" (Software Engineering) StackExchange subforum from the English CQADupstack collection and is part of the FaMTEB benchmark under the BEIR-Fa suite.
Language(s): Persian (Farsi)
Task(s): Retrieval (Duplicate Question Retrieval)… See the full description on the dataset page: https://huggingface.co/datasets/MCINext/cqadupstack-programmers-fa.beir_cqadupstack_programmers
CQADupStack / Programmers (BEIR) — programming Q&A retrieval
Dataset description
CQADupStack is a benchmark for community question answering (cQA) built from Stack Exchange data. It was introduced by Hoogeveen, Verspoor, and Baldwin at ADCS 2015 to support research on duplicate questions: finding earlier posts that match or subsume a newly asked question, so users can reuse existing answers instead of opening redundant threads.
The full CQADupStack release aggregates… See the full description on the dataset page: https://huggingface.co/datasets/orgrctera/beir_cqadupstack_programmers.cqadupstack-programmers-qrels
Dataset Card for "cqadupstack-programmers-qrels"
More Information needed
aurora_programmer_data
My Awesome Dataset
A comprehensive description of my awesome dataset.
Dataset Description
This dataset contains images of cats and dogs. The images were collected from [mention data source(s), e.g., a specific website, scraped from the internet]. It is intended for use in image classification tasks. The dataset consists of [number] images, with approximately [percentage]% allocated to the training set and [percentage]% to the test set. [Add more details about the… See the full description on the dataset page: https://huggingface.co/datasets/naimulislam/aurora_programmer_data.beir_cqadupstack_programmers_test
beir_cqadupstack_programmers_test
BEIR CQADupStack/programmers test split
Field
Value
Benchmark
beir
Sub-benchmark
cqadupstack_programmers
Type
retrieval
Items
876
Exported from Langfuse.
cqadubstack-programmers-qrels
Dataset Card for "cqadubstack-programmers-qrels"
More Information needed
cqudubstack-programmers
Dataset Card for "cqudubstack-programmers"
More Information needed
customer-feedback-action-plans
Customer Feedback → Action Plans
A small, practical dataset that maps raw customer feedback (e.g., restaurant reviews) to actionable recommendations with optional aspect annotations and reasoning. Useful for training instruction-following models, aspect-aware summarizers, or classification heads that support the generation task.
Files & Splits
train.csv — main training split for generation.
validation.csv — validation split for generation.
train_aux_classification.csv —… See the full description on the dataset page: https://huggingface.co/datasets/Programmer-RD-AI/customer-feedback-action-plans.programmerhumor
Dataset Card for ProgrammerHumor.io Memes
Dataset Summary
This dataset contains programming-related memes and humor content collected from programmerhumor.io, along with associated metadata such as titles, categories, tags, and image captions.
Languages
The dataset is monolingual:
English (en): All meme content and descriptions are primarily in English
Dataset Structure
Data Files
The dataset consists of:
Image files… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/programmerhumor.cqudupstack-programmers
Dataset Card for "cqudupstack-programmers"
More Information needed
national-greenhouse-accounts-factors-2024
National Greenhouse Accounts Factors 2024 (Australia)
This dataset contains Australian greenhouse gas emission factors for electricity and gas consumption, structured in JSON format for easy programmatic access. The data is derived from the official Australian Government publication.
Dataset Description
The dataset includes emission factors used to calculate greenhouse gas emissions from:
Electricity consumption by state/territory (Scope 2 and Scope 3)
Gas consumption by… See the full description on the dataset page: https://huggingface.co/datasets/Programmer-RD-AI/national-greenhouse-accounts-factors-2024.Qwen3.6-35B-A3B-oQ8-mtp_mtp-on-intelligence-resultscqadupstack-programmers
Dataset Card for "cqadupstack-programmers"
More Information needed
Qwen3.6-35B-A3B-oQ8-mtp_mtp-off-intelligence-resultscqadupstack-programmers-plPart of BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: konrad.wojtasik@pwr.edu.pl
MNLP_M3_mcqa_datasetsmolified-code-helper-model
🤏 smolified-code-helper-model
Intelligence, Distilled.
This is a synthetic training corpus generated by the Smolify Foundry.
It was used to train the corresponding model programmerGodbyte/smolified-code-helper-model.
📦 Asset Details
Origin: Smolify Foundry (Job ID: aa61ab1e)
Records: 33
Type: Synthetic Instruction Tuning Data
⚖️ License & Ownership
This dataset is a sovereign asset owned by programmerGodbyte.
Generated via Smolify.ai.