programmer
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.MVTecADgenz-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
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.CQADupstack-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.