| --- |
| language: |
| - en |
| license: cc-by-sa-4.0 |
| tags: |
| - retrieval |
| - text-retrieval |
| - beir |
| - programming |
| - stack-exchange |
| - duplicate-question-detection |
| - community-question-answering |
| - benchmark |
| pretty_name: BEIR CQADupStack — Programmers (retrieval) |
| size_categories: "10K<n<100K" |
| task_categories: |
| - text-retrieval |
| --- |
| |
| # CQADupStack / Programmers (BEIR) — programming Q&A retrieval |
|
|
| ## Dataset description |
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| **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. |
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| The full CQADupStack release aggregates **twelve** Stack Exchange subcommunities (subforums). Each subforum is distributed as its own slice with **pre-defined splits** and **duplicate annotations** (which posts are duplicates of which), enabling comparable retrieval and classification experiments. The **Programmers** slice corresponds to the historical **Programmers.StackExchange** community (software engineering and professional programming topics, distinct from “pure code” Q&A). |
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| **BEIR** (*Benchmarking IR*) repackaged CQADupStack — including the **Programmers** sub-benchmark — as part of a **heterogeneous zero-shot IR benchmark** spanning many tasks and domains. In BEIR’s retrieval setting, each slice is a standard **corpus + queries + qrels** collection: systems must rank corpus documents so that human-annotated duplicates appear at the top. |
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| This repository (`orgrctera/beir_cqadupstack_programmers`) exposes the **BEIR CQADupStack / Programmers** **test** split in **Parquet** form for retrieval evaluation pipelines. Each row is one **query** with **relevance judgments** (`qrels`) pointing at corpus document identifiers, aligned with the BEIR release. |
|
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| ### Scale (BEIR / Programmers retrieval setting) |
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| For the **Programmers** task as used in embedding benchmarks (e.g. MTEB’s “CQADupstackProgrammersRetrieval”), the **test** split is on the order of: |
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| - **~32k** unique **documents** in the corpus (Stack Exchange question posts). |
| - **876** **queries** in the official test set. |
| - **~1.9** relevant documents per query on average (some queries have many duplicates annotated). |
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|
| Exact counts depend on the upstream BEIR snapshot; see [BEIR on GitHub](https://github.com/beir-cellar/beir) for version-precise figures. |
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|
| ## Task: retrieval (CQADupStack Programmers) |
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| The task is **ad hoc passage (or document) retrieval** for **duplicate question finding**: |
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| 1. **Input:** a natural-language **question** (the query) posted on the Programmers forum. |
| 2. **Output:** a ranked list of **document IDs** from the Programmers corpus (or scores over the full collection), such that **relevant** IDs — posts marked as duplicates of the query in the official qrels — receive high rank. |
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| Evaluation uses standard IR metrics (e.g. **nDCG@k**, **Recall@k**, **MRR**), as in BEIR’s evaluation utilities or frameworks such as Pyserini / MTEB. |
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| > **Note:** Full retrieval evaluation also requires the **corpus** (passage text keyed by ID). This dataset card describes the **query + qrels** side as prepared for CTERA-style evaluation rows; align corpus IDs with the same **BEIR CQADupStack / Programmers** corpus you use for indexing. |
|
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| ## Data format (this repository) |
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| Each record includes: |
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| | Field | Description | |
| |--------|-------------| |
| | `id` | UUID for this example row. | |
| | `input` | The **query text** (question). | |
| | `expected_output` | JSON string: list of objects `{"id": "<corpus-doc-id>", "score": <relevance>}`. Scores follow the BEIR qrels convention (typically `1` for relevant in binary settings). | |
| | `metadata.query_id` | Original BEIR / CQADupStack query identifier (string). | |
| | `metadata.split` | Split name (here: `test`). | |
|
|
| ### Example 1 — single annotated duplicate |
|
|
| ```json |
| { |
| "id": "4fc8cccf-f1d8-4685-adcc-6506b470d0c1", |
| "input": "How do I write a specification?", |
| "expected_output": "[{\"id\": \"34356\", \"score\": 1}]", |
| "metadata.query_id": "132074", |
| "metadata.split": "test" |
| } |
| ``` |
|
|
| ### Example 2 — multiple duplicate targets |
|
|
| ```json |
| { |
| "id": "0e2e4cb0-3d07-4208-94bb-bc20761f4bb6", |
| "input": "Is this a violation of the Liskov Substitution Principle?", |
| "expected_output": "[{\"id\": \"224350\", \"score\": 1}, {\"id\": \"254398\", \"score\": 1}, {\"id\": \"229549\", \"score\": 1}, {\"id\": \"189222\", \"score\": 1}, {\"id\": \"177831\", \"score\": 1}, {\"id\": \"145941\", \"score\": 1}, {\"id\": \"82682\", \"score\": 1}, {\"id\": \"237843\", \"score\": 1}, {\"id\": \"132612\", \"score\": 1}, {\"id\": \"107723\", \"score\": 1}, {\"id\": \"231300\", \"score\": 1}]", |
| "metadata.query_id": "170138", |
| "metadata.split": "test" |
| } |
| ``` |
|
|
| ## References |
|
|
| ### CQADupStack (original dataset) |
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|
| **Doris Hoogeveen, Karin M. Verspoor, Timothy Baldwin** |
| *CQADupStack: A Benchmark Data Set for Community Question-Answering Research* |
| Proceedings of the 20th Australasian Document Computing Symposium (ADCS 2015), pages 3:1–3:8. |
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| **Abstract (summary):** The paper presents CQADupStack, a resource derived from Stack Exchange with **duplicate question annotations** across multiple communities, together with standard splits and evaluation tooling for **retrieval** and **classification** experiments on duplicate detection in cQA. |
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| - Paper: [ACM DL 10.1145/2838931.2838934](https://doi.org/10.1145/2838931.2838934) — IR Anthology entry: [hoogeveen-2015-cqadupstack](https://ir.webis.de/anthology/2015.adcs_conference-2015.3/). |
| - Original data landing page (University of Melbourne NLP): [CQADupStack resources](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/). |
|
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| ### BEIR benchmark (CQADupStack as a subset) |
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| **Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych** |
| *BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models* |
| NeurIPS 2021 (Datasets and Benchmarks Track). |
|
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| **Abstract (from arXiv):** *“Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval. We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark. Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs. In contrast, dense and sparse-retrieval models are computationally more efficient but often underperform other approaches, highlighting the considerable room for improvement in their generalization capabilities.”* |
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| - Paper: [arXiv:2104.08663](https://arxiv.org/abs/2104.08663) — [OpenReview](https://openreview.net/forum?id=wCu6T5xFjeJ); code and data: [BEIR on GitHub](https://github.com/beir-cellar/beir). |
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| ### Related resources |
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| - **MTEB** packages the same retrieval task as [`mteb/cqadupstack-programmers`](https://huggingface.co/datasets/mteb/cqadupstack-programmers) (corpus + queries + qrels) with descriptive statistics. |
| - **BEIR-style mirrors** on Hugging Face for raw JSONL / TSV layouts (e.g. `BeIR/cqadupstack-*` datasets where applicable) — see the upstream BEIR project for the canonical file layout. |
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| ## Citation |
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| If you use **CQADupStack**, cite the ADCS 2015 paper. If you use the **BEIR** benchmark packaging, cite the BEIR NeurIPS 2021 paper. BibTeX for CQADupStack is available from the [IR Anthology](https://ir.webis.de/anthology/2015.adcs_conference-2015.3/) (BibTeX download on that page). |
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| ## License |
|
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| Stack Exchange content is typically shared under **Creative Commons** terms. This card marks **`cc-by-sa-4.0`** as a common license for Stack Exchange–derived text; verify against your corpus snapshot and upstream terms if compliance is strict. |
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| --- |
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| *Dataset card maintained for the `orgrctera/beir_cqadupstack_programmers` Hub repository.* |
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