datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cqadupstack-unix
CQADupstackUnixRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
Task category
t2t
Domains
Written, Web, Programming
Reference
http://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 = mteb.get_tasks(["CQADupstackUnixRetrieval"])
evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-unix.terminal_bench_2_tasktrove_dq_unix_step10_30b_a3b_20260730_014756
terminal_bench_2_tasktrove_dq_unix_step10_30b_a3b
OpenCode agent trajectories from the TaskTrove DQ unix arm of a Qwen3-Coder-30B-A3B
agentic RL sweep, exported from the complete Harbor rollout artifact set.
Coverage
Built from the full trace_jobs prefix of run rl-tasktrove-dq-sweep-30b-qwen3-coder-30-20260727-082204-e42f1d
(12034 trial directories, 11937 of them scored).
quantity
value
scored trials (result.json)
11937
rows published
11937
coverage… See the full description on the dataset page: https://huggingface.co/datasets/laion/terminal_bench_2_tasktrove_dq_unix_step10_30b_a3b_20260730_014756.hacking
Hacking Text Corpus
A research corpus of historical computer security writings, hacker zines, and hacktivist texts. Built for NLP, text generation, discourse analysis, and security research.
all.txt — all files for training language models
This is the all files for training language models. all.txt is a single concatenated file containing all of the corpus content (every Phrack article, the Phineas Fisher writeups, unfz.txt, and thc.md) in one plain-text document… See the full description on the dataset page: https://huggingface.co/datasets/unixtcpip/hacking.unix-commands
Unix Commands Dataset
Description
The Unix Commands Dataset is a unique collection of real-world Unix command line examples, captured from various system prompts representing different user roles and responsibilities, such as system administrators, DevOps, network administrators, Docker administrators, regular users, and hackers.
The dataset consists of Unix commands ranging from basic to advanced levels and from a wide array of categories, including file operations (ls… See the full description on the dataset page: https://huggingface.co/datasets/harpomaxx/unix-commands.stackexchange-unix-sandboxesbeir-cqadupstack-unix
CQADupstackUnixRetrieval — BEIR, unified schema
A normalised copy of the dataset behind the mteb task CQADupstackUnixRetrieval, 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-unix @ 6c6430d3a6d3 (the revision pinned in mteb)… See the full description on the dataset page: https://huggingface.co/datasets/Hyukkyu/beir-cqadupstack-unix.cqadupstack-unix-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(["CQADupstackUnix-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 as a… See the full description on the dataset page: https://huggingface.co/datasets/GreenNode/cqadupstack-unix-vn.stackexchange-unix-sandboxes_glm_4.7_traces_jupiterterminal_bench_2_a1_stackexchange_unix_20260809_015737stackexchange-unix-sandboxes-traces-terminus-2CQADupstack-Unix-PL
CQADupstack-Unix-PL
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Stack Exchange Question Duplicate Pairs Dataset
Task category
t2t
Domains
Written, Web, Programming
Reference
https://huggingface.co/datasets/clarin-knext/cqadupstack-unix-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-Unix-PL"])
evaluator = mteb.MTEB(task)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/CQADupstack-Unix-PL.beir_cqadupstack_unix
CQADupStack / Unix (BEIR) — Unix & Linux 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 twelve… See the full description on the dataset page: https://huggingface.co/datasets/orgrctera/beir_cqadupstack_unix.terminal_bench_2_a1_stackexchange_unix_20260328_072240cqadupstack-unix
Dataset Card for "cqadupstack-unix"
More Information needed
cqadupstack-unix-fa
Dataset Summary
CQADupstack-unix-Fa is a Persian (Farsi) dataset designed for the Retrieval task, with a focus on duplicate question retrieval. It is a translated version of the "unix" (Unix & Linux Stack Exchange) subforum from the original English CQADupstack dataset, used in the BEIR benchmark, and is part of the FaMTEB (Farsi Massive Text Embedding Benchmark) under the BEIR-Fa collection.
Language(s): Persian (Farsi)
Task(s): Retrieval (Duplicate Question Retrieval)
Source:… See the full description on the dataset page: https://huggingface.co/datasets/MCINext/cqadupstack-unix-fa.cqadupstack-unix-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-unix-top-20-gen-queries.swebench_verified_random_100_folders_a1_stackexchange_unix_20260819_162244cqudubstack-unix
Dataset Card for "cqudubstack-unix"
More Information needed
dev_set_v2_a1_stackexchange_unix_20260813_104843test-dataset-context-aware-fim-autocomplete-oracle-unixcoder_cosine_simswebench_verified_random_100_folders_a1_stackexchange_unix_20260325_214708dev_set_v2_a1_stackexchange_unix_20260325_213949testgen_unixcoderbeir_cqadupstack_unix_test
beir_cqadupstack_unix_test
BEIR CQADupStack/unix test split
Field
Value
Benchmark
beir
Sub-benchmark
cqadupstack_unix
Type
retrieval
Items
1072
Exported from Langfuse.
unix_linux_cleansocial-media-postcqadubstack-unix-qrels
Dataset Card for "cqadubstack-unix-qrels"
More Information needed
cqadupstack-unix-qrels
Dataset Card for "cqadupstack-unix-qrels"
More Information needed
stackexchange-unix-sandboxes-skywork-responseunixlogfiles