QuangDuy/supervised_data-hn-percpos

HUIT-Embedding — TopK-PercPos hard negatives (Vietnamese)

Hard negatives mined for QuangDuy/supervised_data with the method of NV-Retriever and F2LLM. 67 configs. Generated on 2026-10-25. Candidates come from QuangDuy/supervised_data-hn-candidates.

Method

Positive-aware filtering: from the retrieval candidates (hardest first) keep those with score s⁻ < p · s⁺, where s⁺ is the score of the positive, and take the 10 hardest. Queries left with fewer than 10 negatives are dropped. Configs -p090, -p095, -p099 sweep p; -f2llm follows F2LLM (p = 0.95, additionally s⁻ < 0.8, and the top 5 candidates are skipped).

• Retriever: intfloat/multilingual-e5-large (paper), exact search over a per-domain corpus (law, NLI, reading comprehension, health, fact-check, tables) built from the positives and provided negatives of the source data, with evaluation overlaps removed.
• Teacher: BAAI/bge-reranker-v2-m3 (paper).
• Scores in negative_scores: retriever cosine (teacher probability for mmarco_vi-*).

Differences from the paper

• Retriever is intfloat/multilingual-e5-large (NV-Retriever uses e5-mistral; F2LLM uses Qwen3-Embedding-0.6B).
• F2LLM keeps 24 negatives; here 10, as in the other configs.
• mmarco_vi-* configs filter on the bge-reranker-v2-m3 scores of hotchpotch/mmarco-hard-negatives-reranker-score (64 BM25 negatives per query) instead of retriever scores. Those scores were computed on the unicamp-dl/mmarco translation while the texts here are GreenNode/msmarco-vn, so they are approximate.
• Cosine scores of e5 are compressed (most candidates score 0.75–0.9), so p = 0.90 keeps few queries.

Two layouts

• <config>: texts included (query, positive, negatives). Ready to train.
• <config>-ids: ids and scores only; texts are in documents-<group> (doc_id, text). Much smaller.

Usage

from datasets import load_dataset

ds = load_dataset("QuangDuy/supervised_data-hn-percpos", "csconda-f2llm", split="train")

# ids layout: join negatives with the documents table
ids = load_dataset("QuangDuy/supervised_data-hn-percpos", "csconda-f2llm-ids", split="train")
docs = load_dataset("QuangDuy/supervised_data-hn-percpos", "documents-health", split="train")
text = dict(zip(docs["doc_id"], docs["text"]))
row = ids[0]
negatives = [text[d] for d in row["negative_ids"]]

Training: sample 7 of the negatives per step (as in LightOn and NV-Retriever) and keep rows with the same group_id out of one batch.

Configs

Config	Source	Queries in	Queries out	Kept	Negatives / query
csconda-f2llm	csconda	19	1	5.3%	10.0
csconda-p095	csconda	19	10	52.6%	10.0
csconda-p099	csconda	19	16	84.2%	10.0
isedsc2024-f2llm	isedsc2024	7,944	2,204	27.7%	10.0
isedsc2024-p090	isedsc2024	7,944	5,298	66.7%	10.0
isedsc2024-p095	isedsc2024	7,944	7,424	93.5%	10.0
isedsc2024-p099	isedsc2024	7,944	7,847	98.8%	10.0
large_vi_legal_queries-f2llm	large_vi_legal_queries	494,312	275	0.1%	10.0
large_vi_legal_queries-p090	large_vi_legal_queries	494,312	710	0.1%	10.0
large_vi_legal_queries-p095	large_vi_legal_queries	494,312	173,025	35.0%	10.0
large_vi_legal_queries-p099	large_vi_legal_queries	494,312	449,418	90.9%	10.0
mmarco_vi-f2llm	mmarco_vi	480,133	456,082	95.0%	10.0
mmarco_vi-p090	mmarco_vi	480,133	461,994	96.2%	10.0
mmarco_vi-p095	mmarco_vi	480,133	467,169	97.3%	10.0
mmarco_vi-p099	mmarco_vi	480,133	472,283	98.4%	10.0
multi_wiki_qa-f2llm	multi_wiki_qa	4,932	4,640	94.1%	10.0
multi_wiki_qa-p090	multi_wiki_qa	4,932	3,610	73.2%	10.0
multi_wiki_qa-p095	multi_wiki_qa	4,932	4,643	94.1%	10.0
multi_wiki_qa-p099	multi_wiki_qa	4,932	4,879	98.9%	10.0
table_markdown_retrieval-f2llm	table_markdown_retrieval	125,830	15,322	12.2%	10.0
table_markdown_retrieval-p090	table_markdown_retrieval	125,830	4,524	3.6%	10.0
table_markdown_retrieval-p095	table_markdown_retrieval	125,830	38,531	30.6%	10.0
table_markdown_retrieval-p099	table_markdown_retrieval	125,830	89,468	71.1%	10.0
vianli-f2llm	vianli	1,606	55	3.4%	10.0
vianli-p090	vianli	1,606	122	7.6%	10.0
vianli-p095	vianli	1,606	928	57.8%	10.0
vianli-p099	vianli	1,606	1,487	92.6%	10.0
vicov19qa-f2llm	vicov19qa	2,911	72	2.5%	10.0
vicov19qa-p090	vicov19qa	2,911	146	5.0%	10.0
vicov19qa-p095	vicov19qa	2,911	1,032	35.5%	10.0
vicov19qa-p099	vicov19qa	2,911	2,322	79.8%	10.0
vietnamese_law_hn_mined-f2llm	vietnamese_law_hn_mined	327,190	1,140	0.3%	10.0
vietnamese_law_hn_mined-p090	vietnamese_law_hn_mined	327,190	4,003	1.2%	10.0
vietnamese_law_hn_mined-p095	vietnamese_law_hn_mined	327,190	151,311	46.2%	10.0
vietnamese_law_hn_mined-p099	vietnamese_law_hn_mined	327,190	308,901	94.4%	10.0
vifactcheck-f2llm	vifactcheck	1,595	708	44.4%	10.0
vifactcheck-p090	vifactcheck	1,595	1,325	83.1%	10.0
vifactcheck-p095	vifactcheck	1,595	1,567	98.2%	10.0
vifactcheck-p099	vifactcheck	1,595	1,592	99.8%	10.0
vihealthqa-f2llm	vihealthqa	6,582	16	0.2%	10.0
vihealthqa-p090	vihealthqa	6,582	307	4.7%	10.0
vihealthqa-p095	vihealthqa	6,582	3,072	46.7%	10.0
vihealthqa-p099	vihealthqa	6,582	5,430	82.5%	10.0
vimedaqa-f2llm	vimedaqa	31,437	3,459	11.0%	10.0
vimedaqa-p090	vimedaqa	31,437	5,859	18.6%	10.0
vimedaqa-p095	vimedaqa	31,437	23,037	73.3%	10.0
vimedaqa-p099	vimedaqa	31,437	29,420	93.6%	10.0
vimqa-f2llm	vimqa	7,158	7,098	99.2%	10.0
vimqa-p090	vimqa	7,158	5,493	76.7%	10.0
vimqa-p095	vimqa	7,158	7,100	99.2%	10.0
vimqa-p099	vimqa	7,158	7,157	100.0%	10.0
vinli-f2llm	vinli	2,107	53	2.5%	10.0
vinli-p090	vinli	2,107	568	27.0%	10.0
vinli-p095	vinli	2,107	1,897	90.0%	10.0
vinli-p099	vinli	2,107	2,097	99.5%	10.0
vinli_simcse_v2-f2llm	vinli_simcse_v2	268,871	462	0.2%	10.0
vinli_simcse_v2-p090	vinli_simcse_v2	268,871	27,296	10.2%	10.0
vinli_simcse_v2-p095	vinli_simcse_v2	268,871	112,588	41.9%	10.0
vinli_simcse_v2-p099	vinli_simcse_v2	268,871	198,107	73.7%	10.0
virhe4qa-f2llm	virhe4qa	75	71	94.7%	10.0
virhe4qa-p090	virhe4qa	75	37	49.3%	10.0
virhe4qa-p095	virhe4qa	75	71	94.7%	10.0
virhe4qa-p099	virhe4qa	75	75	100.0%	10.0
viwikifc-f2llm	viwikifc	1,802	1,307	72.5%	10.0
viwikifc-p090	viwikifc	1,802	1,447	80.3%	10.0
viwikifc-p095	viwikifc	1,802	1,779	98.7%	10.0
viwikifc-p099	viwikifc	1,802	1,800	99.9%	10.0

Sources without corpus hard negatives (context-dependent questions: top candidates usually answer them too): vire4mrc, vlogqa. Use them with in-batch negatives from the base dataset.

Sources and licenses

Source	Origin	License	Label origin	Translated
csconda	ura-hcmut/Vietnamese-Customer-Support-QA	cc-by-nc-4.0	human_paper	False
isedsc2024	MiuN2k3/isedsc.train.dataset	unknown	competition	False
large_vi_legal_queries	phamson02/large-vi-legal-queries	cc-by-4.0	synthetic	False
mmarco_vi	unicamp-dl/mmarco, GreenNode/msmarco-vn	apache-2.0 (repo); passages from GreenNode/msmarco-vn cc-by-sa-4.0; MS MARCO terms (non-commercial)	translated	True
multi_wiki_qa	alexandrainst/multi-wiki-qa (config vi)	cc-by-nc-sa-4.0	synthetic	False
table_markdown_retrieval	GreenNode/GreenNode-Table-Markdown-Retrieval-VN	mit	synthetic	False
vianli	uitnlp/ViANLI	mit	human_paper	False
vicov19qa	https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/4_ans/UIT-ViCoV19QA_train.csv	unknown (README: research purposes only)	human_paper	False
vietnamese_law_hn_mined	GreenNode/Vietnamese-Law-hn-mined-v2	unknown	competition	False
vifactcheck	tranthaihoa/vifactcheck	mit	human_paper	False
vihealthqa	tarudesu/ViHealthQA	unknown	human_paper	False
vimedaqa	tmnam20/ViMedAQA	unknown	human_paper	False
vimqa	nguyenlab/vimqa	cc-by-nc-sa-4.0	human_paper	False
vinli	presencesw/vinli_4_label	unknown	human_paper	False
vinli_simcse_v2	anti-ai/ViNLI-SimCSE-supervised_v2	mit	translated	True
virhe4qa	TaiDoan123/UIT-R2GQA (config triplet)	unknown	human_paper	False
viwikifc	High-Will/ViWikiFC	cc-by-4.0	human_paper	False

Citations

Please cite the method paper(s) first.

@misc{moreira2025nvretrieverimprovingtextembedding,
      title={NV-Retriever: Improving text embedding models with effective hard-negative mining},
      author={Gabriel de Souza P. Moreira and Radek Osmulski and Mengyao Xu and Ronay Ak and Benedikt Schifferer and Even Oldridge},
      year={2025},
      eprint={2407.15831},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2407.15831},
}

@misc{zhang2025f2llmtechnicalreportmatching,
      title={F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data},
      author={Ziyin Zhang and Zihan Liao and Hang Yu and Peng Di and Rui Wang},
      year={2025},
      eprint={2510.02294},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.02294},
}

@misc{wang2024multilinguale5textembeddings,
      title={Multilingual E5 Text Embeddings: A Technical Report},
      author={Liang Wang and Nan Yang and Xiaolong Huang and Linjun Yang and Rangan Majumder and Furu Wei},
      year={2024},
      eprint={2402.05672},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2402.05672},
}

@misc{chen2025m3embeddingmultilingualitymultifunctionalitymultigranularity,
      title={M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
      author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
      year={2025},
      eprint={2402.03216},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2402.03216},
}

@inproceedings{csconda,
  title     = {A Benchmark Dataset and Evaluation Framework for Vietnamese Large Language Models in Customer Support},
  author    = {Nguyen, Long S. T. and Hua, Truong P. and Nguyen, Thanh M. and Pham, Toan Q. and Ngo, Nam K. and Nguyen, An X. and Pham, Nghi D. M. and Nguyen, Nghia H. and Quan, Tho T.},
  editor    = {Nguyen, Ngoc Thanh and Dinh Duc Anh, Vu and Kozierkiewicz, Adrianna and Nguyen Van, Sinh and Nunez, Manuel and Treur, Jan and Vossen, Gottfried},
  booktitle = {Advances in Computational Collective Intelligence},
  year      = {2026},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  pages     = {481--496},
  isbn      = {978-3-032-10202-7}
}

@inproceedings{vire,
    title = "Which Works Best for {V}ietnamese? {A} Practical Study of Information Retrieval Methods across Domains",
    author = "Nguyen, Long S. T.  and
      Quan, Tho T.",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-eacl.110/",
    doi = "10.18653/v1/2026.findings-eacl.110",
    pages = "2098--2119",
    ISBN = "979-8-89176-386-9"
}

@InProceedings{large-vi-legal-queries,
author="Son, Pham Tien and Hieu, Nguyen Doan and An, Nguyen Dai and Sang, Dinh Viet",
editor="Buntine, Wray and Fjeld, Morten and Tran, Truyen and Tran, Minh-Triet and Huynh Thi Thanh, Binh and Miyoshi, Takumi",
title="Improving Vietnamese Legal Document Retrieval Using Synthetic Data",
booktitle="Information and Communication Technology",
year="2025",
publisher="Springer Nature Singapore",
address="Singapore",
pages="378--393",
isbn="978-981-96-4291-5"
}

@misc{bonifacio2022mmarcomultilingualversionms,
      title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset}, 
      author={Luiz Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and Roberto Lotufo and Rodrigo Nogueira},
      year={2022},
      eprint={2108.13897},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2108.13897}, 
}

@misc{bajaj2018msmarcohumangenerated,
      title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, 
      author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song and Alina Stoica and Saurabh Tiwary and Tong Wang},
      year={2018},
      eprint={1611.09268},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1611.09268}, 
}

@misc{smart2026multiwikiqareadingcomprehensionbenchmark,
      title={MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages}, 
      author={Dan Saattrup Smart},
      year={2026},
      eprint={2509.04111},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.04111}, 
}

@inproceedings{gn-trvn,
  abstract = {Information retrieval often comes in plain text, lacking semi-structured text such as HTML and markdown, retrieving data that contains rich format such as table became non-trivial. In this paper, we tackle this challenge by introducing a new dataset, GreenNode Table Retrieval VN (GN-TRVN), which is collected from a massive corpus, a wide range of topics, and a longer context compared to ViQuAD2.0. To evaluate the effectiveness of our proposed dataset, we introduce two versions, M3-GN-VN and M3-GN-VN-Mixed, by fine-tuning the M3-Embedding model on this dataset. Experimental results show that our models consistently outperform the baselines, including the base model, across most evaluation criteria on various datasets such as VieQuADRetrieval, ZacLegalTextRetrieval, and GN-TRVN. In general, we release a more comprehensive dataset and two model versions that improve response performance for Vietnamese Markdown Table Retrieval.},
  address = {Singapore},
  author = {Pham, Bao Loc
and Hoang, Quoc Viet
and Luu, Quy Tung
and Vo, Trong Thu},
  booktitle = {Proceedings of the Fifth International Conference on Intelligent Systems and Networks},
  isbn = {978-981-95-1746-6},
  pages = {153--163},
  publisher = {Springer Nature Singapore},
  title = {GN-TRVN: A Benchmark for Vietnamese Table Markdown Retrieval Task},
  year = {2026},
}

@article{vianli,
title = {A New Benchmark Dataset and Mixture-of-Experts Language Models for Adversarial Natural Language Inference in Vietnamese},
journal = {Expert Systems with Applications},
pages = {130109},
year = {2025},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2025.130109},
url = {https://www.sciencedirect.com/science/article/pii/S095741742503725X},
author = {Tin Van Huynh, Kiet Van Nguyen and Ngan Luu-Thuy Nguyen},
}

@inproceedings{thai-etal-2022-uit,
    title = "{UIT}-{V}i{C}o{V}19{QA}: A Dataset for {COVID}-19 Community-based Question Answering on {V}ietnamese Language",
    author = "Thai, Triet  and
      Thao-Ha, Ngan Chu  and
      Vo, Anh  and
      Luu, Son",
    editor = "Dita, Shirley  and
      Trillanes, Arlene  and
      Lucas, Rochelle Irene",
    booktitle = "Proceedings of the 36th Pacific {A}sia Conference on Language, Information and Computation",
    month = oct,
    year = "2022",
    address = "Manila, Philippines",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.paclic-1.88/",
    pages = "801--810"
}

@inproceedings{tran2025vifactcheck,
  title     = {ViFactCheck: A New Benchmark Dataset and Methods for Multi-Domain News Fact-Checking in Vietnamese},
  author    = {Tran, Thai Hoa and Tran, Quang Duy and Tran, Khanh Quoc and Nguyen, Kiet Van},
  booktitle = {Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25)},
  pages     = {308--316},
  year      = {2025},
  publisher = {AAAI Press}
}

@inproceedings{tran-etal-2024-vimedaqa,
    title = "{V}i{M}ed{AQA}: A {V}ietnamese Medical Abstractive Question-Answering Dataset and Findings of Large Language Model",
    author = "Tran, Minh-Nam  and
      Nguyen, Phu-Vinh  and
      Nguyen, Long  and
      Dinh, Dien",
    editor = "Fu, Xiyan  and
      Fleisig, Eve",
    booktitle = "Proceedings of the 62nd Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-srw.31/",
    doi = "10.18653/v1/2024.acl-srw.31",
    pages = "252--260",
    ISBN = "979-8-89176-097-4"
}

@inproceedings{le-etal-2022-vimqa,
    title = "{VIMQA}: A {V}ietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering",
    author = "Le, Nguyen-Khang  and
      Nguyen, Dieu-Hien  and
      Le, Tung  and
      Nguyen, Minh",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.700/",
    pages = "6521--6529"
}

@inproceedings{huynh-etal-2022-vinli,
    title = "{V}i{NLI}: A {V}ietnamese Corpus for Studies on Open-Domain Natural Language Inference",
    author = "Huynh, Tin Van  and
      Nguyen, Kiet Van  and
      Nguyen, Ngan Luu-Thuy",
    editor = "Calzolari, Nicoletta  and
      Huang, Chu-Ren  and
      Kim, Hansaem  and
      Pustejovsky, James  and
      Wanner, Leo  and
      Choi, Key-Sun  and
      Ryu, Pum-Mo  and
      Chen, Hsin-Hsi  and
      Donatelli, Lucia  and
      Ji, Heng  and
      Kurohashi, Sadao  and
      Paggio, Patrizia  and
      Xue, Nianwen  and
      Kim, Seokhwan  and
      Hahm, Younggyun  and
      He, Zhong  and
      Lee, Tony Kyungil  and
      Santus, Enrico  and
      Bond, Francis  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.339/",
    pages = "3858--3872"
}

@inproceedings{williams-etal-2018-broad,
    title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
    author = "Williams, Adina  and
      Nangia, Nikita  and
      Bowman, Samuel R.",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the {N}orth {A}merican Chapter of the {A}ssociation for {C}omputational {L}inguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N18-1101/",
    doi = "10.18653/v1/N18-1101",
    pages = "1112--1122"
}

@misc{do2024r2gqaretrieverreadergeneratorquestionanswering,
      title={R2GQA: Retriever-Reader-Generator Question Answering System to Support Students Understanding Legal Regulations in Higher Education}, 
      author={Phuc-Tinh Pham Do and Duy-Ngoc Dinh Cao and Khanh Quoc Tran and Kiet Van Nguyen},
      year={2024},
      eprint={2409.02840},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.02840}, 
}

@inproceedings{le-etal-2026-viwikifc,
    title = "{V}i{W}iki{FC}: Fact-Checking for {V}ietnamese {W}ikipedia-Based Textual Knowledge Source",
    author = "Le, Hung Tuan  and
      To, Long Truong  and
      Nguyen, Manh Trong  and
      Nguyen, Kiet Van",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.869/",
    doi = "10.63317/5ew98tfds6jc",
    pages = "11125--11140"
}

No verified citation was found for: isedsc2024, vietnamese_law_hn_mined.

Limitations and licenses

Each source keeps its own license (column above); this collection is license: other. Sources that are non-commercial or have no stated license: csconda, isedsc2024, multi_wiki_qa, vicov19qa, vietnamese_law_hn_mined, vihealthqa, vimedaqa, vimqa, vinli, virhe4qa. Check the original license before any use beyond research. Q&A sources crawled from the web had person names masked with a Vietnamese NER model and phone/ID/account numbers masked by regex (<name>, <phone>, …).