QuangDuy/supervised_data-hn-rankwindow

HUIT-Embedding — Rank-window hard negatives with consistency filtering (Vietnamese)

Hard negatives mined for QuangDuy/supervised_data with the method of KaLM-Embedding. 32 configs. Generated on 2026-10-25. Candidates come from QuangDuy/supervised_data-hn-candidates.

Method

Consistency filtering: queries whose positive is ranked below 50 by the retriever are dropped (likely mislabeled or weakly matched). Ten negatives are sampled (seeded by the query id) from candidate ranks 30–100 (-r30_100, as SFR and Linq) or 50–100 (-r50_100, as KaLM-Embedding).

• 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.

Differences from the paper

• Retriever is intfloat/multilingual-e5-large.
• mMARCO is not included: its negatives are random BM25 samples without retriever ranks.

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-rankwindow", "csconda-r30_100", split="train")

# ids layout: join negatives with the documents table
ids = load_dataset("QuangDuy/supervised_data-hn-rankwindow", "csconda-r30_100-ids", split="train")
docs = load_dataset("QuangDuy/supervised_data-hn-rankwindow", "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-r30_100	csconda	19	16	84.2%	10.0
csconda-r50_100	csconda	19	16	84.2%	10.0
isedsc2024-r30_100	isedsc2024	7,944	7,831	98.6%	10.0
isedsc2024-r50_100	isedsc2024	7,944	7,831	98.6%	10.0
large_vi_legal_queries-r30_100	large_vi_legal_queries	494,312	437,860	88.6%	10.0
large_vi_legal_queries-r50_100	large_vi_legal_queries	494,312	437,860	88.6%	10.0
multi_wiki_qa-r30_100	multi_wiki_qa	4,932	4,851	98.4%	10.0
multi_wiki_qa-r50_100	multi_wiki_qa	4,932	4,851	98.4%	10.0
table_markdown_retrieval-r30_100	table_markdown_retrieval	125,830	89,784	71.4%	10.0
table_markdown_retrieval-r50_100	table_markdown_retrieval	125,830	89,784	71.4%	10.0
vianli-r30_100	vianli	1,606	1,472	91.7%	10.0
vianli-r50_100	vianli	1,606	1,472	91.7%	10.0
vicov19qa-r30_100	vicov19qa	2,911	2,298	78.9%	10.0
vicov19qa-r50_100	vicov19qa	2,911	2,298	78.9%	10.0
vietnamese_law_hn_mined-r30_100	vietnamese_law_hn_mined	327,190	305,571	93.4%	10.0
vietnamese_law_hn_mined-r50_100	vietnamese_law_hn_mined	327,190	305,571	93.4%	10.0
vifactcheck-r30_100	vifactcheck	1,595	1,590	99.7%	10.0
vifactcheck-r50_100	vifactcheck	1,595	1,590	99.7%	10.0
vihealthqa-r30_100	vihealthqa	6,582	5,317	80.8%	10.0
vihealthqa-r50_100	vihealthqa	6,582	5,317	80.8%	10.0
vimedaqa-r30_100	vimedaqa	31,437	28,894	91.9%	10.0
vimedaqa-r50_100	vimedaqa	31,437	28,894	91.9%	10.0
vimqa-r30_100	vimqa	7,158	7,155	100.0%	10.0
vimqa-r50_100	vimqa	7,158	7,155	100.0%	10.0
vinli-r30_100	vinli	2,107	2,095	99.4%	10.0
vinli-r50_100	vinli	2,107	2,095	99.4%	10.0
vinli_simcse_v2-r30_100	vinli_simcse_v2	268,871	189,498	70.5%	10.0
vinli_simcse_v2-r50_100	vinli_simcse_v2	268,871	189,498	70.5%	10.0
virhe4qa-r30_100	virhe4qa	75	75	100.0%	10.0
virhe4qa-r50_100	virhe4qa	75	75	100.0%	10.0
viwikifc-r30_100	viwikifc	1,802	1,800	99.9%	10.0
viwikifc-r50_100	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
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{hu2025kalmembeddingsuperiortrainingdata,
      title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model},
      author={Xinshuo Hu and Zifei Shan and Xinping Zhao and Zetian Sun and Zhenyu Liu and Dongfang Li and Shaolin Ye and Xinyuan Wei and Qian Chen and Baotian Hu and Haofen Wang and Jun Yu and Min Zhang},
      year={2025},
      eprint={2501.01028},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.01028},
}

@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{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>, …).