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README.md
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license: apache-2.0
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---
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license: apache-2.0
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---
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# tdro-llm/finetune_data
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[](https://arxiv.org/abs/2408.10613)
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[](https://github.com/tdro-llm/tdro)
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[tDRO: Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval](https://arxiv.org/abs/2408.10613). Guangyuan Ma, Yongliang Ma, Xing Wu, Zhenpeng Su, Ming Zhou and Songlin Hu.
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This repo contains all fine-tuning data for Large Language Model-based Dense Retrieval. Please refer to [this repo](https://github.com/tdro-llm/tdro) for details to reproduce.
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A total of 25 heterogeneous retrieval fine-tuning datasets with **Hard Negatives** and **Deduplication** (with test sets) are listed as belows.
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| **Dataset** | **Language** | **Category** | **Symmetry** | **Reference** | **Format** | **HN Mine** | **Size** | **Deduped Size** | **Duplicates** |
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|---------------------------------------------------------|--------------|------------------------------|--------------|---------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|----------|------------------|----------------|
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| agnews | English | News | Asymmetric | [AG news corpus](http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html) | (Title, Description, Negatives) of news articles from the AG News dataset | bge-base-en-v1.5 mine | 1157745 | 1157745 | 0 |
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| AllNLI | English | NLI | Symmetric | [SNLI and MNLI](https://huggingface.co/datasets/multi_nli) | (Anchor, Entailment_Text, Contradiction_Text) - Combination of SNLI + MultiNLI Triplets | Sentence Transformers Train HN | 277230 | 277230 | 0 |
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| altlex | English | Wikipedia Pair | Symmetric | [altlex](https://github.com/chridey/altlex/) | (English_Wikipedia, Simple_English_Wikipedia, Negatives) - Matched pairs | bge-base-en-v1.5 mine | 112696 | 112696 | 0 |
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| amazon_review_2018_1m | English | Amazon | Asymmetric | [Amazon review data (2018)](http://deepyeti.ucsd.edu/jianmo/amazon/index.html) | (Title, review, Negatives) from Amazon. Only Top 1 million samples are used here to shrink the dataset sizes. | bge-base-en-v1.5 mine | 1000000 | 999999 | 1 |
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| cnn_dailymail | English | News | Asymmetric | [CNN Dailymail Dataset](https://huggingface.co/datasets/cnn_dailymail) | (highlight sentences, article) with all highlight sentences as one text for each news article | bge-base-en-v1.5 mine | 311971 | 311971 | 0 |
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| codesearchnet | English | Github | Asymmetric | [CodeSearchNet](https://huggingface.co/datasets/code_search_net) | (Comment, Code, Negatives) - pairs from opensource libraries hosted on GitHub. It contains code and documentation for several programming languages. | bge-base-en-v1.5 mine | 1375067 | 1375067 | 0 |
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| dureader | Chinese | Multilingual Web Collections | Asymmetric | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | (Question, Answer, Negatives) | Use originally provided HN | 86395 | 86395 | 0 |
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| eli5_question_answer | English | Reddit | Asymmetric | [ELI5](https://huggingface.co/datasets/eli5) | (Question, Answer, Negatives) | bge-base-en-v1.5 mine | 325475 | 325390 | 85 |
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| gooaq_pairs | English | Web Collections | Asymmetric | [GooAQ](https://github.com/allenai/gooaq) | (Question, Answer, Negatives) - Pairs from Google auto suggest | bge-base-en-v1.5 mine | 3012496 | 3012347 | 149 |
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| hotpotqa | English | Wikipedia QA | Asymmetric | [HotpotQA](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | (Question, Answer, Negatives) | bge-base-en-v1.5 mine | 85000 | 85000 | 0 |
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| medmcqa | English | Medical | Asymmetric | [MedMCQA](https://huggingface.co/datasets/openlifescienceai/medmcqa) | (Question, Answer, Negatives) | bge-base-en-v1.5 mine | 160869 | 160865 | 4 |
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| miracl | 16 languages | Multilingual Wikipedia | Asymmetric | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | (Question, Answer, Negatives) | Use originally provided HN | 32561 | 32405 | 156 |
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| mr_tydi_combined | 11 languages | Multilingual Wikipedia | Asymmetric | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | (Question, Answer, Negatives) | Use originally provided HN | 48715 | 48475 | 240 |
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| msmarco | English | Web Collections | Asymmetric | [MS MARCO Passages](https://github.com/microsoft/MSMARCO-Passage-Ranking) | (Question, Answer, Negatives) | bowdpr HN by following [this link](https://github.com/ma787639046/bowdpr) | 502939 | 502854 | 85 |
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| nq | English | Wikipedia QA | Asymmetric | [NQ](https://github.com/facebookresearch/DPR) | (Question, Answer, Negatives) | bowdpr HN by following [this link](https://github.com/ma787639046/bowdpr) | 58812 | 58800 | 12 |
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| quora_duplicates_triplets | English | Forum Duplicates | Symmetric | [QQP](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | (Question, Duplicated Question, Negatives) - Duplicate question pairs from Quora | Sentence Transformers Train HN | 101762 | 97011 | 4751 |
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| searchQA_top5_snippets | English | Web Collections | Asymmetric | [search_qa](https://huggingface.co/datasets/search_qa) | (Question, Top5 text snippets, Negatives) from SearchQA dataset. | bge-base-en-v1.5 mine | 117220 | 117219 | 1 |
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| sentence-compression | English | News | Asymmetric | [Sentence-Compression](https://github.com/google-research-datasets/sentence-compression) | (Long text, short text) about sentence-compression | bge-base-en-v1.5 mine | 180000 | 180000 | 0 |
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| SimpleWiki | English | Wikipedia Pair | Symmetric | [SimpleWiki](https://cs.pomona.edu/~dkauchak/simplification/) | (English_Wikipedia, Simple_English_Wikipedia, Negatives) matched pairs | bge-base-en-v1.5 mine | 102225 | 102225 | 0 |
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| squad_pairs | English | Wikipedia QA | Asymmetric | [SQuAD](https://huggingface.co/datasets/squad) | (Question, Answer, Negatives) | bge-base-en-v1.5 mine | 87599 | 87595 | 4 |
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| stackexchange_duplicate_questions_title-body_title-body | English | Forum Duplicates | Symmetric | [Stack Exchange Data API](https://data.stackexchange.com/apple/query/fork/1456963) | (Title-Body, Duplicated Title-Body, Negatives) - pairs of duplicate questions from StackExchange | bge-base-en-v1.5 mine | 250519 | 250516 | 3 |
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| t2ranking | Chinese | Multilingual Web Collections | Asymmetric | [T2Ranking](https://github.com/THUIR/T2Ranking/) | (Question, Answer, Negatives) | Use originally provided HN | 200376 | 200376 | 0 |
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| trivia | English | Wikipedia QA | Asymmetric | [Trivia QA](https://github.com/facebookresearch/DPR) | (Question, Answer, Negatives) | bowdpr HN by following [this link](https://github.com/ma787639046/bowdpr) | 60380 | 60370 | 10 |
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| xsum | English | News | Asymmetric | [xsum](https://huggingface.co/datasets/xsum) | (Summary, News Article) pairs from XSUM dataset | bge-base-en-v1.5 mine | 226711 | 226711 | 0 |
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| yahoo_answers_title_answer | English | Yahoo | Asymmetric | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) | (Title, Answer, Negatives) | bge-base-en-v1.5 mine | 1198260 | 1198018 | 242 |
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| **Total Lines** | | | | | | | 11073023 | 11067280 | 5743 |
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## Cite
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If you are interested in our work, please consider citing our paper.
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```bibtex
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@article{ma2024tdro,
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author = {Guangyuan Ma and
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Yongliang Ma and
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Xing Wu and
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Zhenpeng Su and
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Ming Zhou and
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Songlin Hu},
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title = {Task-level Distributionally Robust Optimization for Large Language
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Model-based Dense Retrieval},
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journal = {CoRR},
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volume = {abs/2408.10613},
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year = {2024},
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url = {https://doi.org/10.48550/arXiv.2408.10613},
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doi = {10.48550/ARXIV.2408.10613},
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eprinttype = {arXiv},
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eprint = {2408.10613},
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timestamp = {Tue, 24 Sep 2024 17:36:32 +0200},
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}
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```
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