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Training Data for Text Embedding Models

This repository contains training files to train text embedding models, e.g. using sentence-transformers.

Data Format

All files are in a jsonl.gz format: Each line contains a JSON-object that represent one training example.

The JSON objects can come in different formats:

  • Pairs: ["text1", "text2"] - This is a positive pair that should be close in vector space.
  • Triplets: ["anchor", "positive", "negative"] - This is a triplet: The positive text should be close to the anchor, while the negative text should be distant to the anchor.
  • Sets: {"set": ["text1", "text2", ...]} A set of texts describing the same thing, e.g. different paraphrases of the same question, different captions for the same image. Any combination of the elements is considered as a positive pair.
  • Query-Pairs: {"query": "text", "pos": ["text1", "text2", ...]} A query together with a set of positive texts. Can be formed to a pair ["query", "positive"] by randomly selecting a text from pos.
  • Query-Triplets: {"query": "text", "pos": ["text1", "text2", ...], "neg": ["text1", "text2", ...]} A query together with a set of positive texts and negative texts. Can be formed to a triplet ["query", "positive", "negative"] by randomly selecting a text from pos and neg.

Available Datasets

Note: I'm currently in the process to upload the files. Please check again next week to get the full list of datasets

We measure the performance for each training dataset by training the nreimers/MiniLM-L6-H384-uncased model on it with MultipleNegativesRankingLoss, a batch size of 256, for 2000 training steps. The performance is then averaged across 14 sentence embedding benchmark datasets from diverse domains (Reddit, Twitter, News, Publications, E-Mails, ...).

Dataset Description Size (#Lines) Performance Reference
gooaq_pairs.jsonl.gz (Question, Answer)-Pairs from Google auto suggest 3,012,496 59.06 GooAQ
yahoo_answers_title_answer.jsonl.gz (Title, Answer) pairs from Yahoo Answers 1,198,260 58.65 Yahoo Answers
msmarco-triplets.jsonl.gz (Question, Answer, Negative)-Triplets from MS MARCO Passages dataset 499,184 58.76 MS MARCO Passages
stackexchange_duplicate_questions_title_title.jsonl.gz (Title, Title) pairs of duplicate questions from StackExchange 304,525 58.47 Stack Exchange Data API
eli5_question_answer.jsonl.gz (Question, Answer)-Pairs from ELI5 dataset 325,475 58.24 ELI5
yahoo_answers_title_question.jsonl.gz (Title, Question_Body) pairs from Yahoo Answers 659,896 58.05 Yahoo Answers
squad_pairs.jsonl.gz (Question, Answer_Passage) Pairs from SQuAD dataset 87,599 58.02 SQuAD
yahoo_answers_question_answer.jsonl.gz (Question_Body, Answer) pairs from Yahoo Answers 681,164 57.74 Yahoo Answers
wikihow.jsonl.gz (Summary, Text) from WikiHow 128,542 57.67 WikiHow
amazon_review_2018.jsonl.gz (Title, review) pairs from Amazon 87,877,725 57.65 Amazon review data (2018)
NQ-train_pairs.jsonl.gz Training pairs (query, answer_passage) from the NQ dataset 100,231 57.48 Natural Questions
amazon-qa.jsonl.gz (Question, Answer) pairs from Amazon 1,095,290 57.48 AmazonQA
S2ORC_title_abstract.jsonl.gz (Title, Abstract) pairs of scientific papers 41,769,185 57.39 S2ORC
quora_duplicates.jsonl.gz Duplicate question pairs from Quora 103,663 57.36 QQP
WikiAnswers.jsonl.gz Sets of duplicates questions 27,383,151 57.34 WikiAnswers Corpus
searchQA_top5_snippets.jsonl.gz Question + Top5 text snippets from SearchQA dataset. Top5 117,220 57.34 search_qa
stackexchange_duplicate_questions_title-body_title-body.jsonl.gz (Title+Body, Title+Body) pairs of duplicate questions from StackExchange 250,460 57.30 Stack Exchange Data API
S2ORC_citations_titles.jsonl.gz Citation network (paper titles) 51,030,086 57.28 S2ORC
stackexchange_duplicate_questions_body_body.jsonl.gz (Body, Body) pairs of duplicate questions from StackExchange 250,519 57.26 Stack Exchange Data API
agnews.jsonl.gz (Title, Description) pairs of news articles from the AG News dataset 1,157,745 57.25 AG news corpus
quora_duplicates_triplets.jsonl.gz Duplicate question pairs from Quora with additional hard negatives (mined & denoised by cross-encoder) 101,762 56.97 QQP
AllNLI.jsonl.gz Combination of SNLI + MultiNLI Triplets: (Anchor, Entailment_Text, Contradiction_Text) 277,230 56.57 SNLI and MNLI
npr.jsonl.gz (Title, Body) pairs from the website 594,384 56.44 Pushshift
specter_train_triples.jsonl.gz Triplets (Title, related_title, hard_negative) for Scientific Publications from Specter 684,100 56.32 SPECTER
SimpleWiki.jsonl.gz Matched pairs (English_Wikipedia, Simple_English_Wikipedia) 102,225 56.15 SimpleWiki
PAQ_pairs.jsonl.gz Training pairs (query, answer_passage) from the PAQ dataset 64,371,441 56.11 PAQ
altlex.jsonl.gz Matched pairs (English_Wikipedia, Simple_English_Wikipedia) 112,696 55.95 altlex
ccnews_title_text.jsonl.gz (Title, article) pairs from the CC News dataset 614,664 55.84 CC-News
codesearchnet.jsonl.gz CodeSearchNet corpus is a dataset of (comment, code) pairs from opensource libraries hosted on GitHub. It contains code and documentation for several programming languages. 1,151,414 55.80 CodeSearchNet
S2ORC_citations_abstracts.jsonl.gz Citation network (paper abstracts) 39,567,485 55.74 S2ORC
sentence-compression.jsonl.gz Pairs (long_text, short_text) about sentence-compression 180,000 55.63 Sentence-Compression
TriviaQA_pairs.jsonl.gz Pairs (query, answer) from TriviaQA dataset 73,346 55.56 TriviaQA
cnn_dailymail_splitted.jsonl.gz (article, highlight sentence) with individual highlight sentences for each news article 311,971 55.36 CNN Dailymail Dataset
cnn_dailymail.jsonl.gz (highlight sentences, article) with all highlight sentences as one text for each news article 311,971 55.27 CNN Dailymail Dataset
flickr30k_captions.jsonl.gz Different captions for the same image from the Flickr30k dataset 31,783 54.68 Flickr30k
xsum.jsonl.gz (Summary, News Article) pairs from XSUM dataset 226,711 53.86 xsum
coco_captions.jsonl.gz Different captions for the same image 82,783 53.77 COCO

Disclaimer: We only distribute these datasets in a specific format, but we do not vouch for their quality or fairness, or claim that you have license to use the dataset. It remains the user's responsibility to determine whether you as a user have permission to use the dataset under the dataset's license and to cite the right owner of the dataset. Please check the individual dataset webpages for the license agreements.

If you're a dataset owner and wish to update any part of it, or do not want your dataset to be included in this dataset collection, feel free to contact me.

Models trained or fine-tuned on sentence-transformers/embedding-training-data