# Training Data for Text Embedding Models

This repository contains training files to train text embedding models, e.g. using [sentence-transformers](https://www.SBERT.net).

## 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](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model on it with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#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 |
| --- | --- | :---: | :---: | --- |
| [AllNLI.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/AllNLI.jsonl.gz) | Combination of SNLI + MultiNLI Triplets: (Anchor, Entailment_Text, Contradiction_Text) | 277,230 | 56.57 | [SNLI](https://huggingface.co/datasets/snli) and [MNLI](https://huggingface.co/datasets/multi_nli)
| [altlex.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/altlex.jsonl.gz) | Matched pairs (English_Wikipedia, Simple_English_Wikipedia) | 112,696 | 55.95 | [altlex](https://github.com/chridey/altlex/)
| [amazon_review_2018.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon_review_2018.jsonl.gz) | (Title, review) pairs from Amazon | 87,877,725 | 57.65 | [Amazon review data (2018)](http://deepyeti.ucsd.edu/jianmo/amazon/index.html)
| [amazon-qa.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon-qa.jsonl.gz) | (Question, Answer) pairs from Amazon | 1,095,290 | 57.48 | [AmazonQA](https://github.com/amazonqa/amazonqa)
| [coco_captions.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/coco_captions.jsonl.gz) | Different captions for the same image | 82,783 | 53.77 | [COCO](https://cocodataset.org/)
| [codesearchnet.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/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](https://huggingface.co/datasets/code_search_net)
| [eli5_question_answer.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/eli5_question_answer.jsonl.gz) | (Question, Answer)-Pairs from ELI5 dataset | 325,475 | 58.24 | [ELI5](https://huggingface.co/datasets/eli5)
| [fever_train.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/fever_train.jsonl.gz) | Training data from the FEVER corpus | 139,051 | 52.63 | [FEVER](https://huggingface.co/datasets/fever)
| [flickr30k_captions.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz) | Different captions for the same image from the Flickr30k dataset | 31,783 | 54.68 | [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/)
| [gooaq_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/gooaq_pairs.jsonl.gz) | (Question, Answer)-Pairs from Google auto suggest | 3,012,496 | 59.06 | [GooAQ](https://github.com/allenai/gooaq)
| [msmarco-triplets.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/msmarco-triplets.jsonl.gz) | (Question, Answer, Negative)-Triplets from MS MARCO Passages dataset | 499,184 | 58.76 | [MS MARCO Passages](https://github.com/microsoft/MSMARCO-Passage-Ranking)
| [NQ-train_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/NQ-train_pairs.jsonl.gz) | Training pairs (query, answer_passage) from the NQ dataset | 100,231 | 57.48 | [Natural Questions](https://ai.google.com/research/NaturalQuestions)
| [PAQ_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/PAQ_pairs.jsonl.gz) | Training pairs (query, answer_passage) from the PAQ dataset | 64,371,441 | 56.11 | [PAQ](https://github.com/facebookresearch/PAQ)
| [quora_duplicates.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates.jsonl.gz) | Duplicate question pairs from Quora | 103,663 | 57.36 | [QQP](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
| [quora_duplicates_triplets.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates_triplets.jsonl.gz) | Duplicate question pairs from Quora with additional hard negatives (mined & denoised by cross-encoder) | 101,762 | 56.97 | [QQP](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
| [sentence-compression.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/sentence-compression.jsonl.gz) | Pairs (long_text, short_text) about sentence-compression | 180,000 | 55.63 | [Sentence-Compression](https://github.com/google-research-datasets/sentence-compression)
| [specter_train_triples.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/specter_train_triples.jsonl.gz) | Triplets (Title, related_title, hard_negative) for Scientific Publications from Specter | 684,100 | 56.32 | [SPECTER](https://github.com/allenai/specter)
| [squad_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/squad_pairs.jsonl.gz) |  (Question, Answer_Passage) Pairs from SQuAD dataset | 87,599 | 58.02 | [SQuAD](https://huggingface.co/datasets/squad)
| [stackexchange_duplicate_questions_body_body.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/stackexchange_duplicate_questions_body_body.jsonl.gz) | (Body, Body) pairs of duplicate questions from StackExchange | 250,519 | 57.26 | [Stack Exchange Data API](https://data.stackexchange.com/apple/query/fork/1456963)
| [stackexchange_duplicate_questions_title-body_title-body.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/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](https://data.stackexchange.com/apple/query/fork/1456963)
| [stackexchange_duplicate_questions_title_title.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/stackexchange_duplicate_questions_title_title.jsonl.gz) | (Title, Title) pairs of duplicate questions from StackExchange | 304,525 | 58.47 | [Stack Exchange Data API](https://data.stackexchange.com/apple/query/fork/1456963)
| [S2ORC_citations_titles.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/S2ORC_citations_titles.jsonl.gz) | Citation network (paper titles) | 51,030,086 | 57.28 | [S2ORC](https://github.com/allenai/s2orc)
| [S2ORC_title_abstract.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/S2ORC_title_abstract.jsonl.gz) | (Title, Abstract) pairs of scientific papers | 41,769,185 | 57.39 | [S2ORC](https://github.com/allenai/s2orc)
| [searchQA_top5_snippets.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/searchQA_top5_snippets.jsonl.gz) | Question + Top5 text snippets from SearchQA dataset. Top5 | 117,220 | 57.34 | [search_qa](https://huggingface.co/datasets/search_qa)
| [SimpleWiki.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/SimpleWiki.jsonl.gz) | Matched pairs (English_Wikipedia, Simple_English_Wikipedia) | 102,225 | 56.15 | [SimpleWiki](https://cs.pomona.edu/~dkauchak/simplification/)
| [TriviaQA_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/TriviaQA_pairs.jsonl.gz) | Pairs (query, answer) from TriviaQA dataset | 73,346 | 55.56 | [TriviaQA](https://huggingface.co/datasets/trivia_qa)
| [WikiAnswers.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/WikiAnswers.jsonl.gz) | Sets of duplicates questions | 27,383,151 | 57.34 | [WikiAnswers Corpus](https://github.com/afader/oqa#wikianswers-corpus)
| [wikihow.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/wikihow.jsonl.gz) | (Summary, Text) from WikiHow | 128,542 | 57.67 | [WikiHow](https://github.com/pvl/wikihow_pairs_dataset)
| [yahoo_answers_question_answer.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/yahoo_answers_question_answer.jsonl.gz) | (Question_Body, Answer) pairs from Yahoo Answers | 681,164 | 57.74 | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset)
| [yahoo_answers_title_answer.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/yahoo_answers_title_answer.jsonl.gz) | (Title, Answer) pairs from Yahoo Answers | 1,198,260 | 58.65 | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset)
| [yahoo_answers_title_question.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/yahoo_answers_title_question.jsonl.gz) | (Title, Question_Body) pairs from Yahoo Answers | 659,896 | 58.05 | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset)


**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.