Datasets:

sentence-transformers
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embedding-training-data

Tasks:

Feature Extraction

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train

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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Column() changed from object to array in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 153, in _generate_tables
                  df = pd.read_json(f, dtype_backend="pyarrow")
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read
                  obj = self._get_object_parser(self.data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse
                  self._parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1403, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
              ValueError: Trailing data
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2643, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1659, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1816, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1347, in __iter__
                  for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 318, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 156, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 130, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0

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Training Data for Text Embedding Models

This repository contains raw datasets, all of which have also been formatted for easy training in the Embedding Model Datasets collection. We recommend looking there first.

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 npr.org 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.