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reStructured Pre-training (RST)

official repository, paper, easter eggs

RST is a new paradigm for language pre-training, which

  • unifies 26 different types of signal from 10 data sources (Totten Tomatoes, Dailymail, Wikipedia, Wikidata, Wikihow, Wordnet, arXiv etc ) in the world structurally, being pre-trained with a monolithcal model,
  • surpasses strong competitors (e.g., T0) on 52/55 popular datasets from a variety of NLP tasks (classification, IE, retrieval, generation etc)
  • achieves superior performance in National College Entrance Examination (Gaokao-English, 高考-英语) achieves 40 points higher than the average scores made by students and 15 points higher than GPT3 with 1/16 parameters. In particular, Qin gets a high score of 138.5 (the full mark is 150) in the 2018 English exam

In such a pre-training paradigm,

  • Data-centric Pre-training: the role of data will be re-emphasized, and model pre-training and fine-tuning of downstream tasks are viewed as a process of data storing and accessing
  • Pre-training over JSON instead of TEXT: a good storage mechanism should not only have the ability to cache a large amount of data but also consider the ease of access.

Model Description

We release all models introduced in our paper, covering 13 different application scenarios. Each model contains 11 billion parameters.

Model Description Recommended Application
rst-all-11b Trained with all the signals below except signals that are used to train Gaokao models All applications below (specialized models are recommended first if high performance is preferred)
rst-fact-retrieval-11b Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym, wikiHow category hierarchy, Wikidata relation, Wikidata entity typing, Paperswithcode entity typing Knowledge intensive tasks, information extraction tasks,factual checker
rst-summarization-11b Trained with the following signals: DailyMail summary, Paperswithcode summary, arXiv summary, wikiHow summary Summarization or other general generation tasks, meta-evaluation (e.g., BARTScore)
rst-temporal-reasoning-11b Trained with the following signals: DailyMail temporal information, wikiHow procedure Temporal reasoning, relation extraction, event-based extraction
rst-information-extraction-11b Trained with the following signals: Paperswithcode entity, Paperswithcode entity typing, Wikidata entity typing, Wikidata relation, Wikipedia entity Named entity recognition, relation extraction and other general IE tasks in the news, scientific or other domains
rst-intent-detection-11b Trained with the following signals: wikiHow goal-step relation Intent prediction, event prediction
rst-topic-classification-11b Trained with the following signals: DailyMail category, arXiv category, wikiHow text category, Wikipedia section title general text classification
rst-word-sense-disambiguation-11b Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym Word sense disambiguation, part-of-speech tagging, general IE tasks, common sense reasoning
rst-natural-language-inference-11b Trained with the following signals: ConTRoL dataset, DREAM dataset, LogiQA dataset, RACE & RACE-C dataset, ReClor dataset, DailyMail temporal information Natural language inference, multiple-choice question answering, reasoning
rst-sentiment-classification-11b Trained with the following signals: Rotten Tomatoes sentiment, Wikipedia sentiment Sentiment classification, emotion classification
rst-gaokao-rc-11b Trained with multiple-choice QA datasets that are used to train the T0pp model General multiple-choice question answering
rst-gaokao-cloze-11b Trained with manually crafted cloze datasets General cloze filling
rst-gaokao-writing-11b Trained with example essays from past Gaokao-English exams and grammar error correction signals Essay writing, story generation, grammar error correction and other text generation tasks

Have a try?

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("XLab/rst-all-11b")
model = AutoModelForSeq2SeqLM.from_pretrained("XLab/rst-all-11b")

inputs = tokenizer.encode("TEXT: this is the best cast iron skillet you will ever buy. QUERY: Is this review \"positive\" or \"negative\"", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))

Data for reStructure Pre-training

This dataset is a precious treasure, containing a variety of naturally occurring signals. Any downstream task you can think of (e.g., the college entrance exam mentioned in the RST paper) can benefit from being pre-trained on some of our provided signals. We spent several months collecting the following 29 signal types, accounting for a total of 46,926,447 data samples. We hope this dataset will be a valuable asset for everyone in natural language processing research.

We provide collected signals through DataLab. For efficiency, we only provide 50,000 samples at most for each signal type. If you want all the samples we collected, please fill this form. More specifically, we collected the following signals.

We will be happy :smiley: to know if the resource is helpful for your work, and please cite our work :blush:
Mine Signal #Sample Use in DataLab Some Applications
Rotten Tomatoes (review, rating) 5,311,109 load_dataset("rst", "rotten_tomatoes_sentiment") Sentiment classification
Daily Mail (text, category) 899,904 load_dataset("rst", "daily_mail_category") Topic classification
Daily Mail (title, text, summary) 1,026,616 load_dataset("rst", "daily_mail_summary") Summarization; Sentence expansion
Daily Mail (text, events) 1,006,412 load_dataset("rst", "daily_mail_temporal") Temporal reasoning
Wikidata (entity, entity_type, text) 2,214,274 load_dataset("rst", "wikidata_entity") Entity typing
Wikidata (subject, object, relation, text) 1,526,674 load_dataset("rst", "wikidata_relation") Relation extraction; Fact retrieval
wikiHow (text, category) 112,109 load_dataset("rst", "wikihow_text_category") Topic classification
wikiHow (low_category, high_category) 4,868 load_dataset("rst", "wikihow_category_hierarchy") Relation extraction; Commonsense reasoning
wikiHow (goal, steps) 47,956 load_dataset("rst", "wikihow_goal_step") Intent detection
wikiHow (text, summary) 703,278 load_dataset("rst", "wikihow_summary") Summarization; Sentence expansion
wikiHow (goal, first_step, second_step) 47,787 load_dataset("rst", "wikihow_procedure") Temporal reasoning
wikiHow (question, description, answer, related_questions) 47,705 load_dataset("rst", "wikihow_question") Question generation
Wikipedia (text, entities) 22,231,011 load_dataset("rst", "wikipedia_entities") Entity recognition
Wikipedia (texts, titles) 3,296,225 load_dataset("rst", "wikipedia_sections") Summarization
WordNet (word, sentence, pos) 27,123 load_dataset("rst", "wordnet_pos") Part-of-speech tagging
WordNet (word, sentence, meaning, possible_meanings) 27,123 load_dataset("rst", "wordnet_meaning") Word sense disambiguation
WordNet (word, sentence, synonyms) 17,804 load_dataset("rst", "wordnet_synonym") Paraphrasing
WordNet (word, sentence, antonyms) 6,408 load_dataset("rst", "wordnet_antonym") Negation
ConTRoL (premise, hypothesis, label) 8,323 load_dataset("rst", "qa_control") Natural language inference
DREAM (context, question, options, answer) 9,164 load_dataset("rst", "qa_dream") Reading comprehension
LogiQA (context, question, options, answer) 7,974 load_dataset("rst", "qa_logiqa") Reading comprehension
ReClor (context, question, options, answer) 5,138 load_dataset("rst", "qa_reclor") Reading comprehension
RACE (context, question, options, answer) 44,880 load_dataset("rst", "qa_race") Reading comprehension
RACE-C (context, question, options, answer) 5,093 load_dataset("rst", "qa_race_c") Reading comprehension
TriviaQA (context, question, answer) 46,636 load_dataset("rst", "qa_triviaqa") Reading comprehension
Arxiv (text, category) 1,696,348 load_dataset("rst", "arxiv_category") Topic classification
Arxiv (text, summary) 1,696,348 load_dataset("rst", "arxiv_summary") Summarization; Sentence expansion
Paperswithcode (text, entities, datasets, methods, tasks, metrics) 4,731,233 load_dataset("rst", "paperswithcode_entity") Entity recognition
Paperswithcode (text, summary) 120,924 load_dataset("rst", "paperswithcode_summary") Summarization; Sentence expansion

Bibtext for Citation Info

@article{yuan2022restructured,
  title={reStructured Pre-training},
  author={Yuan, Weizhe and Liu, Pengfei},
  journal={arXiv preprint arXiv:2206.11147},
  year={2022}
}
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