rst-summarization-11b

<p align="center"> <br> <img src="https://expressai-xlab.s3.amazonaws.com/rst/intro_rst.png" width="1000"/> <br> </p>

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.

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:

Bibtext for Citation Info

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