metadata

language:
  - en
  - zh
  - multilingual
license: apache-2.0
tags:
  - GENIUS
  - conditional text generation
  - sketch-based text generation
  - data augmentation
datasets:
  - c4
  - beyond/chinese_clean_passages_80m
widget:
  - text: >-
      <mask> Conference on Empirical Methods <mask> submission of research
      papers <mask> Deep Learning <mask>
    example_title: Example 1
  - text: >-
      <mask> machine learning <mask> my research interest <mask> data science
      <mask>
    example_title: Example 2
  - text: >-
      <mask> play basketball <mask> a strong team <mask> Shanghai University of
      Finance and Economics <mask> last Sunday <mask>
    example_title: Example 3
  - text: >-
      Good news: <mask> the European Union <mask> month by EU <mask> Farm
      Commissioner Franz <mask>
    example_title: Example with a prompt 1
  - text: >-
      Bad news: <mask> the European Union <mask> month by EU <mask> Farm
      Commissioner Franz <mask>
    example_title: Example with a prompt 2
inference:
  parameters:
    max_length: 200
    num_beams: 3
    do_sample: true

GENIUS: generating text using sketches!

You can use this model directly with a pipeline for masked language modeling:

from transformers import pipeline
# 1. load the model with the huggingface `pipeline`
genius = pipeline("text2text-generation", model='beyond/genius-large', device=0)
# 2. provide a sketch (joint by <mask> tokens)
sketch = "<mask> Conference on Empirical Methods <mask> submission of research papers <mask> Deep Learning <mask>"
# 3. here we go!
generated_text = genius(sketch, num_beams=3, do_sample=True, max_length=200)[0]['generated_text']
print(generated_text)

If you find our paper/code/demo useful, please cite our paper:

@article{guo2022genius,
  title={GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation},
  author={Guo, Biyang and Gong, Yeyun and Shen, Yelong and Han, Songqiao and Huang, Hailiang and Duan, Nan and Chen, Weizhu},
  journal={arXiv preprint arXiv:2211.10330},
  year={2022}
}