metadata
language: en
tags:
- augmentation
license: apache-2.0
datasets:
- C4
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
SEGA-large model
SEGA: SkEtch-based Generative Augmentation
SEGA is a general text augmentation model that can be used for data augmentation for various NLP tasks (including sentiment analysis, topic classification, NER, and QA). SEGA uses an encoder-decoder structure (based on the BART architecture) and is pre-trained on the C4-realnewslike corpus.
- Paper: this paper
- Github: this repository.
How to use
from transformers import pipeline
# 1. load the model with the huggingface `pipeline`
sega = pipeline("text2text-generation", model='beyond/sega-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. just do it!
generated_text = sega(sketch, num_beams=3, do_sample=True, max_length=200)[0]['generated_text']
print(generated_text)
'The Conference on Empirical Methods welcomes the submission of research papers. Abstracts should be in the form of a paper or presentation. Please submit abstracts to the following email address: eemml.stanford.edu. The conference will be held at Stanford University on April 1618, 2019. The theme of the conference is Deep Learning.'
Model variations
| Model | #params | Language |
|---|---|---|
sega-large |
xM | English |
sega-base |
xM | English |
sega-small |
xM | English |
sega-large-chinese |
xM | Chinese |
sega-base-chinese |
xM | Chinese |
sega-small-chinese |
xM | Chinese |