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_new_tokens: 200
top_k: 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.
Model description
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 |