---
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](to_be_added)
- Github: [this repository](to_be_added). 


## 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 |


## Intended uses & limitations



### How to use


### Limitations and bias


## Training data


## Training procedure

### Preprocessing


### Pretraining

## Evaluation results



### BibTeX entry and citation info