---
language: en
tags:
- augmentation
license: apache-2.0
datasets:
- C4
---

# 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