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---
language: en
tags:
- SEGA
- data augmentation
- keywords-to-text generation
- sketch-to-text generation
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](to_be_added)
- Github: [this repository](to_be_added).
### How to use
```python
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)
```
Output:
```shell
'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 |
## Data Augmentation for Text Classification Tasks:
- Setting: Low-resource setting, where only n={50,100,200,500,1000} labeled samples are available for training. The below results are the average of all training sizes.
- Datasets: [HuffPost](https://huggingface.co/datasets/khalidalt/HuffPost), [BBC](https://huggingface.co/datasets/SetFit/bbc-news), [SST2](https://huggingface.co/datasets/glue), [IMDB](https://huggingface.co/datasets/imdb), [Yahoo](https://huggingface.co/datasets/yahoo_answers_topics), [20NG](https://huggingface.co/datasets/newsgroup).
- Base classifier: [DistilBERT](https://huggingface.co/distilbert-base-cased)
| Method | HuffPost | BBC | SST2 | IMDB | Yahoo | 20NG | avg. |
|---------|:------------------:|:------------------:|:----------------------:|:----------------------:|:----------:|:----------:|:----------:|
| | ID / OOD (BBC) | ID / OOD (Huff) | ID / OOD (IMDB) | ID / OOD (SST2) | | | |
| none | 79.17 / 62.32 | **96.16** / 62.00 | 76.67 / 73.16 | 77.87 / 74.43 | 45.77 | 46.67 | 69.42 |
| EDA | 79.63 / 67.48 | 95.11 / 58.92 | 75.52 / 69.46 | 77.88 / 75.88 | 45.10 | 46.15 | 69.11 |
| STA | 80.74 / 69.31 | 95.64 / 64.82 | 77.80 / 73.66 | 77.88 / 74.77 | 46.96 | 47.27 | 70.88 |
| Back | 80.48 / 67.75 | 95.28 / 63.10 | 76.96 / 72.23 | 78.35 / 75.96 | 46.10 | 46.61 | 70.28 |
| MLM | 80.04 / 66.80 | 96.07 / 65.39 | 76.61/ 73.11 | 75.73 / 73.70 | 45.35 | 46.53 | 69.93 |
| C-MLM | 79.96 / 65.10 | 96.13 / **67.80** | 76.91 / 71.83 | 77.31 / 75.02 | 45.29 | 46.36 | 70.17 |
| LAMBADA | 81.03 / 68.89 | 93.75 / 52.79 | 77.87 / 74.54 | 77.49 / 74.33 | 50.66 | 47.72 | 69.91 |
| **SEGA (Ours)** | 81.43 / 74.87 | 95.61 / 67.79 | 77.87 / 72.94 | **79.51** / **76.75** | 49.43 | 50.47 | 72.67 |
| **SEGA-f (Ours)** | **81.82** / **76.18** | 95.78 / 67.79 | **80.59** / **80.32** | 79.37 / 76.61 | **50.12** | **50.81** | **73.94** |
### BibTeX entry and citation info
|