|
--- |
|
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** \ |
|
**基于草稿的生成式增强模型** |
|
|
|
**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: [coming soon](to_be_added) |
|
- GitHub: [SEGA](https://github.com/beyondguo/SEGA). |
|
|
|
**SEGA** is able to write complete paragraphs given a *sketch*, which can be composed of: |
|
- keywords /key-phrases, like "––NLP––AI––computer––science––" |
|
- spans, like "Conference on Empirical Methods––submission of research papers––" |
|
- sentences, like "I really like machine learning––I work at Google since last year––" |
|
- or mixup~ |
|
|
|
|
|
**Model variations:** |
|
| Model | #params | Language | comment| |
|
|------------------------|--------------------------------|-------|---------| |
|
| [`sega-large`](https://huggingface.co/beyond/sega-large) | 406M | English | The version used in paper | |
|
| [`sega-large-k2t`](https://huggingface.co/beyond/sega-large-k2t) | 406M | English | keywords-to-text | |
|
| [`sega-base`](https://huggingface.co/beyond/sega-base) | 139M | English | smaller version | |
|
| [`sega-base-ps`](https://huggingface.co/beyond/sega-base) | 139M | English | pre-trained both in paragraphs and short sentences | |
|
| [`sega-base-chinese`](https://huggingface.co/beyond/sega-base-chinese) | 116M | 中文 | 在一千万纯净中文段落上预训练| |
|
|
|
--- |
|
|
|
### How to use |
|
#### 1. If you want to generate sentences given a **sketch** |
|
```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.' |
|
``` |
|
|
|
#### 2. If you want to do **data augmentation** to generate new training samples |
|
Please Check our Github page: [github.com/beyondguo/SEGA](https://github.com/beyondguo/SEGA), where we provide ready-to-run scripts for data augmentation for text classification/NER/MRC tasks. |
|
|
|
--- |
|
|
|
## SEGA as A Strong Data Augmentation Tool: |
|
- 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) |
|
|
|
|
|
In-distribution (ID) evaluations: |
|
| Method | Huff | BBC | Yahoo | 20NG | IMDB | SST2 | avg. | |
|
|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:| |
|
| none | 79.17 | **96.16** | 45.77 | 46.67 | 77.87 | 76.67 | 70.39 | |
|
| EDA | 79.20 | 95.11 | 45.10 | 46.15 | 77.88 | 75.52 | 69.83 | |
|
| BackT | 80.48 | 95.28 | 46.10 | 46.61 | 78.35 | 76.96 | 70.63 | |
|
| MLM | 80.04 | 96.07 | 45.35 | 46.53 | 75.73 | 76.61 | 70.06 | |
|
| C-MLM | 80.60 | 96.13 | 45.40 | 46.36 | 77.31 | 76.91 | 70.45 | |
|
| LAMBADA | 81.46 | 93.74 | 50.49 | 47.72 | 78.22 | 78.31 | 71.66 | |
|
| STA | 80.74 | 95.64 | 46.96 | 47.27 | 77.88 | 77.80 | 71.05 | |
|
| **SEGA** | 81.43 | 95.74 | 49.60 | 50.38 | **80.16** | 78.82 | 72.68 | |
|
| **SEGA-f** | **81.82** | 95.99 | **50.42** | **50.81** | 79.40 | **80.57** | **73.17** | |
|
|
|
Out-of-distribution (OOD) evaluations: |
|
| | Huff->BBC | BBC->Huff | IMDB->SST2 | SST2->IMDB | avg. | |
|
|------------|:----------:|:----------:|:----------:|:----------:|:----------:| |
|
| none | 62.32 | 62.00 | 74.37 | 73.11 | 67.95 | |
|
| EDA | 67.48 | 58.92 | 75.83 | 69.42 | 67.91 | |
|
| BackT | 67.75 | 63.10 | 75.91 | 72.19 | 69.74 | |
|
| MLM | 66.80 | 65.39 | 73.66 | 73.06 | 69.73 | |
|
| C-MLM | 64.94 | **67.80** | 74.98 | 71.78 | 69.87 | |
|
| LAMBADA | 68.57 | 52.79 | 75.24 | 76.04 | 68.16 | |
|
| STA | 69.31 | 64.82 | 74.72 | 73.62 | 70.61 | |
|
| **SEGA** | 74.87 | 66.85 | 76.02 | 74.76 | 73.13 | |
|
| **SEGA-f** | **76.18** | 66.89 | **77.45** | **80.36** | **75.22** | |
|
|
|
|
|
|
|
### BibTeX entry and citation info |
|
|
|
|
