Create README.md

README.md

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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** \
+**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. 
+
+
+![sega-illustration](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/sega-main-illustration.png)
+
+- 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
+