Update README.md

README.md

@@ -29,11 +29,25 @@ inference:
     num_beams: 3
     do_sample: True
 ---
+
 # 💡GENIUS – generating text using sketches!
+
+**基于草稿的文本生成模型**
+
 - **Paper: [GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation](https://github.com/beyondguo/genius/blob/master/GENIUS_gby_arxiv.pdf)**
-- **GitHub: [GENIUS project, GENIUS pre-training, GeniusAug for data augmentation](https://github.com/beyondguo/genius)**
 
-💡**GENIUS** is a powerful conditional text generation model using sketches as input, which can fill in the missing contexts for a given **sketch** (key information consisting of textual spans, phrases, or words, concatenated by mask tokens). GENIUS is pre-trained on a large-scale textual corpus with a novel *reconstruction from sketch* objective using an *extreme and selective masking* strategy, enabling it to generate diverse and high-quality texts given sketches.
+💡**GENIUS** is a powerful conditional text generation model using sketches as input, which can fill in the missing contexts for a given **sketch** (key information consisting of textual spans, phrases, or words, concatenated by mask tokens). GENIUS is pre-trained on a large- scale textual corpus with a novel *reconstruction from sketch* objective using an *extreme and selective masking* strategy, enabling it to generate diverse and high-quality texts given sketches.
+
+**Example 1:**
+
+- sketch: `__ machine learning __ my research interest __ data science __`
+- **GENIUS**: `I am a Ph.D. student in machine learning, and my research interest is in data science. I am interested in understanding how humans and machines interact and how we can improve the quality of life for people around the world.`
+
+**Example 2:**
+
+- sketch: `自然语言处理__谷歌__通用人工智能__`
+- **GENIUS**: `自然语言处理是谷歌在通用人工智能领域的一个重要研究方向，其目的是为了促进人类智能的发展。 `
+
 
 
 **GENIUS** can also be used as a general textual **data augmentation tool** for **various NLP tasks** (including sentiment analysis, topic classification, NER, and QA). 
@@ -42,6 +56,7 @@ inference:
 ![image-20221119164544165](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/hi-genius.png)
 
 
+
 - Models hosted in 🤗 Huggingface:
 
 **Model variations:**
@@ -54,7 +69,14 @@ inference:
 | [`genius-base-ps`](https://huggingface.co/beyond/genius-base)  | 139M    | English | pre-trained both in paragraphs and short sentences |
 | [`genius-base-chinese`](https://huggingface.co/beyond/genius-base-chinese) | 116M    | 中文 | 在一千万纯净中文段落上预训练|
 
----
+![image-20221119191940969](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/202211191919005.png)
+
+
+
+
+More Examples:
+
+![image-20221119184950762](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/202211191849815.png)
 
 ## Usage
 
@@ -69,7 +91,6 @@ The sketch which can be composed of:
 - sentences, like `I really like machine learning__I work at Google since last year__`
 - or a mixup!
 
-(the `__` is the mask token. Use `<mask>` for English, and `[MASK]` for Chinese)
 
 ### How to use the model
 #### 1. If you already have a sketch in mind, and want to get a paragraph based on it...
@@ -95,3 +116,43 @@ TODO: we are also building a python package for more convenient use of GENIUS, w
 #### 2. If you have an NLP dataset (e.g. classification) and want to do data augmentation to enlarge your dataset...
 
 Please check [genius/augmentation_clf](https://github.com/beyondguo/genius/tree/master/augmentation_clf) and [genius/augmentation_ner_qa](https://github.com/beyondguo/genius/tree/master/augmentation_ner_qa), where we provide ready-to-run scripts for data augmentation for text classification/NER/MRC tasks.
+
+
+
+## Augmentation Experiments:
+Data augmentation is an important application for natural language generation (NLG) models, which is also a valuable evaluation of whether the generated text can be used in real applications. 
+- 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.
+- Text Classification 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   |
+|  **GeniusAug**  |   81.43   |   95.74   |   49.60   |   50.38   | **80.16** |   78.82   |   72.68   |
+| **GeniusAug-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   |
+|  **GeniusAug**  |   74.87   |   66.85   |   76.02   |   74.76   |   73.13   |
+| **GeniusAug-f** | **76.18** |   66.89   | **77.45** | **80.36** | **75.22** |
+
+### BibTeX entry and citation info
+TBD
+
+