Update README.md

README.md

@@ -31,12 +31,25 @@ 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). 
@@ -45,6 +58,7 @@ inference:
 ![image-20221119164544165](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/hi-genius.png)
 
 
+
 - Models hosted in 🤗 Huggingface:
 
 **Model variations:**
@@ -57,7 +71,14 @@ 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
 
@@ -67,13 +88,12 @@ First, what is a **sketch**? As defined in our paper, a sketch is "key informati
 
 The sketch which can be composed of:
 
-- keywords /key-phrases, like "`__NLP__AI__computer__science__`"
+- 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 a mixup!
 
 
-
 ### How to use the model
 #### 1. If you already have a sketch in mind, and want to get a paragraph based on it...
 ```python