Update README.md

README.md

@@ -34,11 +34,22 @@ inference:
 
 # 💡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://arxiv.org/abs/2211.10330)**
 - **GitHub: [GENIUS, Pre-training/Data Augmentation Tutorial](https://github.com/beyondguo/genius)**
 
+**How to use:**
+```python
+from transformers import pipeline
+# 1. load the model with the huggingface `pipeline`
+genius = pipeline("text2text-generation", model='beyond/genius-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. here we go!
+generated_text = genius(sketch, num_beams=3, do_sample=True, max_length=200)[0]['generated_text']
+print(generated_text)
+```
+
 💡**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** can also be used as a general textual **data augmentation tool** for **various NLP tasks** (including sentiment analysis, topic classification, NER, and QA).