Update README.md

README.md

@@ -5,7 +5,7 @@ tags:
 - t5
 - pytorch
 - zh
-- text-generation
+- Text2Text-Generation
 license: "apache-2.0"
 widget:
 - text: "对联：丹枫江冷人初去"
@@ -33,6 +33,11 @@ T5的网络结构(原生T5)：
 
 本项目开源在文本生成项目：[textgen](https://github.com/shibing624/textgen)，可支持T5模型，通过如下命令调用：
 
+Install package:
+```shell
+pip install -U textgen
+```
+
 ```shell
 >>> from textgen import T5Model
 >>> model = T5Model("t5", "shibing624/t5-chinese-couplet")
@@ -40,6 +45,40 @@ T5的网络结构(原生T5)：
 ['白石矶寒客不归']
 ```
 
+## Usage (HuggingFace Transformers)
+Without [textgen](https://github.com/shibing624/textgen), you can use the model like this: 
+
+First, you pass your input through the transformer model, then you get the generated sentence.
+
+Install package:
+```
+pip install transformers 
+```
+
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+tokenizer = T5Tokenizer.from_pretrained("shibing624/t5-chinese-couplet")
+model = T5ForConditionalGeneration.from_pretrained("shibing624/t5-chinese-couplet")
+
+
+def batch_generate(input_texts, max_length=64):
+    features = tokenizer(input_texts, return_tensors='pt')
+    outputs = model.generate(input_ids=features['input_ids'],
+                             attention_mask=features['attention_mask'],
+                             max_length=max_length)
+    return tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+
+r = batch_generate(["对联：丹枫江冷人初去"])
+print(r)
+```
+
+output:
+```shell
+['白石矶寒客不归']
+```
+
 模型文件组成：
 ```
 t5-chinese-couplet