Create README.md

README.md

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+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+
+# 转换 onnx 模型
+def convert(path, onnx_path, onnx_path):
+    onnx_model = ORTModelForSequenceClassification.from_pretrained(path, from_transformers=True)
+    tokenizer = AutoTokenizer.from_pretrained(path)
+
+    onnx_model.save_pretrained(onnx_path)
+    tokenizer.save_pretrained(onnx_path)
+
+
+# 加载模型，用pipeline包装
+def load_model(model_name):
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
+    print(text_classification_pipeline('这是一个简单的demo，用来防止忘记'))
+    return text_classification_pipeline
+
+
+# 加载 onnx 模型，用pipeline包装
+def load_onnx_model(onnx_path):
+    lang_tokenizer = AutoTokenizer.from_pretrained(onnx_path)
+    lang_model = ORTModelForSequenceClassification.from_pretrained(onnx_path)
+    lang_detecter = pipeline("text-classification", model=lang_model, tokenizer=lang_tokenizer, truncation=True)
+    print(lang_detecter('这是一个简单的demo，用来防止忘记'))
+    return lang_detecter