| 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 | |