28 lines
1.1 KiB
Python
28 lines
1.1 KiB
Python
import paddle
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import numpy as np
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from paddlenlp.transformers import ErnieTokenizer
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import paddle.nn.functional as F # 用于 Softmax
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# 加载模型和tokenizer
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model = paddle.jit.load("trained_model_static") # 加载保存的静态图模型
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tokenizer = ErnieTokenizer.from_pretrained("E:/workingSpace/PycharmProjects/Intention/models/ernie-3.0-tiny-base-v2-zh")
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# 创建输入示例
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text = "今天送变电二公司有？"
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inputs = tokenizer(text, max_length=256, truncation=True, padding='max_length', return_tensors="pd")
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# 将输入数据转化为 Paddle tensor 格式
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input_ids = paddle.to_tensor(inputs["input_ids"])
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# 进行推理
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model.eval() # 确保模型在推理模式
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logits = model(input_ids) # 模型推理得到logits
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# 使用 Softmax 转换 logits 为概率
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probabilities = F.softmax(logits, axis=1) # 归一化 logits 得到概率分布
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# 获取最大概率的标签
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max_prob_idx = np.argmax(probabilities.numpy(), axis=1)
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max_prob_value = np.max(probabilities.numpy(), axis=1)
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# 输出预测结果
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print(f"Predicted label: {max_prob_idx}")
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print(f"Predicted label: {max_prob_value}")
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