62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
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import paddle
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from paddlenlp.transformers import ErnieForTokenClassification, ErnieTokenizer
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class SlotRecognition:
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def __init__(self, model_path: str, label_map: dict):
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"""
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初始化槽位识别模型和tokenizer
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:param model_path: 模型路径
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:param label_map: 标签映射字典
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"""
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self.model = ErnieForTokenClassification.from_pretrained(model_path)
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self.tokenizer = ErnieTokenizer.from_pretrained(model_path)
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self.label_map = label_map
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def recognize(self, text: str):
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"""
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对输入的文本进行槽位识别，返回识别出的实体。
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:param text: 输入的文本
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:return: entities 字典，包含识别出的槽位实体
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"""
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# 处理输入文本
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inputs = self.tokenizer(text, max_length=512, return_tensors="pd")
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# 使用无梯度计算
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with paddle.no_grad():
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logits = self.model(**inputs)
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predictions = paddle.argmax(logits, axis=-1)
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# 解析预测结果
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predicted_labels = predictions.numpy()[0]
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tokens = self.tokenizer.convert_ids_to_tokens(inputs["input_ids"][0].numpy())
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entities = {}
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current_entity = None
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current_label = None
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for token, label_id in zip(tokens, predicted_labels):
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label = self.label_map.get(label_id, "O")
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if label.startswith("B-"): # 开始新实体
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if current_entity:
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entities[current_label] = "".join(current_entity)
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current_entity = [token]
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current_label = label[2:] # 去掉 B-
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elif label.startswith("I-") and current_entity and label[2:] == current_label:
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current_entity.append(token) # 继续合并同一实体
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else: # 非实体
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if current_entity:
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entities[current_label] = "".join(current_entity)
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current_entity = None
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current_label = None
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# 处理最后一个实体
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if current_entity:
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entities[current_label] = "".join(current_entity)
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# 对所有实体进行替换：替换每个实体中的 '##' 为 ' '
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for key, value in entities.items():
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entities[key] = value.replace('#', '')
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return entities
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