68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
from paddlenlp.transformers import ErnieForTokenClassification, ErnieTokenizer
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import paddle
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# 1. 加载模型和 tokenizer
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model_path = R"E:\workingSpace\PycharmProjects\Intention_dev\uie\output\checkpoint-16920" # 你的模型路径
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model = ErnieForTokenClassification.from_pretrained(model_path)
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tokenizer = ErnieTokenizer.from_pretrained(model_path)
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# 2. 处理输入文本
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text = "今天杨柳220kV变电站220kV南坪间隔扩建工程有多少作业计划"
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inputs = tokenizer(text, max_len=512, return_tensors="pd")
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# 3. 进行预测
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model.eval()
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with paddle.no_grad():
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logits = model(**inputs)
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predictions = paddle.argmax(logits, axis=-1)
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# 4. 标签映射
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label_map = {
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0: 'O', # 非实体
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1: 'B-date', 12: 'I-date',
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2: 'B-project_name', 13: 'I-project_name',
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3: 'B-project_type', 14: 'I-project_type',
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4: 'B-construction_unit', 15: 'I-construction_unit',
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5: 'B-implementation_organization', 16: 'I-implementation_organization',
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6: 'B-project_department', 17: 'I-project_department',
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7: 'B-project_manager', 18: 'I-project_manager',
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8: 'B-subcontractor', 19: 'I-subcontractor',
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9: 'B-team_leader', 20: 'I-team_leader',
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10: 'B-risk_level', 21: 'I-risk_level',
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11: 'B-page', 22: 'I-page',
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}
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# 5. 解析预测结果
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predicted_labels = predictions.numpy()[0]
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tokens = 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 = 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.append({"text": "".join(current_entity), "label": current_label})
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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.append({"text": "".join(current_entity), "label": current_label})
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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.append({"text": "".join(current_entity), "label": current_label})
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# 输出最终的实体
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for entity in entities:
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print(f"Entity: {entity['text']}, Label: {entity['label']}")
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