使用 Huggingface 转换器进行命名实体识别，映射回完整实体

我正在查看文档用于命名实体识别的 Huggingface 管道 https://huggingface.co/transformers/task_summary.html#named-entity-recognition，我不清楚这些结果如何用于实际的实体识别模型。

例如，给出文档中的示例：

>>> from transformers import pipeline

>>> nlp = pipeline("ner")

>>> sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very"
...            "close to the Manhattan Bridge which is visible from the window."

This outputs a list of all words that have been identified as an entity from the 9 classes     defined above. Here is the expected results:

print(nlp(sequence))

[
{'word': 'Hu', 'score': 0.9995632767677307, 'entity': 'I-ORG'},
{'word': '##gging', 'score': 0.9915938973426819, 'entity': 'I-ORG'},
{'word': 'Face', 'score': 0.9982671737670898, 'entity': 'I-ORG'},
{'word': 'Inc', 'score': 0.9994403719902039, 'entity': 'I-ORG'},
{'word': 'New', 'score': 0.9994346499443054, 'entity': 'I-LOC'},
{'word': 'York', 'score': 0.9993270635604858, 'entity': 'I-LOC'},
{'word': 'City', 'score': 0.9993864893913269, 'entity': 'I-LOC'},
{'word': 'D', 'score': 0.9825621843338013, 'entity': 'I-LOC'},
{'word': '##UM', 'score': 0.936983048915863, 'entity': 'I-LOC'},
{'word': '##BO', 'score': 0.8987102508544922, 'entity': 'I-LOC'},
{'word': 'Manhattan', 'score': 0.9758241176605225, 'entity': 'I-LOC'},
{'word': 'Bridge', 'score': 0.990249514579773, 'entity': 'I-LOC'}
]

虽然这一点本身就令人印象深刻，但我不清楚从以下位置获取“DUMBO”的正确方法：

{'word': 'D', 'score': 0.9825621843338013, 'entity': 'I-LOC'},
{'word': '##UM', 'score': 0.936983048915863, 'entity': 'I-LOC'},
{'word': '##BO', 'score': 0.8987102508544922, 'entity': 'I-LOC'},

---或者甚至是更清晰的多个标记匹配，例如将“纽约市”与简单的“约克”市区分开来。

虽然我可以想象启发式方法，但根据您的输入将这些标记重新连接到正确的标签中的正确预期方法是什么？

当您设置参数时，管道对象可以为您执行此操作：

• 变压器 : 分组实体 https://huggingface.co/transformers/v4.6.0/main_classes/pipelines.html#transformers.TokenClassificationPipeline to True.
• 变形金刚 >= 4.7.0: 聚合策略 https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.TokenClassificationPipeline.aggregation_strategy to simple

from transformers import pipeline

#transformers < 4.7.0
#ner = pipeline("ner", grouped_entities=True)

ner = pipeline("ner", aggregation_strategy='simple')

sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very close to the Manhattan Bridge which is visible from the window."

output = ner(sequence)

print(output)

Output:

[{'entity_group': 'I-ORG', 'score': 0.9970663785934448, 'word': 'Hugging Face Inc'}
, {'entity_group': 'I-LOC', 'score': 0.9993778467178345, 'word': 'New York City'}
, {'entity_group': 'I-LOC', 'score': 0.9571147759755453, 'word': 'DUMBO'}
, {'entity_group': 'I-LOC', 'score': 0.9838141202926636, 'word': 'Manhattan Bridge'}
, {'entity_group': 'I-LOC', 'score': 0.9838141202926636, 'word': 'Manhattan Bridge'}]