利用置换实现NER训练数据和实体的可视化

visualizing NER training data and entity using displacy(利用置换实现NER训练数据和实体的可视化)

问题描述

我已经为训练NER数据创建了一个数据集。在创建之后，我想在应用到训练管道之前测试实体和数据是否匹配。使用置换，我们可以以更好的方式可视化。而是如何在Spacy 3中实现它。

推荐答案

上述问题的代码如下

import spacy
from spacy import displacy


annot_data = [('A Very SoNA Christmas
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Folder:
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Folder:
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Reimagined Celebration
A Very SoNA Christmas
Jul 10, 2021
Written By SoNA
Saturday, December 11, 2021 2PM 7:30PM Walton Arts Center, Fayetteville
A mix of sacred and secular holiday favorites with local guest soloists, The SoNA Singers, and area high school and collegiate choruses. Saturday, December 11, 2021 2PM Matinee Performance Saturday, December 11, 2021 7:30PM Evening Performance
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Single Tickets: 35, 45, 57 Under 18 FREE with purchase of adult ticket limited quantities Interested in a full season subscription Learn more here . Concert sponsored by Bogle Family Foundation
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  {'entities': [(1958, 1962, 'organization'),
    (1230, 1236, 'performance_starttime'),
    (1343, 1359, 'organization'),
    (1208, 1225, 'performance_date'),
    (1237, 1255, 'auditorium'),
    (0, 21, 'production_name'),
    (1226, 1229, 'performance_starttime')]})]

nlp = spacy.blank('en')
raw_text = annot_data[0][0]
doc = nlp.make_doc(raw_text)
spans = annot_data[0][1]["entities"]
ents = []
for span_start, span_end, label in spans:
    ent = doc.char_span(span_start, span_end, label=label)
    if ent is None:
        continue

    ents.append(ent)

doc.ents = ents
displacy.render(doc, style="ent", jupyter=True)

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