python - 如何从 Spacy NER 模型中获得每个实体的预测概率？

Question

我用过这个 official example code使用我自己的训练样本从头开始训练 NER 模型。

当我在新文本上使用此模型进行预测时，我想获得每个实体的预测概率。

    # test the saved model
    print("Loading from", output_dir)
    nlp2 = spacy.load(output_dir)
    for text, _ in TRAIN_DATA:
        doc = nlp2(text)
        print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
        print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc])

我无法在 Spacy 中找到一种方法来获得每个实体的预测概率。

我如何从 Spacy 获得这个概率？我需要它来应用截止。

Answer 1

从 Spacy NER 模型中获取每个实体的预测概率并非易事。
这是改编自 here 的解决方案:

import spacy
from collections import defaultdict

texts = ['John works at Microsoft.']

# Number of alternate analyses to consider. More is slower, and not necessarily better -- you need to experiment on your problem.
beam_width = 16
# This clips solutions at each step. We multiply the score of the top-ranked action by this value, and use the result as a threshold. This prevents the parser from exploring options that look very unlikely, saving a bit of efficiency. Accuracy may also improve, because we've trained on greedy objective.
beam_density = 0.0001 
nlp = spacy.load('en_core_web_md')


docs = list(nlp.pipe(texts, disable=['ner']))
beams = nlp.entity.beam_parse(docs, beam_width=beam_width, beam_density=beam_density)

for doc, beam in zip(docs, beams):
    entity_scores = defaultdict(float)
    for score, ents in nlp.entity.moves.get_beam_parses(beam):
        for start, end, label in ents:
            entity_scores[(start, end, label)] += score

l= []
for k, v in entity_scores.items():
    l.append({'start': k[0], 'end': k[1], 'label': k[2], 'prob' : v} )

for a in sorted(l, key= lambda x: x['start']):
    print(a)

### Output: ####

{'start': 0, 'end': 1, 'label': 'PERSON', 'prob': 0.4054479906820232}
{'start': 0, 'end': 1, 'label': 'ORG', 'prob': 0.01002015005487447}
{'start': 0, 'end': 1, 'label': 'PRODUCT', 'prob': 0.0008592912552754791}
{'start': 0, 'end': 1, 'label': 'WORK_OF_ART', 'prob': 0.0007666755792166002}
{'start': 0, 'end': 1, 'label': 'NORP', 'prob': 0.00034931990870877333}
{'start': 0, 'end': 1, 'label': 'TIME', 'prob': 0.0002786051849320804}
{'start': 3, 'end': 4, 'label': 'ORG', 'prob': 0.9990115861687987}
{'start': 3, 'end': 4, 'label': 'PRODUCT', 'prob': 0.0003378157477046507}
{'start': 3, 'end': 4, 'label': 'FAC', 'prob': 8.249734411749544e-05}