如何通过spacy重新训练模型？

Question

Earlier token 'Modi' is recognised as an Org by spacy to I retrain it with the following code:

import spacy 
import random
nlp = spacy.load('en')
nlp.entity.add_label('CELEBRITY')
TRAIN_DATA = [
        (u"Modi", {"entities": [(0, 4, "PERSON")]}),
        (u"India", {"entities": [(0, 5, "GPE")]})]

optimizer = nlp.begin_training()
for i in range(20):
    random.shuffle(TRAIN_DATA)
    for text, annotations in TRAIN_DATA:
        nlp.update([text], [annotations],drop=0.3, sgd=optimizer)


text = "But Modi is starting India. The company made a late push\ninto hardware, and Apple’s Siri and Google available on iPhones, and Amazon’s Alexa\nsoftware, which runs on its Echo and Dot devices, have clear leads in\nconsumer adoption."
doc = nlp(text)
for ent in doc.ents:
    print(ent.text,ent.label_)

And I got the following answer:

Modi PERSON
India GPE
Apple’s Siri ORG
Google ORG
iPhones ORG
Amazon GPE
Echo PERSON
Dot PERSON

It changes the Modi to the person at the same time it doing incorrect NER as compare to the previous mode. In the previous model, Amazon was recognized as ORG but now change to GPE. Now I add the extra-label CELEBRITY and categorize Modi to CELEBRITY with this following code

import spacy 
import random
nlp = spacy.load('en')
nlp.entity.add_label('CELEBRITY')
TRAIN_DATA = [
        (u"Modi", {"entities": [(0, 4, "CELEBRITY")]})]

optimizer = nlp.begin_training()
for i in range(20):
    random.shuffle(TRAIN_DATA)
    for text, annotations in TRAIN_DATA:
        nlp.update([text], [annotations],drop=0.3, sgd=optimizer)


text = "But Modi is starting India. The company made a late push\ninto hardware, and Apple’s Siri and Google available on iPhones, and Amazon’s Alexa\nsoftware, which runs on its Echo and Dot devices, have clear leads in\nconsumer adoption."
doc = nlp(text)
for ent in doc.ents:
    print(ent.text,ent.label_)

But looks like it crashes my model and getting the following result:

But CELEBRITY
Modi CELEBRITY
is CELEBRITY
starting CELEBRITY
India GPE
. CELEBRITY
The CELEBRITY
company CELEBRITY
made CELEBRITY
a CELEBRITY
late CELEBRITY
push CELEBRITY
into CELEBRITY
hardware CELEBRITY
, CELEBRITY
and CELEBRITY
Apple CELEBRITY

Please let me know the behind the seen reason and also how can I achieve that only entity which I label should change while all other should be according to spacy.

Answer 1

您应该在训练数据中提及句子中存在的所有实体，而不仅仅是新实体。