如何使用Spacy mini Batch和GoldParse来训练使用BILUO标签方案的NER模型？

How can we use Spacy minibatch and GoldParse to train NER model using BILUO tagging scheme?(如何使用Spacy mini Batch和GoldParse来训练使用BILUO标签方案的NER模型？)

问题描述

我对Spacy NER模型的输入数据在BILUO标记方案中，我希望将其用作某些需求的一部分。当我试着在没有小批量的情况下简单地训练模型时，它工作得很好(注释部分)。但是我不知道如何在这里使用MiniBatch和GoldParse来提高模型的精度。我的期望在这里是有效的，因为我找不到一个具有这种组合的例子？此外，我已经用开始、结束、标签格式的方法训练了模型。请帮我弄清楚这一节。我的代码如下

import spacy
from spacy.gold import offsets_from_biluo_tags
from spacy.gold import biluo_tags_from_offsets
import random
from spacy.util import minibatch, compounding
from os import path
from tqdm import tqdm


def train_spacy(data, iterations, model=None):
    TRAIN_DATA = data
    print(f"downloads = {model}")
    if model is not None and path.exists(model):
        print(f"training existing model")
        nlp = spacy.load(model)
        print("Model is Loaded '%s'" % model)
    else:
        print(f"Creating new model")

        nlp = spacy.blank('en')  # create blank Language class

    if 'ner' not in nlp.pipe_names:
        ner = nlp.create_pipe('ner')
        nlp.add_pipe(ner, last=True)
    else:
        ner = nlp.get_pipe('ner')

    # Based on template, get labels and save those for further training
    LABEL = ["Name", "ORG"]

    for i in LABEL:
        # print(i)
        ner.add_label(i)

    # get names of other pipes to disable them during training
    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
    with nlp.disable_pipes(*other_pipes):  # only train NER
        if model is None:
            optimizer = nlp.begin_training()
        else:
            optimizer = nlp.entity.create_optimizer()
        tags = dict()
        for itn in range(iterations):
            print("Starting iteration " + str(itn))
            random.shuffle(TRAIN_DATA)
            losses = {}
            # for text, annotations in tqdm(TRAIN_DATA):
            #     print(f"text={text}, an={annotations}")
            #     tags['entities'] = offsets_from_biluo_tags(nlp(text), annotations)
            #     print(f"a={tags}")
            #     nlp.update([text],  # batch of texts
            #                [tags],  # batch of annotations
            #                drop=0.5,  # dropout - make it harder to memorise data
            #                sgd=optimizer,  # callable to update weights
            #                losses=losses)
            # print(losses)
            batches = minibatch(TRAIN_DATA, size=compounding(4.0, 16.0, 1.001))
            # type 2 with mini batch
            for batch in batches:
                texts, annotations = zip(*batch)
                print(texts)
                tags = {'entities': annotations}
                nlp.update(
                    texts,  # batch of texts
                    [tags],  # batch of annotations
                    drop=0.4,  # dropout - make it harder to memorise data
                    losses=losses,
                    sgd=optimizer
                )
            print(losses)
    return nlp

data_biluo = [
    ('I am Shah Khan, I work in MS Co', ['O', 'O', 'B-Name', 'L-Name', 'O', 'O', 'O', 'B-ORG', 'L-ORG']),
    ('I am Tom Tomb, I work in Telecom Networks', ['O', 'O', 'B-Name', 'L-Name', 'O', 'O', 'O', 'B-ORG', 'L-ORG'])
]


model = train_spacy(data_biluo, 10)
model.to_disk('./Vectors/')

推荐答案

您的微型批次有两个问题：

1. tags应为带偏移量的NER标签的可迭代
2. 您的data_biluo没有说明句子中间的,。

一旦你纠正了这些错误，你就可以走了：

import spacy
from spacy.gold import offsets_from_biluo_tags, GoldParse
from spacy.util import minibatch, compounding
import random
from tqdm import tqdm

def train_spacy(data, iterations, model=None):
    TRAIN_DATA = data
    print(f"downloads = {model}")
    if model is not None and path.exists(model):
        print(f"training existing model")
        nlp = spacy.load(model)
        print("Model is Loaded '%s'" % model)
    else:
        print(f"Creating new model")

        nlp = spacy.blank('en')  # create blank Language class

    if 'ner' not in nlp.pipe_names:
        ner = nlp.create_pipe('ner')
        nlp.add_pipe(ner, last=True)
    else:
        ner = nlp.get_pipe('ner')

    # Based on template, get labels and save those for further training
    LABEL = ["Name", "ORG"]

    for i in LABEL:
        # print(i)
        ner.add_label(i)

    # get names of other pipes to disable them during training
    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
    with nlp.disable_pipes(*other_pipes):  # only train NER
        if model is None:
            optimizer = nlp.begin_training()
        else:
            optimizer = nlp.entity.create_optimizer()
        tags = dict()
        for itn in range(iterations):
            print("Starting iteration " + str(itn))
            random.shuffle(TRAIN_DATA)
            losses = {}
            batches = minibatch(TRAIN_DATA, size=compounding(4.0, 16.0, 1.001))
            # type 2 with mini batch
            for batch in batches:
                texts, _ = zip(*batch)
                golds = [GoldParse(nlp.make_doc(t),entities = a) for t,a in batch]
                nlp.update(
                    texts,  # batch of texts
                    golds,  # batch of annotations
                    drop=0.4,  # dropout - make it harder to memorise data
                    losses=losses,
                    sgd=optimizer
                )
            print(losses)
    return nlp

data_biluo = [
    ('I am Shah Khan, I work in MS Co', ['O', 'O', 'B-Name', 'L-Name', 'O', 'O', 'O', 'O', 'B-ORG', 'L-ORG']),
    ('I am Tom Tomb, I work in Telecom Networks', ['O', 'O', 'B-Name', 'L-Name', 'O', 'O', 'O', 'O', 'B-ORG', 'L-ORG'])
]


model = train_spacy(data_biluo, 10)

Starting iteration 0
{'ner': 17.999998331069946}
Starting iteration 1
{'ner': 16.6766300201416}
Starting iteration 2
{'ner': 16.997647166252136}
Starting iteration 3
{'ner': 16.486496448516846}
Starting iteration 4
{'ner': 15.695325374603271}
Starting iteration 5
{'ner': 14.312554001808167}
Starting iteration 6
{'ner': 12.099276185035706}
Starting iteration 7
{'ner': 11.473928153514862}
Starting iteration 8
{'ner': 8.814643770456314}
Starting iteration 9
{'ner': 7.233813941478729}

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