python - Spacy 自定义名称实体识别 (NER) 'catastrophic forgetting' 问题

Question

模型无法记住之前训练的标签 我知道这是“灾难性的遗忘”，但似乎没有例子或博客可以帮助解决这个问题。 对此最常见的回应是这个博客是这个 https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting但这现在已经很旧了，没有帮助

这是我的代码:

from __future__ import unicode_literals, print_function
import json
labeled_data = []
with open(r"/content/emails_labeled.jsonl", "r") as read_file:
    for line in read_file:
        data = json.loads(line)
        labeled_data.append(data)

TRAIN_DATA = []
for entry in labeled_data:
    entities = []
    for e in entry['labels']:
        entities.append((e[0], e[1],e[2]))
    spacy_entry = (entry['text'], {"entities": entities})
    TRAIN_DATA.append(spacy_entry)       
import plac
import random
import warnings
from pathlib import Path
import spacy
from spacy.util import minibatch, compounding


# new entity label
LABEL = "OIL"

# training data
# Note: If you're using an existing model, make sure to mix in examples of
# other entity types that spaCy correctly recognized before. Otherwise, your
# model might learn the new type, but "forget" what it previously knew.
# https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting
'''
TRAIN_DATA = [
    (
        "Horses are too tall and they pretend to care about your feelings",
        {"entities": [(0, 6, LABEL)]},
    ),
    ("Do they bite?", {"entities": []}),
    (
        "horses are too tall and they pretend to care about your feelings",
        {"entities": [(0, 6, LABEL)]},
    ),
    ("horses pretend to care about your feelings", {"entities": [(0, 6, LABEL)]}),
    (
        "they pretend to care about your feelings, those horses",
        {"entities": [(48, 54, LABEL)]},
    ),
    ("horses?", {"entities": [(0, 6, LABEL)]}),
]
'''

@plac.annotations(
    model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
    new_model_name=("New model name for model meta.", "option", "nm", str),
    output_dir=("Optional output directory", "option", "o", Path),
    n_iter=("Number of training iterations", "option", "n", int),
)
def main(model='/content/LinkModelOutput', new_model_name="Oil21", output_dir='/content/Last', n_iter=30):
    """Set up the pipeline and entity recognizer, and train the new entity."""
    random.seed(0)
    if model is not None:
        nlp = spacy.load(model)  # load existing spaCy model
        print("Loaded model '%s'" % model)
    else:
        nlp = spacy.blank("en")  # create blank Language class
        print("Created blank 'en' model")
    # Add entity recognizer to model if it's not in the pipeline
    # nlp.create_pipe works for built-ins that are registered with spaCy
    if "ner" not in nlp.pipe_names:
        ner = nlp.create_pipe("ner")
        nlp.add_pipe(ner)
    # otherwise, get it, so we can add labels to it
    else:
        ner = nlp.get_pipe("ner")

    ner.add_label(LABEL)  # add new entity label to entity recognizer
    # Adding extraneous labels shouldn't mess anything up
    #ner.add_label("VEGETABLE")
    if model is None:
        optimizer = nlp.begin_training()
    else:
        optimizer = nlp.resume_training()
    move_names = list(ner.move_names)
    # get names of other pipes to disable them during training
    pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
    other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
    # only train NER
    with nlp.disable_pipes(*other_pipes), warnings.catch_warnings():
        # show warnings for misaligned entity spans once
        warnings.filterwarnings("once", category=UserWarning, module='spacy')

        sizes = compounding(1.0, 4.0, 1.001)
        # batch up the examples using spaCy's minibatch
        for itn in range(n_iter):
            random.shuffle(TRAIN_DATA)
            batches = minibatch(TRAIN_DATA, size=sizes)
            losses = {}
            for batch in batches:
                texts, annotations = zip(*batch)
                nlp.entity.update(texts, annotations, sgd=optimizer, drop=0.35, losses=losses)
            print("Losses", losses)

    # test the trained model
    test_text = "Here is Hindustan petroleum's oil reserves coup in Australia. Details can be found at https://www.textfixer.com/tools/remove-line-breaks.php?"
    doc = nlp(test_text)
    print("Entities in '%s'" % test_text)
    for ent in doc.ents:
        print(ent.label_, ent.text)

    # save model to output directory
    if output_dir is not None:
        output_dir = Path(output_dir)
        if not output_dir.exists():
            output_dir.mkdir()
        nlp.meta["name"] = new_model_name  # rename model
        nlp.to_disk(output_dir)
        print("Saved model to", output_dir)

        # test the saved model
        print("Loading from", output_dir)
        nlp2 = spacy.load(output_dir)
        # Check the classes have loaded back consistently
        assert nlp2.get_pipe("ner").move_names == move_names
        doc2 = nlp2(test_text)
        for ent in doc2.ents:
            print(ent.label_, ent.text)


if __name__ == "__main__":
    plac.call(main)

并且数据注释是在“Daccano”上完成的。 看一下数据:

{"id": 174, "text": "service\tmarathon petroleum reduces service postings marathon petroleum co said it reduced the contract price it will pay for all grades of service oil one dlr a barrel effective today the decrease brings marathon s posted price for both west texas intermediate and west texas sour to dlrs a bbl the south louisiana sweet grade of service was reduced to dlrs a bbl the company last changed its service postings on jan reuter", "meta": {}, "annotation_approver": null, "labels": [[61, 70, "OIL"], [147, 150, "OIL"]]}
{"id": 175, "text": "mutual funds\tmunsingwear inc mun th qtr jan loss shr loss cts vs loss seven cts net loss vs loss revs mln vs mln year shr profit cts vs profit cts net profit vs profit revs mln vs mln avg shrs vs note per shr adjusted for for stock split july and for split may reuter", "meta": {}, "annotation_approver": null, "labels": []}

Answer 1

我不是spacy专家，但我也遇到了同样的问题。有一些要点是必要的:注释工具、训练数据量、正确预测实体的混合。 首先确保您的训练数据已由您选择的工具正确标记(您不会收到用户警告)。为了获得良好的预测，您的模型需要大量数据。这意味着您要训练的每个实体至少有 200 个示例。我个人标记尽可能多的数据。 spacy 的制作者建议混合您的模型正确预测的实体。