Question

我正在使用spacy 2.1.3

我想将自定义实体添加到模型中。我使用Gensim的word2vec创建了模型：

python -m spacy初始化模型C：\ myproject \ gcmodel -v gcword2vec.txt

然后，我要按照文档中给出的示例对自定义实体数据进行培训：

def main(model=None,output_dir=None, n_iter=100):
    """Load the model, set up the pipeline and train the entity recognizer."""
    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")

    # create the built-in pipeline components and add them to 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, last=True)
    # otherwise, get it so we can add labels
    else:
        ner = nlp.get_pipe("ner")

    prepareTrainingData()   # Our extension to create training data

    # add labels
    for _, annotations in TRAIN_DATA:
        for ent in annotations.get("entities"):
            ner.add_label(ent[2])

    # 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
        # reset and initialize the weights randomly – but only if we're
        # training a new model
        if model is None:
            nlp.begin_training()

        for itn in range(n_iter):
            random.shuffle(TRAIN_DATA)
            losses = {}
            # batch up the examples using spaCy's minibatch
            batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
            for batch in batches:
                texts, annotations = zip(*batch)
                nlp.update(
                    texts,  # batch of texts
                    annotations,  # batch of annotations
                    drop=0.5,  # dropout - make it harder to memorise data
                    losses=losses,
                )
            print("Losses", losses)
......
.....
}

当我运行它时，它在nlp.update（）调用处中断，并显示错误：

    proc.update(docs, golds, sgd=get_grads, losses=losses, **kwargs)
  File "nn_parser.pyx", line 391, in spacy.syntax.nn_parser.Parser.update
  File "nn_parser.pyx", line 235, in spacy.syntax.nn_parser.Parser.require_model
ValueError: [E109] Model for component 'ner' not initialized. Did you forget to load a model, or forget to call begin_training()?

该模型已在命令行中传递，并且已预先加载。我在做什么错了？

谢谢！

从现有模型故障为NER更新空间模型

0 个答案: