每次迭代后保存spacy的NER模型

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我想在每次迭代后保存到Spacy自定义NER模型 . 我们是否有任何类似于tensorflow中的API,以便在每个/确定没有后保存模型权重 . 迭代 . 然后我可以重新加载该保存的模型并从那里继续训练 .

另外我如何在linux中使用我系统上的所有内核 . 我发现只使用了四个核心中的两个核心 . 他们使用多任务CNN进行NER,我知道这需要更多的时间来重新训练CPU . 还有其他加速NER模型训练的方法 .

@plac.annotations(
    model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
    output_dir=("Optional output directory", "option", "o", Path),
    n_iter=("Number of training iterations", "option", "n", int))
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")

    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')

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

    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
    with nlp.disable_pipes(*other_pipes):  # only train NER
        optimizer = nlp.begin_training()
        for itn in range(n_iter):
            random.shuffle(TRAIN_DATA)
            losses = {}
            for text, annotations in TRAIN_DATA:
                nlp.update(
                    [text],  # batch of texts
                    [annotations],  # batch of annotations
                    drop=0.5,  # dropout - make it harder to memorise data
                    sgd=optimizer,  # callable to update weights
                    losses=losses)
            print(losses)

    # 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.to_disk(output_dir)
        print("Saved model to", output_dir)

if __name__ == '__main__':
    plac.call(main)
1回答

  • 0

    要在每次迭代后保存模型,只需将最后一段代码移动到循环中即可 . 例如:

    @plac.annotations(
        model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
        output_dir=("Optional output directory", "option", "o", Path),
        n_iter=("Number of training iterations", "option", "n", int))
    
    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")
    
        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')
    
        # add labels
        for _, annotations in TRAIN_DATA:
            for ent in annotations.get('entities'):
                ner.add_label(ent[2])
    
        other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
        with nlp.disable_pipes(*other_pipes):  # only train NER
            optimizer = nlp.begin_training()
            for itn in range(n_iter):
                random.shuffle(TRAIN_DATA)
                losses = {}
                for text, annotations in TRAIN_DATA:
                    nlp.update(
                        [text],  # batch of texts
                        [annotations],  # batch of annotations
                        drop=0.5,  # dropout - make it harder to memorise data
                        sgd=optimizer,  # callable to update weights
                        losses=losses)
                print(losses)
                    # save model to output directory
                if output_dir is not None:
                    output_dir = Path(output_dir+str(int))
                    if not output_dir.exists():
                        output_dir.mkdir()
                    nlp.to_disk(output_dir)
                    print("Saved model to", output_dir)
    
    
    
    if __name__ == '__main__':
        plac.call(main)
    

    只需在每个循环中修改字符串,每次都不会覆盖最新的保存 .

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