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
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：
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 .