Model() 为参数“nr_class"获得了多个值 - SpaCy 多

Model() got multiple values for argument #39;nr_class#39; - SpaCy multi-classification model (BERT integration)(Model() 为参数“nr_class获得了多个值 - SpaCy 多分类模型(BERT 集成))

问题描述

您好，我正在使用新的 SpaCy 模型 en_pytt_bertbaseuncased_lg 实现多分类模型(5 个类).新管道的代码在这里:

Hi I am working on implementing a multi-classification model (5 classes) with the new SpaCy Model en_pytt_bertbaseuncased_lg. The code for the new pipe is here:

nlp = spacy.load('en_pytt_bertbaseuncased_lg')
textcat = nlp.create_pipe(
    'pytt_textcat',
    config={
        "nr_class":5,
        "exclusive_classes": True,
    }
)
nlp.add_pipe(textcat, last = True)

textcat.add_label("class1")
textcat.add_label("class2")
textcat.add_label("class3")
textcat.add_label("class4")
textcat.add_label("class5")

训练代码如下，基于这里的例子(https://pypi.org/project/spacy-pytorch-transformers/):

The code for the training is as follows and is based on the example from here(https://pypi.org/project/spacy-pytorch-transformers/):

def extract_cat(x):
    for key in x.keys():
        if x[key]:
            return key

# get names of other pipes to disable them during training
n_iter = 250 # number of epochs

train_data = list(zip(train_texts, [{"cats": cats} for cats in train_cats]))


dev_cats_single   = [extract_cat(x) for x in dev_cats]
train_cats_single = [extract_cat(x) for x in train_cats]
cats = list(set(train_cats_single))
recall = {}
for c in cats:
    if c is not None: 
        recall['dev_'+c] = []
        recall['train_'+c] = []



optimizer = nlp.resume_training()
batch_sizes = compounding(1.0, round(len(train_texts)/2), 1.001)

for i in range(n_iter):
    random.shuffle(train_data)
    losses = {}
    batches = minibatch(train_data, size=batch_sizes)
    for batch in batches:
        texts, annotations = zip(*batch)
        nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
    print(i, losses)

所以我的数据结构是这样的:

So the structure of my data looks like this:

[('TEXT TEXT TEXT',
  {'cats': {'class1': False,
    'class2': False,
    'class3': False,
    'class4': True,
    'class5': False}}), ... ]

我不知道为什么会出现以下错误:

I am not sure why I get the following error:

TypeError                                 Traceback (most recent call last)
<ipython-input-32-1588a4eadc8d> in <module>
     21 
     22 
---> 23 optimizer = nlp.resume_training()
     24 batch_sizes = compounding(1.0, round(len(train_texts)/2), 1.001)
     25 

TypeError: Model() got multiple values for argument 'nr_class'

如果我去掉 nr_class 参数，我会在这里得到这个错误:

if I take out the nr_class argument, I get this error here:

ValueError: operands could not be broadcast together with shapes (1,2) (1,5)

我实际上认为这会发生，因为我没有指定 nr_class 参数.那是对的吗?

I actually thought this would happen because I didn't specify the nr_class argument. Is that correct?

推荐答案

这是我们发布的 spacy-pytorch-transformers 最新版本的回归.对不起！

This is a regression in the most recent version we released of spacy-pytorch-transformers. Sorry about this!

根本原因是，这是**kwargs 邪恶的又一案例.我期待着改进 spaCy API 以防止将来出现这些问题.

The root cause is, this is another case of the evils of **kwargs. I'm looking forward to refining the spaCy API to prevent these issues in future.

你可以在这里看到违规行:https://github.com/explosion/spacy-pytorch-transformers/blob/c1def95e1df783c69bff9bc8b40b5461800e9231/spacy_pytorch_transformers/pipeline/textcat.py#L71 .我们提供了 nr_class 位置参数，它与您在配置期间传入的显式参数重叠.

You can see the offending line here: https://github.com/explosion/spacy-pytorch-transformers/blob/c1def95e1df783c69bff9bc8b40b5461800e9231/spacy_pytorch_transformers/pipeline/textcat.py#L71 . We provide the nr_class positional argument, which overlaps with the explicit argument you passed in during the config.

为了解决这个问题，您可以简单地从您传入 spacy.create_pipe()config 的字典中删除 nr_class 键/代码>.