Question

您好，我正在使用新的SpaCy模型en_pytt_bertbaseuncased_lg实现一个多分类模型（5个类）。新管道的代码在这里：

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")

培训代码如下，并基于此处的示例（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)

所以我的数据结构如下：

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

我不确定为什么会出现以下错误：

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'

编辑：

如果我删除了nr_class参数，则会在此出现此错误：

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

我实际上以为会发生这种情况，因为我没有指定nr_class参数。那是对的吗？

Answer 1

这是我们发布的'&::before': { content: '""', }最新版本的回归。抱歉！

根本原因是，这是spacy-pytorch-transformers邪恶的另一种情况。我期待改进spaCy API，以防止将来出现这些问题。

您可以在此处查看违规行：https://github.com/explosion/spacy-pytorch-transformers/blob/c1def95e1df783c69bff9bc8b40b5461800e9231/spacy_pytorch_transformers/pipeline/textcat.py#L71。我们提供了**kwargs位置参数，它与您在配置期间传递的显式参数重叠。

要变通解决此问题，您只需从传递给nr_class的{{1}}字典中删除nr_class键即可。

Model（）为参数'nr_class'获取了多个值-SpaCy多分类模型（BERT集成）

1 个答案: