用于文本分类的训练空间

问题描述

阅读docs并执行tutorial后，我想我应该做一个小演示。结果我的模特不想训练。以下是代码

import spacy
import random
import json

TRAINING_DATA = [
    ["My little kitty is so special", {"KAT": True}],
    ["Dude, Totally, Yeah, Video Games", {"KAT": False}],
    ["Should I pay $1,000 for the iPhone X?", {"KAT": False}],
    ["The iPhone 8 reviews are here", {"KAT": False}],
    ["Noa is a great cat name.", {"KAT": True}],
    ["We got a new kitten!", {"KAT": True}]
]

nlp = spacy.blank("en")
category = nlp.create_pipe("textcat")
nlp.add_pipe(category)
category.add_label("KAT")

# Start the training
nlp.begin_training()

# Loop for 10 iterations
for itn in range(100):
    # Shuffle the training data
    random.shuffle(TRAINING_DATA)
    losses = {}

    # Batch the examples and iterate over them
    for batch in spacy.util.minibatch(TRAINING_DATA, size=2):
        texts = [text for text, entities in batch]
        annotations = [{"textcat": [entities]} for text, entities in batch]
        nlp.update(texts, annotations, losses=losses)
    if itn % 20 == 0:
        print(losses)

当我运行此命令时，输出表明学习的内容很少。

{'textcat': 0.0}
{'textcat': 0.0}
{'textcat': 0.0}
{'textcat': 0.0}
{'textcat': 0.0}

这感觉不对。应该有错误或有意义的标记。预测证实了这一点。

for text, d in TRAINING_DATA:
    print(text, nlp(text).cats)

# Dude, Totally, Yeah, Video Games {'KAT': 0.45303162932395935}
# The iPhone 8 reviews are here {'KAT': 0.45303162932395935}
# Noa is a great cat name. {'KAT': 0.45303162932395935}
# Should I pay $1,000 for the iPhone X? {'KAT': 0.45303162932395935}
# We got a new kitten! {'KAT': 0.45303162932395935}
# My little kitty is so special {'KAT': 0.45303162932395935}

感觉我的代码遗漏了一些东西，但我搞不清楚是什么。

---

解决方案

如果您更新并使用Spacy 3-上面的代码将不再工作。解决方案是在进行一些更改后进行迁移。我已相应地修改了cantdutchthis中的示例。

更改摘要：

• 使用配置更改架构。旧的默认设置是词袋，新的默认设置是使用注意力的文本集合。在调整模型时请记住这一点
• 标签现在需要一次性编码
• add_pipe接口稍有更改
• nlp.update现在需要Example对象，而不是text、annotation
的元组

import spacy
# Add imports for example, as well as textcat config...
from spacy.training import Example
from spacy.pipeline.textcat import single_label_bow_config, single_label_default_config
from thinc.api import Config
import random

# labels should be one-hot encoded
TRAINING_DATA = [
    ["My little kitty is so special", {"KAT0": True}],
    ["Dude, Totally, Yeah, Video Games", {"KAT1": True}],
    ["Should I pay $1,000 for the iPhone X?", {"KAT1": True}],
    ["The iPhone 8 reviews are here", {"KAT1": True}],
    ["Noa is a great cat name.", {"KAT0": True}],
    ["We got a new kitten!", {"KAT0": True}]
]


# bow
# config = Config().from_str(single_label_bow_config)

# textensemble with attention
config = Config().from_str(single_label_default_config)

nlp = spacy.blank("en")
# now uses `add_pipe` instead
category = nlp.add_pipe("textcat", last=True, config=config)
category.add_label("KAT0")
category.add_label("KAT1")


# Start the training
nlp.begin_training()

# Loop for 10 iterations
for itn in range(100):
    # Shuffle the training data
    random.shuffle(TRAINING_DATA)
    losses = {}

    # Batch the examples and iterate over them
    for batch in spacy.util.minibatch(TRAINING_DATA, size=4):
        texts = [nlp.make_doc(text) for text, entities in batch]
        annotations = [{"cats": entities} for text, entities in batch]

        # uses an example object rather than text/annotation tuple
        examples = [Example.from_dict(doc, annotation) for doc, annotation in zip(
            texts, annotations
        )]
        nlp.update(examples, losses=losses)
    if itn % 20 == 0:
        print(losses)