zero-shot文字分类模型

我们选择facebook的bart-large-mnli模型做分类处理，bart-large-mnli是基于自然语言推理(Natural Language Inference)的零次文字分类模型。

首先需要安装Huggingface的transformers.

pip install transformers

Huggingface的介绍页面：https://huggingface.co/facebook/bart-large-mnli。
如下示例代码，来判断 "one day I will see the world"这句话，属于三个分类中的哪一个（travel，cooking，dancing）：

from transformers import pipeline

zero_shot_classifier = pipeline(
    "zero-shot-classification",
    model="facebook/bart-large-mnli"
)

sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']

classfication = zero_shot_classifier(
    sequence_to_classify,
    candidate_labels,
    multi_label=True      # 可匹配多个label分类
)
# print(classfication)

print(classfication['labels'])
print(classfication['scores'])

运行之后，得到如下结果，travel标签最为匹配，得分为0.99：

['travel', 'dancing', 'cooking']
[0.994511067867279, 0.005706176161766052, 0.0018192899879068136]

更换一组如下数据：

sequence_to_classify = "The company's quarterly earnings increased by 20%, exceeding market expectations."
candidate_labels = ["finance", "sports", "politics", "technology"]

得到结果如下，finance标签最为匹配，得分0.85：

['finance', 'technology', 'sports', 'politics']
[0.8526014089584351, 0.5210740566253662, 0.004615711513906717, 0.000885962916072458]

这次，给定一个网址，而非之前的自然语言，测试下分类情况：

sequence_to_classify = "https://nba.sina.com.cn/"

得到结果如下，sports标签最为匹配，得分为0.83。

['sports', 'technology', 'finance', 'politics']
[0.8341358304023743, 0.20186419785022736, 0.07376285642385483, 0.004406137391924858]