用于情感分析的 Hugging Face 管道

Python 中的自然语言处理（NLP）

Fouad Trad

Machine Learning Engineer

回顾：NLP 工作流

完整工作流图，标注第 1 章覆盖预处理，第 2 章覆盖特征提取。

Hugging Face 管道

完整工作流图，说明第 3、4 章将介绍 Hugging Face 管道，其封装了预处理、特征提取和建模。

现成流程：一次函数调用完成全部步骤
定义管道需指定：
- NLP 任务
- 执行该任务的模型

用于情感分析的管道

文本分类任务
预测文本表达积极或消极情绪

图片显示：积极情绪为笑脸和点赞，消极情绪为哭脸和倒拇指。

文本分类的模型

一个动图，演示如何在网站中滚动并选择合适的任务以筛选模型。

¹ https://huggingface.co/models

代码中的管道

from transformers import pipeline


classification_pipeline = pipeline(

  task="sentiment-analysis", # or text-classification

  model="distilbert/distilbert-base-uncased-finetuned-sst-2-english" 
)

result = classification_pipeline("I really liked the movie!!")

print(result)

[{'label': 'POSITIVE', 'score': 0.9998093247413635}]

批量文本的情感分析

texts = ["I really liked the movie!!",
         "Great job ruining my day.",
         "This product exceeded my expectations.",
         "Wow, just what I needed... another problem.", 
         "Absolutely fantastic experience!"]

results = classification_pipeline(texts)

print(results)

[{'label': 'POSITIVE', 'score': 0.9998093247413635}, 
 {'label': 'NEGATIVE', 'score': 0.8666700124740601}, 
 {'label': 'POSITIVE', 'score': 0.998874843120575}, 
 {'label': 'POSITIVE', 'score': 0.98626708984375}, 
 {'label': 'POSITIVE', 'score': 0.9998812675476074}]

评估情感分析模型

texts = ["I really liked the movie!!",
         "Great job ruining my day.",
         "This product exceeded my expectations.",
         "Wow, just what I needed... another problem.", 
         "Absolutely fantastic experience!"]

true_labels = ["POSITIVE", "NEGATIVE", "POSITIVE", "NEGATIVE", "POSITIVE"]

results = classification_pipeline(texts)

predicted_labels = [result['label'] for result in results]

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(true_labels, predicted_labels)

print(f"Accuracy: {accuracy}")

Accuracy: 0.80

Passons à la pratique !

Python 中的自然语言处理（NLP）