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tokenize新上映_tokenizer返回结果(2024年11月抢先看)

内容来源：卡姆驱动平台所属栏目：导读更新日期：2024-11-29

tokenize

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𐟓–Python轻松统计句子数量𐟓ˆ 𐟤”想知道一段话里有多少个句子吗？Python来帮你！首先，我们需要对文本进行语料清洗，把一些不需要的符号或者格式去掉。𐟧𜧄𖥐Ž，我们可以使用nltk这个强大的自然语言处理库来进行句子切分。𐟔 𐟎‰举个例子，如果我们有一段文本，我们可以先用Python进行清洗，然后使用nltk的sent_tokenize函数来切分句子。这样，我们就能轻松地知道这段文本中有多少个句子了！𐟎‰ 𐟒ᦘ露是很简单呢？快来试试吧！如果你还有其他问题或者需要更详细的代码示例，欢迎随时提问哦！𐟒က

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𐟓š8步打造AI文本分析模型✨ 𐟓–想要制作AI文本分析模型？跟着这8个简单步骤，轻松搭建文本分析框架！ 1️⃣ 导入所需库： ```python import pandas as pd import numpy as np import string from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score, classification_report ``` 2️⃣ 数据预处理： ```python # 加载数据集 df = pd.read_csv('text_dataset.csv') # 定义清洗函数 def clean_text(text): text = text.lower() text = text.translate(str.maketrans('', '', string.punctuation)) words = word_tokenize(text) stop_words = set(stopwords.words('english')) words = [word for word in words if word not in stop_words] cleaned_text = ' '.join(words) return cleaned_text # 应用清洗函数到每条文本数据 df['cleaned_text'] = df['text'].apply(clean_text) # 打印处理后的数据以供检查 print(df['cleaned_text'].head()) ``` 3️⃣ 特征提取： ```python # 将处理过的文本转换为数值特征向量 vectorizer = CountVectorizer() X = vectorizer.fit_transform(df['cleaned_text']) ``` 4️⃣ 划分数据集： ```python # 将数据集分为训练集和测试集 y = df['label'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5) ``` 5️⃣ 选择模型：基于问题选择合适的算法，例如MultinomialNB等。 6️⃣ 训练模型：使用训练集数据训练模型，例如`model.fit(X_train, y_train)`。 7️⃣ 测试模型：使用测试集评估模型性能，例如`predictions = model.predict(X_test)`。 8️⃣ 结果评估：使用各种指标评估模型性能，例如`accuracy_score(y_test, predictions)`和`classification_report(y_test, predictions)`。

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