NLTK库主要用于自然语言处理任务，不直接提供计算模型评估指标的功能。一般来说，要计算模型评估指标，可以使用其他库如scikit-learn或者直接编写代码来计算。以下是一个示例代码，演示如何使用scikit-learn库计算模型评估指标：
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_scorefrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LogisticRegressionimport nltkfrom nltk.corpus import movie_reviews# Load movie reviews datasetnltk.download('movie_reviews')documents = [(list(movie_reviews.words(fileid)), category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)]text = [" ".join(document) for document, category in documents]labels = [category for document, category in documents]# Vectorize the text datafrom sklearn.feature_extraction.text import CountVectorizervectorizer = CountVectorizer()X = vectorizer.fit_transform(text)# Split the data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)# Train a logistic regression modelmodel = LogisticRegression()model.fit(X_train, y_train)# Make predictions on the test sety_pred = model.predict(X_test)# Calculate evaluation metricsaccuracy = accuracy_score(y_test, y_pred)precision = precision_score(y_test, y_pred, average='macro')recall = recall_score(y_test, y_pred, average='macro')f1 = f1_score(y_test, y_pred, average='macro')print(f"Accuracy: {accuracy}")print(f"Precision: {precision}")print(f"Recall: {recall}")print(f"F1 Score: {f1}")上述代码使用scikit-learn库加载电影评论数据集，训练了一个逻辑回归模型，并计算了准确率、精确率、召回率和F1分数等模型评估指标。您可以根据实际需求修改代码以适应不同的数据集和模型。
