如何找到我的word2vec模型的准确性、精确度、召回率、f1分数？

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import numpy as np

# 假设你已经有了Word2Vec模型，并且已经将文本转换为词向量
# X是特征矩阵，y是标签向量
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 使用逻辑回归作为分类器
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

# 预测测试集
y_pred = classifier.predict(X_test)

# 计算指标
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')
f1 = f1_score(y_test, y_pred, average='weighted')

print(f"Accuracy: {accuracy}")
print(f"Precision: {precision}")
print(f"Recall: {recall}")
print(f"F1 Score: {f1}")