帮我写一段完整的代码，使用glove与训练模型文本分类

好的，以下是一个基于GloVe的文本分类的Python代码示例：

首先，我们需要安装所需的Python库，包括numpy，scikit-learn和nltk：

!pip install numpy
!pip install scikit-learn
!pip install nltk

然后加载所需的库：

import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import nltk
nltk.download('stopwords')
nltk.download('punkt')

接下来，我们需要加载预训练的GloVe向量：

def load_glove_vectors(glove_file):
    word_vectors = {}
    with open(glove_file, encoding="utf8") as f:
        for line in f:
            values = line.split()
            word = values[0]
            vector = np.asarray(values[1:], dtype='float32')
            word_vectors[word] = vector
    return word_vectors

glove_file = "glove.6B.100d.txt" # 请提前下载GloVe文件
word_vectors = load_glove_vectors(glove_file)

然后，我们需要准备训练数据和测试数据：

def prepare_data():
    # 准备数据
    train_data = [
        ("I love this sandwich.", "positive"),
        ("This is an amazing place!", "positive"),
        ("I feel very good about these beers.", "positive"),
        ("This is my best work.", "positive"),
        ("What an awesome view", "positive"),
        ("I do not like this restaurant", "negative"),
        ("I am tired of this stuff.", "negative"),
        ("I can't deal with this", "negative"),
        ("He is my sworn enemy!", "negative"),
        ("My boss is horrible.", "negative")
    ]

    test_data = [
        ("The beer was good.", "positive"),
        ("I do not enjoy my job", "negative"),
        ("I ain't feeling dandy today.", "negative"),
        ("I feel amazing!", "positive"),
        ("Gary is a friend of mine.", "positive"),
        ("I can't believe I'm doing this.", "negative")
    ]

    train_corpus = [x[0] for x in train_data]
    train_labels = [x[1] for x in train_data]
    test_corpus = [x[0] for x in test_data]
    test_labels = [x[1] for x in test_data]

    return train_corpus, train_labels, test_corpus, test_labels

train_corpus, train_labels, test_corpus, test_labels = prepare_data()

然后，我们需要将文本数据转换为GloVe向量表示：

def text_to_vector(text):
    stop_words = set(stopwords.words('english'))
    words = word_tokenize(text.lower())
    words = [w for w in words if not w in stop_words]
    vector = np.zeros(100)
    count = 0
    for word in words:
        if word in word_vectors:
            vector += word_vectors[word]
            count += 1
    if count != 0:
        vector /= count
    return vector

def corpus_to_vectors(corpus):
    return np.array([text_to_vector(text) for text in corpus])

X_train = corpus_to_vectors(train_corpus)
X_test = corpus_to_vectors(test_corpus)

接下来，我们可以使用分类器对数据进行分类，这里我们使用了SVM作为分类器：

from sklearn.svm import SVC

def train_classifier(X_train, y_train):
    classifier = SVC(kernel='linear', probability=True)
    classifier.fit(X_train, y_train)
    return classifier

classifier = train_classifier(X_train, train_labels)

最后，我们可以对测试数据进行预测并计算分类准确率：

def test_classifier(classifier, X_test, y_test):
    y_pred = classifier.predict(X_test)
    return accuracy_score(y_test, y_pred)

accuracy = test_classifier(classifier, X_test, test_labels)
print("Accuracy:", accuracy)

完整代码如下：