list_corpus = df["text"].tolist() list_labels = df["target"].tolist() X_train, X_test, y_train, y_test = train_test_split(list_corpus, list_labels, test_size=0.2, random_state=random_state_split)

这代码用于从DataFrame中提取文本数据和标签，并将它们分别存储在`list_corpus`和`list_labels`中。然后，使用`train_test_split`函数将数据集划分为训练集和测试集，其中训练集的比例为80%，测试集的比例为20%。`X_train`和`X_test`分别存储训练集和测试集的文本数据，`y_train`和`y_test`分别存储训练集和测试集的标签数据。参数`random_state`用于控制数据划分的随机性，确保每次运行代码时得到相同的划分结果。

相关问题

请在注释处填入代码完成对训练集和测试集的结巴分词from paddlenlp.datasets import load_dataset def read(data_path): data_set = [] with open(data_path, 'r', encoding='utf-8') as f: for line in f: l = line.strip('\n').split('\t') if len(l) != 2: print (len(l), line) words, labels = line.strip('\n').split('\t') data_set.append((words,labels)) return data_set train_ds = read(data_path='train.txt') dev_ds = read(data_path='dev.txt') test_ds = read(data_path='test.txt') for i in range(5): print("sentence %d" % (i), train_ds[i][0]) print("sentence %d" % (i), train_ds[i][1]) print(len(train_ds),len(dev_ds)) import jieba def data_preprocess(corpus): data_set = [] ####填结巴分词代码 for text in corpus: seg_list = jieba.cut(text) data_set.append(" ".join(seg_list)) return data_set train_corpus = data_preprocess(train_ds) test_corpus = data_preprocess(test_ds) print(train_corpus[:2]) print(test_corpus[:2])

from paddlenlp.datasets import load_dataset
def read(data_path):
data_set = []
with open(data_path, 'r', encoding='utf-8') as f:
for line in f:
l = line.strip('\n').split('\t')
if len(l) != 2:
print (len(l), line)
words, labels = line.strip('\n').split('\t')
data_set.append((words,labels))
return data_set

train_ds = read(data_path='train.txt')
dev_ds = read(data_path='dev.txt')
test_ds = read(data_path='test.txt')

for i in range(5):
print("sentence %d" % (i), train_ds[i][0])
print("sentence %d" % (i), train_ds[i][1])

print(len(train_ds),len(dev_ds))

import jieba

def data_preprocess(corpus):
data_set = []
for text in corpus:
seg_list = jieba.cut(text[0])
data_set.append((" ".join(seg_list), text[1]))
return data_set

train_corpus = data_preprocess(train_ds)
test_corpus = data_preprocess(test_ds)

print(train_corpus[:2])
print(test_corpus[:2])

cv = CountVectorizer(min_df=0., max_df=1.) cv_matrix = cv.fit_transform(norm_corpus)

As an AI language model, I cannot access any data or information outside of the conversation. However, I can explain what the code snippet means in general terms.

The code snippet is related to natural language processing (NLP) and machine learning. CountVectorizer is a method from the scikit-learn library in Python used for text preprocessing. It transforms a collection of text documents into a matrix of token counts.

The "min_df" parameter specifies the minimum frequency a word needs to appear in the corpus to be included in the vocabulary. The "max_df" parameter specifies the maximum frequency a word can appear in the corpus to be included in the vocabulary.

The "cv_matrix" variable is the output of the CountVectorizer method. It is a sparse matrix that represents the frequency of each word in each document of the corpus.