解释代码1:sum_sent = sentence_split(judgment_summary)
时间: 2023-06-07 15:10:22 浏览: 64
这是一行Python代码,它的作用是将一个字符串(judgment_summary)分割成若干个句子,并将分割后的句子存储到一个列表(sum_sent)中。具体的分割方法可以参考sentence_split函数,但是该代码并未提供该函数的实现。
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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])
def markov(txt,init_mat,trans_mat,emit_mat): list_all = txt.split(" ") print("词库", list_all) sentence = "".join(list_all) #处理发射矩阵 original = [i for i in sentence] list_column = [0, 0, 0, 0] df_column = [column for column in emit_mat] for item in original: if item not in df_column: emit_mat[item] = list_column #处理BMSE single = [] for word in list_all: word_tag = get_tag(word) single.extend(word_tag) BMES.append(single) print("BMES:", BMES) 解释以上代码
这段代码实现了一个基于马尔可夫模型的中文分词算法。其中,
- txt表示输入的待分词文本;
- init_mat表示初始状态矩阵;
- trans_mat表示状态转移矩阵;
- emit_mat表示发射矩阵。
具体实现流程如下:
1. 将输入文本按空格分割成一个个词语,并将它们组成词库。
2. 将词库中的所有词语拼接成一个字符串,并将这个字符串中的每个字单独提取出来,作为发射矩阵的行。
3. 遍历输入文本中的每个字,如果它不在发射矩阵的行中,则在发射矩阵中新增一行。
4. 遍历词库中的每个词语,将它们分别转化为BMES标记序列,并将所有词语的BMES标记序列组成一个二维列表。
5. 输出BMES标记序列以及词库。
总的来说,这段代码实现了一个简单的中文分词算法,但实际效果可能并不理想,因为其基于马尔可夫模型的假设过于简单,并且没有进行更加复杂的语言模型训练。