计算分词结果的准确率P、召回率R、F1值，其中msr_test_gold.utf8为msr_test.utf8分词的标准答案python代码怎么写？

可以Python中的分词库jieba和sklearn.metrics库来计算准确率P、召回率R、F1值。具体代码如下：

import jieba
from sklearn.metrics import precision_score, recall_score, f1_score

# 读取分词结果和标准答案
with open('msr_test.utf8', 'r', encoding='utf-8') as f:
    test_data = f.readlines()
with open('msr_test_gold.utf8', 'r', encoding='utf-8') as f:
    gold_data = f.readlines()

# 将分词结果和标准答案分别转化为列表
test_words = [word.strip() for line in test_data for word in line.split()]
gold_words = [word.strip() for line in gold_data for word in line.split()]

# 使用jieba进行分词
test_cut_words = list(jieba.cut(''.join(test_data)))
gold_cut_words = list(jieba.cut(''.join(gold_data)))

# 计算准确率P、召回率R、F1值
precision = precision_score(gold_words, test_words, average='macro')
recall = recall_score(gold_words, test_words, average='macro')
f1 = f1_score(gold_words, test_words, average='macro')

print('准确率P：', precision)
print('召回率R：', recall)
print('F1值：', f1)

注意，其中msr_test.utf8和msr_test_gold.utf8为分词结果和标准答案文件，应该先下载到本地并放在相应路径下。另外，分词结果和标准答案的每个词之间应该用空格隔开。