nlp-vtcc/codex-math-en

```py
import g4f
from copy import deepcopy
from datasets import load_dataset

translate_prompt = (
"Translate the following python snippet code into Vietnamese language (tiếng Việt). "
"Only translate the comments while preserving the name of functions, variables and other code. "
"Your translations must convey all the content in the original text and cannot involve explanations or other unnecessary information. "
"Please ensure that the translated text is natural for native speakers with correct grammar and proper word choices. "
"Your translation must also use exact terminology to provide accurate information even for the experts in the related fields. "
"Your output must only contain the code with translated comments and cannot include explanations or other information. "
"NOTE: Only translate the comments and DO NOT translate the name of functions, variables, arguments and other code. "
"Python code:\n"
)

def translate_response(example):
reply = example["reply"]
text = f"{translate_prompt}{reply}"
success = False
# try:
response = g4f.ChatCompletion.create(
model="gpt-3.5-turbo",
provider=g4f.Provider.GPTalk,
messages=[{"role": "user", "content": text}],
stream=False,
)
success = True
# except:
# response = text
# success = False
# print(f">>> Fail at {text}")
new_example = deepcopy(example)
new_example["reply"] = response
new_example["success"] = success
return new_example

## USAGE
dataset = load_dataset("json", data_files="codex00", split="train")
example = dataset[32]
new_example = translate_response(example)
print(new_example)
```

原始信息汇总

数据集概述

数据集加载

• 加载方式: 使用 load_dataset 函数从 "json" 文件中加载数据集。
• 文件名: "codex00"
• 数据集类型: 训练集（split="train"）

数据处理函数

• 函数名: translate_response
• 功能: 将Python代码中的注释翻译成越南语（tiếng Việt）。
• 输入: 数据集中的一个示例（example）。
• 输出: 包含翻译后注释的新示例（new_example）。

具体步骤

1. 生成翻译请求文本: 将原始代码中的注释提取并生成翻译请求文本。
2. 调用翻译API: 使用 g4f.ChatCompletion.create 函数调用翻译API进行翻译。
3. 处理响应: 将翻译后的注释替换原始注释，并标记翻译是否成功。

示例代码

python dataset = load_dataset("json", data_files="codex00", split="train") example = dataset[32] new_example = translate_response(example) print(new_example)