kaggle-notebooks-edu-v0

# Kaggle Notebooks LLM Filtered

- Model: `meta-llama/Meta-Llama-3.1-70B-Instruct`
- Sample: `12,400`
- Source dataset: `data-agents/kaggle-notebooks`
- Prompt:
```
Below is an extract from a Jupyter notebook. Evaluate whether it has a high analysis value and could help a data scientist.

The notebooks are formatted with the following tokens:

START
<text block>

# Here comes markdown content

<input block>
<lang python>

# Here comes python code

<output block>

# Here comes code output

# More blocks

END

Use the additive 5-point scoring system described below. Points are accumulated based on the satisfaction of each criterion, so stop counting if any of the criteria is not fulfilled:

- Add 1 point if the notebook contains valid code, even if it's not educational, like boilerplate code, configs, and niche concepts.
- Add another point if the notebook successfully loads a dataset e.g. a CSV or JSON file, even if it lacks further analysis and contains the code outputs.
- Award a third point if the notebook runs some analysis on the dataset by running statistics or plotting useful properties, even if they are mostly uncommented.
- Give a fourth point if the majority of the notebook contains text between the code cells explaining insights and performing reasoning.
- Give a fifth point if the notebook is clean and outstanding in it's analysis, creates insightful, explained plots and contains consistent, multi-step reasoning connected across the whole notebook and gains useful insights from the data.

The extract:
START
{}
END

After examining the extract:
- Briefly justify your total score, up to 100 words.
- Conclude with the score using the format: "Educational score: <total points>" where <total points> is just a one digit number.
```

# 经大语言模型（LLM）筛选的Kaggle笔记本数据集

- 模型：`meta-llama/Meta-Llama-3.1-70B-Instruct`
- 样本数量：`12,400`
- 源数据集：`data-agents/kaggle-notebooks`
- 提示词：

以下为一段Jupyter笔记本的节选内容，请评估其分析价值高低，是否能够为数据科学家提供有效助力。

该类笔记本采用以下Token（Token）进行格式标记：
START
<text block>

# 此处为Markdown内容

<input block>
<lang python>

# 此处为Python代码

<output block>

# 此处为代码输出

# 更多内容块
END

请采用下述加分制5分评分规则，每满足一项评分准则即可获得对应分值，若任意一项准则未达标则停止计分：
- 若笔记本包含有效代码（即便不具备教育意义，如样板代码、配置代码及小众领域相关代码），加1分。
- 若笔记本成功加载数据集（如CSV或JSON文件），即便缺乏后续分析且包含代码输出，再加1分。
- 若笔记本通过统计分析或绘制有效属性图表对数据集开展了分析，即便多数内容未添加注释，再授予1分。
- 若笔记本的大部分内容为代码单元间的阐释文本，用于阐述分析结论与开展推理，再授予1分。
- 若笔记本的分析过程清晰出色，绘制了具备阐释性的可视化图表，且全本贯穿连贯的多步推理并从数据中获取了有效洞见，再授予1分。

待评估节选内容：
START
{}
END

完成节选内容审阅后：
- 简要阐述总得分的评分依据，字数不超过100词。
- 最终得分需遵循以下格式："Educational score: <总得分>"，其中<总得分>为单个阿拉伯数字。