docs: update index page with new features #51
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- Update Train Judge Models card (remove WIP badge, add link) - Update Generate Rubrics card to cover both Zero-Shot and Data-Driven approaches - Add LangSmith and Langfuse integration cards (remove WIP badges) - Add Zero-Shot Evaluation to Quick Tutorials section - Reduce card header font size for better text fitting - Update Training Frameworks description
helloml0326
requested review from
XiaoBoAI,
XieLipeng0830,
ployts and
weizhang25
Summary of ChangesHello @helloml0326, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request focuses on updating the project's documentation, specifically the main index page and README files, to accurately reflect the current state of features and integrations. The changes aim to provide users with up-to-date information on grader building methods, including the newly available judge model training and advanced rubric generation techniques. Additionally, it highlights the readiness of key observability platform integrations, enhancing clarity and improving the overall user experience by presenting a more current and comprehensive overview of the project's capabilities. Highlights
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Code Review
This pull request provides a comprehensive update to the documentation, reflecting new features such as Zero-Shot Evaluation, LangSmith/Langfuse integrations, and refined grader building methods. The changes across README.md, README_zh.md, and docs/index.md are clear and align well with the stated goals. The accompanying CSS adjustments for the documentation site also improve the visual presentation. Overall, this is a solid update that enhances the project's documentation. I've included a few minor suggestions to further improve formatting and consistency.
| * **Zero-shot Rubrics Generation:** Not sure what criteria to use, and no labeled data yet? Just provide a task description and optional sample queries—the LLM will automatically generate evaluation rubrics for you. Ideal for rapid prototyping when you want to get started immediately. 👉 [Zero-shot Rubrics Generation Guide](https://modelscope.github.io/OpenJudge/building_graders/generate_rubrics_as_graders/#simple-rubric-zero-shot-generation) | ||
| * **Data-driven Rubrics Generation:** Ambiguous requirements, but have few examples? Use the GraderGenerator to automatically | ||
| summarize evaluation Rubrics from your annotated data, and generate a llm-based grader. 👉 [Data-driven Rubrics Generation Guide](https://modelscope.github.io/OpenJudge/building_graders/generate_rubrics_as_graders/#iterative-rubric-data-driven-generation) | ||
| * **Training Judge Models:** Massive data and need peak performance? Use our training pipeline to train a dedicated Judge Model. This is ideal for complex scenarios where prompt-based grading falls short.👉 [Train Judge Models](https://modelscope.github.io/OpenJudge/building_graders/training_judge_models/) |
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For better readability and consistency, it's good practice to add a space before the arrow emoji (👉).
| * **Training Judge Models:** Massive data and need peak performance? Use our training pipeline to train a dedicated Judge Model. This is ideal for complex scenarios where prompt-based grading falls short.👉 [Train Judge Models](https://modelscope.github.io/OpenJudge/building_graders/training_judge_models/) | |
| * **Training Judge Models:** Massive data and need peak performance? Use our training pipeline to train a dedicated Judge Model. This is ideal for complex scenarios where prompt-based grading falls short. 👉 [Train Judge Models](https://modelscope.github.io/OpenJudge/building_graders/training_judge_models/) |
| * **自定义：** 需求明确但没有现成的评分器？如果您有明确的规则或逻辑，使用我们的 Python 接口或 Prompt 模板快速定义您自己的评分器。👉 [自定义评分器开发指南](https://modelscope.github.io/OpenJudge/building_graders/create_custom_graders/) | ||
| * **零样本评估标准生成：** 不确定使用什么标准，也没有标注数据？只需提供任务描述和可选的示例查询，LLM 将自动为您生成评估标准。非常适合快速原型开发。👉 [零样本评估标准生成指南](https://modelscope.github.io/OpenJudge/building_graders/generate_rubrics_as_graders/#simple-rubric-zero-shot-generation) | ||
| * **数据驱动的评估标准生成：** 需求模糊但有少量样例？使用 GraderGenerator 从您的标注数据中自动总结评估标准，并生成基于 LLM 的评分器。👉 [数据驱动评估标准生成指南](https://modelscope.github.io/OpenJudge/building_graders/generate_rubrics_as_graders/#iterative-rubric-data-driven-generation) | ||
| * **训练评判模型：** 拥有大量数据且需要极致性能？使用我们的训练流程来训练专用的评判模型。适用于基于 Prompt 的评分无法满足的复杂场景。👉 [训练评判模型](https://modelscope.github.io/OpenJudge/building_graders/training_judge_models/) |
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For better readability and consistency, it's good practice to add a space before the arrow emoji (👉).
| * **训练评判模型：** 拥有大量数据且需要极致性能？使用我们的训练流程来训练专用的评判模型。适用于基于 Prompt 的评分无法满足的复杂场景。👉 [训练评判模型](https://modelscope.github.io/OpenJudge/building_graders/training_judge_models/) | |
| * **训练评判模型：** 拥有大量数据且需要极致性能？使用我们的训练流程来训练专用的评判模型。适用于基于 Prompt 的评分无法满足的复杂场景。 👉 [训练评判模型](https://modelscope.github.io/OpenJudge/building_graders/training_judge_models/) |
| ### 🔌 轻松集成 | ||
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| 我们正在积极构建与主流可观测性平台和训练框架的无缝连接器。敬请期待！→ 查看 [集成](#-集成) | ||
| 如果您正在使用主流可观测性平台（如 **LangSmith** 或 **Langfuse**），我们提供无缝集成方案，可增强平台的评测器和自动评测能力。我们也正在构建与训练框架（如 **verl**）的集成方案。👉 查看 [集成](#-集成) 了解详情 |
There was a problem hiding this comment.
For better readability and consistency, it's good practice to add a space before the arrow emoji (👉).
| 如果您正在使用主流可观测性平台（如 **LangSmith** 或 **Langfuse**），我们提供无缝集成方案，可增强平台的评测器和自动评测能力。我们也正在构建与训练框架（如 **verl**）的集成方案。👉 查看 [集成](#-集成) 了解详情 | |
| 如果您正在使用主流可观测性平台（如 **LangSmith** 或 **Langfuse**），我们提供无缝集成方案，可增强平台的评测器和自动评测能力。我们也正在构建与训练框架（如 **verl**）的集成方案。 👉 查看 [集成](#-集成) 了解详情 |
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These empty lines appear to be unnecessary and can be removed for better code cleanliness.
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