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479 lines
12 KiB
Markdown
479 lines
12 KiB
Markdown
<div align="center">
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<img src="docs/zh_cn/_static/image/logo.svg" width="500px"/>
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<br />
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<br />
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[](https://opencompass.readthedocs.io/zh_CN)
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[](https://github.com/open-compass/opencompass/blob/main/LICENSE)
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<!-- [](https://pypi.org/project/opencompass/) -->
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[🌐Website](https://opencompass.org.cn/) |
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[📘Documentation](https://opencompass.readthedocs.io/zh_CN/latest/index.html) |
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[🛠️Installation](https://opencompass.readthedocs.io/zh_CN/latest/get_started/installation.html) |
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[🤔Reporting Issues](https://github.com/open-compass/opencompass/issues/new/choose)
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[English](/README.md) | 简体中文
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</div>
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<p align="center">
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👋 加入我们的 <a href="https://discord.gg/KKwfEbFj7U" target="_blank">Discord</a> 和 <a href="https://r.vansin.top/?r=opencompass" target="_blank">微信社区</a>
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</p>
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## 🧭 欢迎
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来到**OpenCompass**！
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就像指南针在我们的旅程中为我们导航一样，我们希望OpenCompass能够帮助你穿越评估大型语言模型的重重迷雾。OpenCompass提供丰富的算法和功能支持，期待OpenCompass能够帮助社区更便捷地对NLP模型的性能进行公平全面的评估。
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> **🔥 注意**<br />
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> 我们正式启动 OpenCompass 共建计划，诚邀社区用户为 OpenCompass 提供更具代表性和可信度的客观评测数据集!
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> 点击 [Issue](https://github.com/open-compass/opencompass/issues/248) 获取更多数据集.
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> 让我们携手共进，打造功能强大易用的大模型评测平台！
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## 🚀 最新进展 <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
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- **\[2023.09.26\]** 我们在评测榜单上更新了[Qwen](https://github.com/QwenLM/Qwen), 这是目前表现最好的开源模型之一, 欢迎访问[官方网站](https://opencompass.org.cn)获取详情.🔥🔥🔥.
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- **\[2023.09.20\]** 我们在评测榜单上更新了[InternLM-20B](https://github.com/InternLM/InternLM), 欢迎访问[官方网站](https://opencompass.org.cn)获取详情.🔥🔥🔥.
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- **\[2023.09.19\]** 我们在评测榜单上更新了WeMix-LLaMA2-70B/Phi-1.5-1.3B, 欢迎访问[官方网站](https://opencompass.org.cn)获取详情.
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- **\[2023.09.18\]** 我们发布了[长文本评测指引](docs/zh_cn/advanced_guides/longeval.md).
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- **\[2023.09.08\]** 我们在评测榜单上更新了Baichuan-2/Tigerbot-2/Vicuna-v1.5, 欢迎访问[官方网站](https://opencompass.org.cn)获取详情。
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- **\[2023.09.06\]** 欢迎 [**Baichuan2**](https://github.com/baichuan-inc/Baichuan2) 团队采用OpenCompass对模型进行系统评估。我们非常感谢社区在提升LLM评估的透明度和可复现性上所做的努力。
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- **\[2023.09.02\]** 我们加入了[Qwen-VL](https://github.com/QwenLM/Qwen-VL)的评测支持。
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- **\[2023.08.25\]** 欢迎 [**TigerBot**](https://github.com/TigerResearch/TigerBot) 团队采用OpenCompass对模型进行系统评估。我们非常感谢社区在提升LLM评估的透明度和可复现性上所做的努力。
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- **\[2023.08.21\]** [**Lagent**](https://github.com/InternLM/lagent) 正式发布，它是一个轻量级、开源的基于大语言模型的智能体（agent）框架。我们正与Lagent团队紧密合作，推进支持基于Lagent的大模型工具能力评测 !
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> [更多](docs/zh_cn/notes/news.md)
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## ✨ 介绍
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OpenCompass 是面向大模型评测的一站式平台。其主要特点如下：
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- **开源可复现**：提供公平、公开、可复现的大模型评测方案
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- **全面的能力维度**：五大维度设计，提供 70+ 个数据集约 40 万题的的模型评测方案，全面评估模型能力
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- **丰富的模型支持**：已支持 20+ HuggingFace 及 API 模型
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- **分布式高效评测**：一行命令实现任务分割和分布式评测，数小时即可完成千亿模型全量评测
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- **多样化评测范式**：支持零样本、小样本及思维链评测，结合标准型或对话型提示词模板，轻松激发各种模型最大性能
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- **灵活化拓展**：想增加新模型或数据集？想要自定义更高级的任务分割策略，甚至接入新的集群管理系统？OpenCompass 的一切均可轻松扩展！
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## 📊 性能榜单
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我们将陆续提供开源模型和API模型的具体性能榜单，请见 [OpenCompass Leaderbaord](https://opencompass.org.cn/rank) 。如需加入评测，请提供模型仓库地址或标准的 API 接口至邮箱 `opencompass@pjlab.org.cn`.
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<p align="right"><a href="#top">🔝返回顶部</a></p>
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## 🛠️ 安装
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下面展示了快速安装以及准备数据集的步骤。
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```Python
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conda create --name opencompass python=3.10 pytorch torchvision pytorch-cuda -c nvidia -c pytorch -y
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conda activate opencompass
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git clone https://github.com/open-compass/opencompass opencompass
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cd opencompass
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pip install -e .
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# 下载数据集到 data/ 处
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wget https://github.com/open-compass/opencompass/releases/download/0.1.1/OpenCompassData.zip
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unzip OpenCompassData.zip
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```
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有部分第三方功能,如 Humaneval 以及 Llama,可能需要额外步骤才能正常运行，详细步骤请参考[安装指南](https://opencompass.readthedocs.io/zh_CN/latest/get_started/installation.html)。
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<p align="right"><a href="#top">🔝返回顶部</a></p>
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## 🏗️ ️评测
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确保按照上述步骤正确安装 OpenCompass 并准备好数据集后，可以通过以下命令评测 LLaMA-7b 模型在 MMLU 和 C-Eval 数据集上的性能：
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```bash
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python run.py --models hf_llama_7b --datasets mmlu_ppl ceval_ppl
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```
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OpenCompass 预定义了许多模型和数据集的配置，你可以通过 [工具](./docs/zh_cn/tools.md#ListConfigs) 列出所有可用的模型和数据集配置。
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```bash
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# 列出所有配置
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python tools/list_configs.py
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# 列出所有跟 llama 及 mmlu 相关的配置
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python tools/list_configs.py llama mmlu
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```
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你也可以通过命令行去评测其它 HuggingFace 模型。同样以 LLaMA-7b 为例：
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```bash
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python run.py --datasets ceval_ppl mmlu_ppl \
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--hf-path huggyllama/llama-7b \ # HuggingFace 模型地址
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--model-kwargs device_map='auto' \ # 构造 model 的参数
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--tokenizer-kwargs padding_side='left' truncation='left' use_fast=False \ # 构造 tokenizer 的参数
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--max-out-len 100 \ # 最长生成 token 数
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--max-seq-len 2048 \ # 模型能接受的最大序列长度
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--batch-size 8 \ # 批次大小
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--no-batch-padding \ # 不打开 batch padding，通过 for loop 推理，避免精度损失
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--num-gpus 1 # 运行该模型所需的最少 gpu 数
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```
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> **注意**<br />
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> 若需要运行上述命令，你需要删除所有从 `# ` 开始的注释。
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通过命令行或配置文件，OpenCompass 还支持评测 API 或自定义模型，以及更多样化的评测策略。请阅读[快速开始](https://opencompass.readthedocs.io/zh_CN/latest/get_started/quick_start.html)了解如何运行一个评测任务。
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更多教程请查看我们的[文档](https://opencompass.readthedocs.io/zh_CN/latest/index.html)。
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<p align="right"><a href="#top">🔝返回顶部</a></p>
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## 📖 数据集支持
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<table align="center">
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<tbody>
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<tr align="center" valign="bottom">
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<td>
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<b>语言</b>
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</td>
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<td>
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<b>知识</b>
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</td>
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<td>
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<b>推理</b>
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</td>
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<td>
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<b>考试</b>
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</td>
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</tr>
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<tr valign="top">
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<td>
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<details open>
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<summary><b>字词释义</b></summary>
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- WiC
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- SummEdits
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</details>
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<details open>
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<summary><b>成语习语</b></summary>
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- CHID
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</details>
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<details open>
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<summary><b>语义相似度</b></summary>
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- AFQMC
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- BUSTM
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</details>
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<details open>
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<summary><b>指代消解</b></summary>
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- CLUEWSC
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- WSC
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- WinoGrande
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</details>
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<details open>
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<summary><b>翻译</b></summary>
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- Flores
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- IWSLT2017
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</details>
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<details open>
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<summary><b>多语种问答</b></summary>
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- TyDi-QA
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- XCOPA
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</details>
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<details open>
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<summary><b>多语种总结</b></summary>
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- XLSum
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</details>
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</td>
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<td>
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<details open>
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<summary><b>知识问答</b></summary>
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- BoolQ
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- CommonSenseQA
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- NaturalQuestions
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- TriviaQA
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</details>
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</td>
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<td>
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<details open>
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<summary><b>文本蕴含</b></summary>
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- CMNLI
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- OCNLI
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- OCNLI_FC
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- AX-b
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- AX-g
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- CB
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- RTE
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- ANLI
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</details>
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<details open>
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<summary><b>常识推理</b></summary>
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- StoryCloze
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- COPA
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- ReCoRD
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- HellaSwag
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- PIQA
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- SIQA
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</details>
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<details open>
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<summary><b>数学推理</b></summary>
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- MATH
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- GSM8K
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</details>
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<details open>
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<summary><b>定理应用</b></summary>
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- TheoremQA
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- StrategyQA
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- SciBench
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</details>
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<details open>
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<summary><b>综合推理</b></summary>
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- BBH
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</details>
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</td>
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<td>
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<details open>
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<summary><b>初中/高中/大学/职业考试</b></summary>
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- C-Eval
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- AGIEval
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- MMLU
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- GAOKAO-Bench
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- CMMLU
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- ARC
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- Xiezhi
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</details>
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<details open>
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<summary><b>医学考试</b></summary>
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- CMB
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</details>
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</td>
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</tr>
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</td>
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</tr>
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</tbody>
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<tbody>
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<tr align="center" valign="bottom">
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<td>
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<b>理解</b>
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</td>
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<td>
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<b>长文本</b>
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</td>
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<td>
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<b>安全</b>
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</td>
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<td>
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<b>代码</b>
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</td>
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</tr>
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<tr valign="top">
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<td>
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<details open>
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<summary><b>阅读理解</b></summary>
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- C3
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- CMRC
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- DRCD
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- MultiRC
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- RACE
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- DROP
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- OpenBookQA
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- SQuAD2.0
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</details>
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<details open>
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<summary><b>内容总结</b></summary>
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- CSL
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- LCSTS
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- XSum
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- SummScreen
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</details>
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<details open>
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<summary><b>内容分析</b></summary>
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- EPRSTMT
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- LAMBADA
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- TNEWS
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</details>
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</td>
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<td>
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<details open>
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<summary><b>长文本理解</b></summary>
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- LEval
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- LongBench
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- GovReports
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- NarrativeQA
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- Qasper
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</details>
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</td>
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<td>
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<details open>
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<summary><b>安全</b></summary>
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- CivilComments
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- CrowsPairs
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- CValues
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- JigsawMultilingual
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- TruthfulQA
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</details>
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<details open>
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<summary><b>健壮性</b></summary>
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- AdvGLUE
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</details>
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</td>
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<td>
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<details open>
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<summary><b>代码</b></summary>
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- HumanEval
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- HumanEvalX
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- MBPP
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- APPs
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- DS1000
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</details>
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</td>
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</tr>
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</td>
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</tr>
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</tbody>
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</table>
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<p align="right"><a href="#top">🔝返回顶部</a></p>
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## 📖 模型支持
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<table align="center">
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<tbody>
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<tr align="center" valign="bottom">
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<td>
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<b>开源模型</b>
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</td>
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<td>
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<b>API 模型</b>
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</td>
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<!-- <td>
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<b>自定义模型</b>
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</td> -->
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</tr>
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<tr valign="top">
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<td>
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- InternLM
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- LLaMA
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- Vicuna
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- Alpaca
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- Baichuan
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- WizardLM
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- ChatGLM2
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- Falcon
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- TigerBot
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- Qwen
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- ……
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</td>
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<td>
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- OpenAI
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- Claude
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- PaLM (即将推出)
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||
- ……
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</td>
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</tr>
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</tbody>
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</table>
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<p align="right"><a href="#top">🔝返回顶部</a></p>
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## 🔜 路线图
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||
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- [ ] 主观评测
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- [ ] 发布主观评测榜单
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- [ ] 发布主观评测数据集
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- [ ] 长文本
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- [ ] 支持广泛的长文本评测集
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- [ ] 发布长文本评测榜单
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- [ ] 代码能力
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- [ ] 发布代码能力评测榜单
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- [ ] 提供非Python语言的评测服务
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- [ ] 智能体
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- [ ] 支持丰富的智能体方案
|
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- [ ] 提供智能体评测榜单
|
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- [ ] 鲁棒性
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- [ ] 支持各类攻击方法
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## 👷♂️ 贡献
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|
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我们感谢所有的贡献者为改进和提升 OpenCompass 所作出的努力。请参考[贡献指南](https://opencompass.readthedocs.io/zh_CN/latest/notes/contribution_guide.html)来了解参与项目贡献的相关指引。
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## 🤝 致谢
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该项目部分的代码引用并修改自 [OpenICL](https://github.com/Shark-NLP/OpenICL)。
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该项目部分的数据集和提示词实现修改自 [chain-of-thought-hub](https://github.com/FranxYao/chain-of-thought-hub), [instruct-eval](https://github.com/declare-lab/instruct-eval)
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## 🖊️ 引用
|
||
|
||
```bibtex
|
||
@misc{2023opencompass,
|
||
title={OpenCompass: A Universal Evaluation Platform for Foundation Models},
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||
author={OpenCompass Contributors},
|
||
howpublished = {\url{https://github.com/open-compass/opencompass}},
|
||
year={2023}
|
||
}
|
||
```
|
||
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