docs: make Chinese README the default

README.md

@@ -1,3 +1,9 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/cleanlab/cleanlab) · [上游 README](https://github.com/cleanlab/cleanlab/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <div align="center">
   <img src="https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/cleanlab_logo_open_source.png" width=60%>
 </div>
@@ -12,21 +18,21 @@
 
 <h4 align="center">
     <p>
-        <a href="https://docs.cleanlab.ai/">Documentation</a> |
-        <a href="https://github.com/cleanlab/examples">Examples</a> |
-        <a href="https://cleanlab.ai/blog/learn/">Blog</a> |
-        <a href="#citation-and-related-publications">Research</a>
+        <a href="https://docs.cleanlab.ai/">文档</a> |
+        <a href="https://github.com/cleanlab/examples">示例</a> |
+        <a href="https://cleanlab.ai/blog/learn/">博客</a> |
+        <a href="#citation-and-related-publications">研究</a>
     <p>
 </h4>
 
-Cleanlab’s open-source library helps you **clean** data and **lab**els by automatically detecting issues in a ML dataset. To facilitate **machine learning with messy, real-world data**, this data-centric AI package uses your *existing* models to estimate dataset problems that can be fixed to train even *better* models.
+Cleanlab 的开源库可帮助你通过自动检测 ML 数据集中的问题来**清理**（clean）数据与**标注**（lab**el**）。为促进**在混乱的真实世界数据上进行机器学习**，这一以数据为中心的 AI（data-centric AI）包利用你*现有*的模型来估计数据集中的问题，修复这些问题后即可训练出*更优*的模型。
  
 
 <p align="center">
   <img src="https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/datalab_issues.png" width=74%>
 </p>
 <p align="center">
-    Examples of various issues in Cat/Dog dataset <b>automatically detected</b> by cleanlab via this code:    
+    通过以下代码，cleanlab **自动检测**到的猫/狗（Cat/Dog）数据集中各类问题的示例：    
 </p>
 
 ```python
@@ -36,55 +42,55 @@ Cleanlab’s open-source library helps you **clean** data and **lab**els by auto
         lab.report()
 ```
 
-- Use cleanlab to automatically check every: [text](https://docs.cleanlab.ai/stable/tutorials/datalab/text.html), [audio](https://docs.cleanlab.ai/stable/tutorials/datalab/audio.html), [image](https://docs.cleanlab.ai/stable/tutorials/datalab/image.html), or [tabular](https://docs.cleanlab.ai/stable/tutorials/datalab/tabular.html) dataset.
-- Use cleanlab to automatically: [detect data issues (outliers, duplicates, label errors, etc)](https://docs.cleanlab.ai/stable/tutorials/datalab/datalab_quickstart.html), [train robust models](https://docs.cleanlab.ai/stable/tutorials/indepth_overview.html), [infer consensus + annotator-quality for multi-annotator data](https://docs.cleanlab.ai/stable/tutorials/multiannotator.html), [suggest data to (re)label next (active learning)](https://github.com/cleanlab/examples/blob/master/active_learning_multiannotator/active_learning.ipynb).
+- 使用 cleanlab 自动检查各类数据集：[文本](https://docs.cleanlab.ai/stable/tutorials/datalab/text.html), [音频](https://docs.cleanlab.ai/stable/tutorials/datalab/audio.html), [图像](https://docs.cleanlab.ai/stable/tutorials/datalab/image.html), 或 [表格](https://docs.cleanlab.ai/stable/tutorials/datalab/tabular.html) 数据集。
+- 使用 cleanlab 自动：[检测数据问题（异常值、重复项、标注错误等）](https://docs.cleanlab.ai/stable/tutorials/datalab/datalab_quickstart.html), [训练鲁棒模型](https://docs.cleanlab.ai/stable/tutorials/indepth_overview.html), [推断多标注者数据的共识与标注者质量](https://docs.cleanlab.ai/stable/tutorials/multiannotator.html), [建议下一步应（重新）标注的数据（主动学习）](https://github.com/cleanlab/examples/blob/master/active_learning_multiannotator/active_learning.ipynb).
 
 
 ---
 
 
-## Run cleanlab open-source
+## 运行 cleanlab 开源版
 
-This cleanlab package runs on Python 3.10+ and supports Linux, macOS, as well as Windows.
+此 cleanlab 包运行于 Python 3.10+，支持 Linux、macOS 以及 Windows。
 
-- Get started [here](https://docs.cleanlab.ai/)! Install via `uv`, `pip`, or `conda`.
-- Developers who install the bleeding-edge from source should refer to [this master branch documentation](https://docs.cleanlab.ai/master/index.html).
+- 从[这里](https://docs.cleanlab.ai/)! 开始入门。可通过 `uv`、`pip` 或 `conda` 安装。
+- 从源码安装最新开发版的开发者请参阅[此 master 分支文档](https://docs.cleanlab.ai/master/index.html).
 
-**Practicing data-centric AI can look like this:**
-1. Train initial ML model on original dataset.
-2. Utilize this model to diagnose data issues (via cleanlab methods) and improve the dataset.
-3. Train the same model on the improved dataset. 
-4. Try various modeling techniques to further improve performance.
+**实践以数据为中心的 AI 可以是这样：**
+1. 在原始数据集上训练初始 ML 模型。
+2. 利用该模型诊断数据问题（通过 cleanlab 方法）并改进数据集。
+3. 在改进后的数据集上训练同一模型。 
+4. 尝试各种建模技术以进一步提升性能。
 
-Most folks jump from Step 1 → 4, but you may achieve big gains without *any* change to your modeling code by using cleanlab!
-Continuously boost performance by iterating Steps 2 → 4 (and try to evaluate with *cleaned* data).
+多数人直接从步骤 1 跳到 4，但借助 cleanlab，你无需对建模代码做任何修改也可能获得巨大提升！
+通过反复执行步骤 2 → 4 持续提升性能（并尽量使用*清洗后*的数据进行评估）。
 
 ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/flowchart.png)
 
 
-## Use cleanlab with any model and in most ML tasks
+## 将 cleanlab 用于任意模型及大多数 ML 任务
 
-All features of cleanlab work with **any dataset** and **any model**. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc.
+cleanlab 的全部功能均可配合**任意数据集**与**任意模型**使用。是的，任意模型：PyTorch、Tensorflow、Keras、JAX、HuggingFace、OpenAI、XGBoost、scikit-learn 等。
 
-cleanlab is useful across a wide variety of Machine Learning tasks. Specific tasks this data-centric AI package offers dedicated functionality for include:
-1. [Binary and multi-class classification](https://docs.cleanlab.ai/stable/tutorials/indepth_overview.html)
-2. [Multi-label classification](https://docs.cleanlab.ai/stable/tutorials/multilabel_classification.html) (e.g. image/document tagging)
-3. [Token classification](https://docs.cleanlab.ai/stable/tutorials/token_classification.html) (e.g. entity recognition in text)
-4. [Regression](https://docs.cleanlab.ai/stable/tutorials/regression.html) (predicting numerical column in a dataset)
-5. [Image segmentation](https://docs.cleanlab.ai/stable/tutorials/segmentation.html) (images with per-pixel annotations)
-6. [Object detection](https://docs.cleanlab.ai/stable/tutorials/object_detection.html) (images with bounding box annotations)
-7. [Classification with data labeled by multiple annotators](https://docs.cleanlab.ai/stable/tutorials/multiannotator.html)
-8. [Active learning with multiple annotators](https://github.com/cleanlab/examples/blob/master/active_learning_multiannotator/active_learning.ipynb) (suggest which data to label or re-label to improve model most)
-9. [Outlier detection](https://docs.cleanlab.ai/stable/tutorials/outliers.html) (identify atypical data that appears out of distribution)
+cleanlab 适用于种类繁多的机器学习任务。此以数据为中心的 AI 包为以下特定任务提供专门功能：
+1. [二分类与多分类](https://docs.cleanlab.ai/stable/tutorials/indepth_overview.html)
+2. [多标签分类](https://docs.cleanlab.ai/stable/tutorials/multilabel_classification.html)（例如图像/文档标注）
+3. [Token 分类](https://docs.cleanlab.ai/stable/tutorials/token_classification.html)（例如文本中的实体识别）
+4. [回归](https://docs.cleanlab.ai/stable/tutorials/regression.html)（预测数据集中的数值列）
+5. [图像分割](https://docs.cleanlab.ai/stable/tutorials/segmentation.html)（带逐像素标注的图像）
+6. [目标检测](https://docs.cleanlab.ai/stable/tutorials/object_detection.html)（带边界框标注的图像）
+7. [由多名标注者标注数据的分类](https://docs.cleanlab.ai/stable/tutorials/multiannotator.html)
+8. [多名标注者的主动学习](https://github.com/cleanlab/examples/blob/master/active_learning_multiannotator/active_learning.ipynb)（建议标注或重新标注哪些数据最能提升模型）
+9. [异常值检测](https://docs.cleanlab.ai/stable/tutorials/outliers.html)（识别看似分布外的非典型数据）
 
-For other ML tasks, cleanlab can still help you improve your dataset if appropriately applied.
-See our [Example Notebooks](https://github.com/cleanlab/examples) and [Blog](https://cleanlab.ai/blog/learn/).
+对于其他 ML 任务，若恰当应用，cleanlab 仍可帮助你改进数据集。
+请参阅我们的[示例 Notebook](https://github.com/cleanlab/examples) 与[博客](https://cleanlab.ai/blog/learn/).
 
 
-## So fresh, so cleanlab
+## 如此清新，如此 cleanlab
 
-Beyond automatically catching [all sorts of issues](https://docs.cleanlab.ai/stable/cleanlab/datalab/guide/issue_type_description.html) lurking in your data, this data-centric AI package helps you deal with **noisy labels** and train more **robust ML models**.
-Here's an example:
+除了自动捕获潜藏在你数据中的[各类问题](https://docs.cleanlab.ai/stable/cleanlab/datalab/guide/issue_type_description.html)，这一以数据为中心的 AI 包还可帮助你处理**噪声标注**，并训练更**鲁棒的 ML 模型**。
+示例如下：
 
 ```python
 
@@ -104,28 +110,27 @@ cl.predict(test_data)
 cleanlab.dataset.health_summary(labels, confident_joint=cl.confident_joint)
 ```
 
-cleanlab **clean**s your data's **lab**els via state-of-the-art *confident learning* algorithms, published in this [paper](https://jair.org/index.php/jair/article/view/12125) and [blog](https://l7.curtisnorthcutt.com/confident-learning). See some of the datasets cleaned with cleanlab at [labelerrors.com](https://labelerrors.com).
+cleanlab 通过最先进的*置信学习*（confident learning）算法来**清理**你数据的**标注**，相关算法发表于此[论文](https://jair.org/index.php/jair/article/view/12125) 与[博客](https://l7.curtisnorthcutt.com/confident-learning).。可在 [labelerrors.com](https://labelerrors.com). 查看部分经 cleanlab 清洗的数据集。
 
-cleanlab is:
+cleanlab 具备：
 
-1. **backed by theory** -- with [provable guarantees](https://arxiv.org/abs/1911.00068) of exact label noise estimation, even with imperfect models.
-2. **fast** -- code is parallelized and scalable.
-4. **easy to use** -- one line of code to find mislabeled data, bad annotators, outliers, or train noise-robust models.
-6. **general** -- works with **[any dataset](https://labelerrors.com/)** (text, image, tabular, audio,...) + **any model** (PyTorch, OpenAI, XGBoost,...)
+1. **有理论支撑**——即使模型不完美，也能对标注噪声进行精确估计，并具有[可证明的保证](https://arxiv.org/abs/1911.00068)。
+2. **快速**——代码已并行化且可扩展。
+4. **易于使用**——一行代码即可找出错误标注、劣质标注者、异常值，或训练抗噪声模型。
+6. **通用**——适用于**[任意数据集](https://labelerrors.com/)****（文本、图像、表格、音频等）+ **任意模型**（PyTorch、OpenAI、XGBoost 等）
 <br/>
 
 ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/label-errors-examples.png)
 <p align="center">
-Examples of incorrect given labels in various image datasets <a href="https://l7.curtisnorthcutt.com/label-errors">found and corrected</a> using cleanlab. 
-While these examples are from image datasets, this also works for text, audio, tabular data.
+使用 cleanlab <a href="https://l7.curtisnorthcutt.com/label-errors">发现并纠正</a>的各类图像数据集中错误给定标注的示例。 
+尽管这些示例来自图像数据集，该方法同样适用于文本、音频、表格数据。
 </p>
 
+## 引用与相关出版物
 
-## Citation and related publications
+cleanlab 基于同行评审的研究。若你使用本软件包，可引用以下相关论文：
 
-cleanlab is based on peer-reviewed research. Here are relevant papers to cite if you use this package:
-
-<details><summary><a href="https://arxiv.org/abs/1911.00068">Confident Learning (JAIR '21)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/1911.00068">Confident Learning (JAIR '21)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @article{northcutt2021confidentlearning,
         title={Confident Learning: Estimating Uncertainty in Dataset Labels},
@@ -138,7 +143,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/1705.01936">Rank Pruning (UAI '17)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/1705.01936">Rank Pruning (UAI '17)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{northcutt2017rankpruning,
         author={Northcutt, Curtis G. and Wu, Tailin and Chuang, Isaac L.},
@@ -154,7 +159,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://jonasmueller.org/info/LabelQuality_icml.pdf"> Label Quality Scoring (ICML '22)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://jonasmueller.org/info/LabelQuality_icml.pdf"> Label Quality Scoring (ICML '22)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{kuan2022labelquality,
         title={Model-agnostic label quality scoring to detect real-world label errors},
@@ -165,7 +170,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/2210.03920"> Label Errors in Token Classification / Entity Recognition (NeurIPS '22)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/2210.03920"> Label Errors in Token Classification / Entity Recognition (NeurIPS '22)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{wang2022tokenerrors,
         title={Detecting label errors in token classification data},
@@ -176,7 +181,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/2211.13895"> Label Errors in Multi-Label Classification (ICLR '23)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/2211.13895"> Label Errors in Multi-Label Classification (ICLR '23)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{thyagarajan2023multilabel,
         title={Identifying Incorrect Annotations in Multi-Label Classification Data},
@@ -187,7 +192,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/2309.00832"> Label Errors in Object Detection (ICML '23)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/2309.00832"> Label Errors in Object Detection (ICML '23)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{tkachenko2023objectlab,
         title={ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data},
@@ -198,7 +203,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/2307.05080"> Label Errors in Image Segmentation (ICML '23)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/2307.05080"> Label Errors in Image Segmentation (ICML '23)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{lad2023segmentation,
         title={Estimating label quality and errors in semantic segmentation data via any model},
@@ -209,7 +214,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/2305.16583"> Detecting Errors in Numerical Data (DMLR '24)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/2305.16583"> Detecting Errors in Numerical Data (DMLR '24)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{zhou2023errors,
         title={Detecting Errors in a Numerical Response via any Regression Model},
@@ -220,7 +225,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/2207.03061"> Out-of-Distribution Detection (ICML '22)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/2207.03061"> Out-of-Distribution Detection (ICML '22)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{kuan2022ood,
         title={Back to the Basics: Revisiting Out-of-Distribution Detection Baselines},
@@ -231,7 +236,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/2210.06812"> CROWDLAB for Data with Multiple Annotators (NeurIPS '22)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/2210.06812"> CROWDLAB for Data with Multiple Annotators (NeurIPS '22)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{goh2022crowdlab,
         title={CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple annotators},
@@ -242,7 +247,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/2301.11856"> ActiveLab: Active learning with data re-labeling (ICLR '23)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/2301.11856"> ActiveLab: Active learning with data re-labeling (ICLR '23)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{goh2023activelab,
         title={ActiveLab: Active Learning with Re-Labeling by Multiple Annotators},
@@ -253,7 +258,7 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-<details><summary><a href="https://arxiv.org/abs/2305.15696"> Detecting Dataset Drift and Non-IID Sampling (ICML '23)</a> (<b>click to show bibtex</b>) </summary>
+<details><summary><a href="https://arxiv.org/abs/2305.15696"> Detecting Dataset Drift and Non-IID Sampling (ICML '23)</a> (<b>点击显示 bibtex</b>) </summary>
 
     @inproceedings{cummings2023drift,
         title={Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors},
@@ -264,23 +269,23 @@ cleanlab is based on peer-reviewed research. Here are relevant papers to cite if
 
 </details>
 
-To understand/cite other cleanlab functionality not described above, check out our [Blog](https://cleanlab.ai/blog/learn).
+要了解/引用上文未介绍的 cleanlab 其他功能，请查阅我们的 [Blog](https://cleanlab.ai/blog/learn).
 
 
-## Other resources
+## 其他资源
 
-- [Example Notebooks demonstrating practical applications of this package](https://github.com/cleanlab/examples)
+- [展示本软件包实际应用的 Example Notebooks](https://github.com/cleanlab/examples)
 
-- [Cleanlab Blog](https://cleanlab.ai/blog/learn)
+- [Cleanlab 博客](https://cleanlab.ai/blog/learn)
 
-- [Blog post: Introduction to Confident Learning](https://l7.curtisnorthcutt.com/confident-learning)
+- [博客文章：Confident Learning 简介](https://l7.curtisnorthcutt.com/confident-learning)
 
-- [NeurIPS 2021 paper: Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://arxiv.org/abs/2103.14749)
+- [NeurIPS 2021 论文：Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://arxiv.org/abs/2103.14749)
 
-- [Introduction to Data-centric AI (MIT IAP Course)](https://dcai.csail.mit.edu/)
+- [数据-centric AI（Data-centric AI）简介（MIT IAP 课程）](https://dcai.csail.mit.edu/)
 
-- [Release notes for past versions](https://github.com/cleanlab/cleanlab/releases)
+- [过往版本的发布说明](https://github.com/cleanlab/cleanlab/releases)
 
-**Interested in contributing?**  See the [contributing guide](CONTRIBUTING.md), [development guide](DEVELOPMENT.md), and [ideas on useful contributions](https://github.com/cleanlab/cleanlab/wiki#ideas-for-contributing-to-cleanlab).
+**有兴趣参与贡献？** 请参阅 [贡献指南](CONTRIBUTING.md)、[开发指南](DEVELOPMENT.md) 以及[实用贡献建议](https://github.com/cleanlab/cleanlab/wiki#ideas-for-contributing-to-cleanlab).
 
-**Have questions?**  Check out [our FAQ](https://docs.cleanlab.ai/stable/tutorials/faq.html) and [Github Issues](https://github.com/cleanlab/cleanlab/issues?q=is%3Aissue).
+**有问题？** 请查看[我们的常见问题](https://docs.cleanlab.ai/stable/tutorials/faq.html) 和 [Github Issues](https://github.com/cleanlab/cleanlab/issues?q=is%3Aissue).