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-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。
- Examples of various issues in Cat/Dog dataset automatically detected 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：
```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）。  -## 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）-Examples of incorrect given labels in various image datasets found and corrected using cleanlab. -While these examples are from image datasets, this also works for text, audio, tabular data. +使用 cleanlab 发现并纠正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。
+## 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: - -