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README.en.md

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+<div align="center">
+  <img src="https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/cleanlab_logo_open_source.png" width=60%>
+</div>
+
+<div align="center">
+<a href="https://pypi.org/pypi/cleanlab/" target="_blank"><img src="https://img.shields.io/pypi/v/cleanlab.svg" alt="pypi_versions"></a>
+<a href="https://pypi.org/pypi/cleanlab/" target="_blank"><img src="https://img.shields.io/badge/python-3.10%2B-blue" alt="py_versions"></a>
+<a href="https://app.codecov.io/gh/cleanlab/cleanlab" target="_blank"><img src="https://codecov.io/gh/cleanlab/cleanlab/branch/master/graph/badge.svg" alt="coverage"></a>
+<a href="https://github.com/cleanlab/cleanlab/stargazers/" target="_blank"><img src="https://img.shields.io/github/stars/cleanlab/cleanlab?style=social&maxAge=2592000" alt="Github Stars"></a>
+<a href="https://twitter.com/CleanlabAI" target="_blank"><img src="https://img.shields.io/twitter/follow/CleanlabAI?style=social" alt="Twitter"></a>
+</div>
+
+<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>
+    <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.
+ 
+
+<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:    
+</p>
+
+```python
+        lab = cleanlab.Datalab(data=dataset, label="column_name_for_labels")
+        # Fit any ML model, get its feature_embeddings & pred_probs for your data
+        lab.find_issues(features=feature_embeddings, pred_probs=pred_probs)
+        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).
+
+
+---
+
+
+## Run cleanlab open-source
+
+This cleanlab package runs on Python 3.10+ and supports Linux, macOS, as well as 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).
+
+**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.
+
+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).
+
+![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/flowchart.png)
+
+
+## Use cleanlab with any model and in most ML tasks
+
+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 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)
+
+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/).
+
+
+## So fresh, so 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:
+
+```python
+
+# cleanlab works with **any classifier**. Yup, you can use PyTorch/TensorFlow/OpenAI/XGBoost/etc.
+cl = cleanlab.classification.CleanLearning(sklearn.YourFavoriteClassifier())
+
+# cleanlab finds data and label issues in **any dataset**... in ONE line of code!
+label_issues = cl.find_label_issues(data, labels)
+
+# cleanlab trains a robust version of your model that works more reliably with noisy data.
+cl.fit(data, labels)
+
+# cleanlab estimates the predictions you would have gotten if you had trained with *no* label issues.
+cl.predict(test_data)
+
+# A universal data-centric AI tool, cleanlab quantifies class-level issues and overall data quality, for any dataset.
+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 is:
+
+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,...)
+<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.
+</p>
+
+
+## Citation and related publications
+
+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>
+
+    @article{northcutt2021confidentlearning,
+        title={Confident Learning: Estimating Uncertainty in Dataset Labels},
+        author={Curtis G. Northcutt and Lu Jiang and Isaac L. Chuang},
+        journal={Journal of Artificial Intelligence Research (JAIR)},
+        volume={70},
+        pages={1373--1411},
+        year={2021}
+    }
+
+</details>
+
+<details><summary><a href="https://arxiv.org/abs/1705.01936">Rank Pruning (UAI '17)</a> (<b>click to show bibtex</b>) </summary>
+
+    @inproceedings{northcutt2017rankpruning,
+        author={Northcutt, Curtis G. and Wu, Tailin and Chuang, Isaac L.},
+        title={Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels},
+        booktitle = {Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence},
+        series = {UAI'17},
+        year = {2017},
+        location = {Sydney, Australia},
+        numpages = {10},
+        url = {http://auai.org/uai2017/proceedings/papers/35.pdf},
+        publisher = {AUAI Press},
+    }
+
+</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>
+
+    @inproceedings{kuan2022labelquality,
+        title={Model-agnostic label quality scoring to detect real-world label errors},
+        author={Kuan, Johnson and Mueller, Jonas},
+        booktitle={ICML DataPerf Workshop},
+        year={2022}
+    }
+
+</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>
+
+    @inproceedings{wang2022tokenerrors,
+        title={Detecting label errors in token classification data},
+        author={Wang, Wei-Chen and Mueller, Jonas},
+        booktitle={NeurIPS Workshop on Interactive Learning for Natural Language Processing (InterNLP)},
+        year={2022}
+    }
+
+</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>
+
+    @inproceedings{thyagarajan2023multilabel,
+        title={Identifying Incorrect Annotations in Multi-Label Classification Data},
+        author={Thyagarajan, Aditya and Snorrason, Elías and Northcutt, Curtis and Mueller, Jonas},
+        booktitle={ICLR Workshop on Trustworthy ML},
+        year={2023}
+    }
+
+</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>
+
+    @inproceedings{tkachenko2023objectlab,
+        title={ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data},
+        author={Tkachenko, Ulyana and Thyagarajan, Aditya and Mueller, Jonas},
+        booktitle={ICML Workshop on Data-centric Machine Learning Research},
+        year={2023}
+    }
+
+</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>
+
+    @inproceedings{lad2023segmentation,
+        title={Estimating label quality and errors in semantic segmentation data via any model},
+        author={Lad, Vedang and Mueller, Jonas},
+        booktitle={ICML Workshop on Data-centric Machine Learning Research},
+        year={2023}
+    }
+
+</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>
+
+    @inproceedings{zhou2023errors,
+        title={Detecting Errors in a Numerical Response via any Regression Model},
+        author={Zhou, Hang and Mueller, Jonas and Kumar, Mayank and Wang, Jane-Ling and Lei, Jing},
+        booktitle={Journal of Data-centric Machine Learning Research},
+        year={2024}
+    }
+
+</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>
+
+    @inproceedings{kuan2022ood,
+        title={Back to the Basics: Revisiting Out-of-Distribution Detection Baselines},
+        author={Kuan, Johnson and Mueller, Jonas},
+        booktitle={ICML Workshop on Principles of Distribution Shift},
+        year={2022}
+    }
+
+</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>
+
+    @inproceedings{goh2022crowdlab,
+        title={CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple annotators},
+        author={Goh, Hui Wen and Tkachenko, Ulyana and Mueller, Jonas},
+        booktitle={NeurIPS Human in the Loop Learning Workshop},
+        year={2022}
+    }
+
+</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>
+
+    @inproceedings{goh2023activelab,
+        title={ActiveLab: Active Learning with Re-Labeling by Multiple Annotators},
+        author={Goh, Hui Wen and Mueller, Jonas},
+        booktitle={ICLR Workshop on Trustworthy ML},
+        year={2023}
+    }
+
+</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>
+
+    @inproceedings{cummings2023drift,
+        title={Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors},
+        author={Cummings, Jesse and Snorrason, Elías and Mueller, Jonas},
+        booktitle={ICML Workshop on Data-centric Machine Learning Research},
+        year={2023}
+    }
+
+</details>
+
+To understand/cite other cleanlab functionality not described above, check out our [Blog](https://cleanlab.ai/blog/learn).
+
+
+## Other resources
+
+- [Example Notebooks demonstrating practical applications of this package](https://github.com/cleanlab/examples)
+
+- [Cleanlab Blog](https://cleanlab.ai/blog/learn)
+
+- [Blog post: Introduction to Confident Learning](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)
+
+- [Introduction to Data-centric AI (MIT IAP Course)](https://dcai.csail.mit.edu/)
+
+- [Release notes for past versions](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).
+
+**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).