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 ## Table of Contents
 
+|     | Example                                                                                        | Description                                                                                                                                                                                                                                                                  |
+| --- | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | 1   | [datalab](datalab_image_classification/README.md)                              | Use Datalab to detect various types of data issues in (a subset of) the Caltech-256 image classification dataset.                        |
 | 2   | [find_label_errors_iris](find_label_errors_iris/find_label_errors_iris.ipynb)                                         | Find label errors introduced into the Iris classification dataset.                                                 |
 | 3   | [classifier_comparison](classifier_comparison/classifier_comparison.ipynb)                                     | Use CleanLearning to train 10 different classifiers on 4 dataset distributions with label errors.                     |
@@ -18,7 +20,7 @@ To quickly learn how to run cleanlab on your own data, first check out the [quic
 | 10   | [huggingface_keras_imdb](huggingface_keras_imdb/huggingface_keras_imdb.ipynb)                                             |  CleanLearning for text classification with Keras Model + pretrained BERT backbone and Tensorflow Dataset.         |
 | 11   | [fasttext_amazon_reviews](fasttext_amazon_reviews/fasttext_amazon_reviews.ipynb)                         | Finding label errors in Amazon Reviews text dataset using a cleanlab-compatible [FastText model](https://github.com/cleanlab/cleanlab/blob/master/cleanlab/models/fasttext.py).                                                                                                    |
 | 12   | [multiannotator_cifar10](multiannotator_cifar10/multiannotator_cifar10.ipynb)                                             | Iteratively improve consensus labels and trained classifier from data labeled by multiple annotators.                                                            |
-| 13 | [llm_evals_w_crowdlab](llm_evals_w_crowdlab/llm_evals_w_crowdlab.ipynb) | Reliable LLM Evaluation with multiple human/AI reviewers of varying competency (via CROWDLAB and LLM-as-judge GPT token probabilities). |
+| 13 | [llm_evals_w_crowdlab](llm_evals_w_crowdlab/llm_evals_w_crowdlab.ipynb) | Reliable LLM Evaluation with multiple human/AI reviewers of varying competency (via CROWDLAB and LLM-as-judge GPT token probabilities).                                                                                |
 | 14  | [active_learning_multiannotator](active_learning_multiannotator/active_learning.ipynb)                                             | Improve a classifier model by iteratively collecting additional labels from data annotators. This active learning pipeline considers data labeled in batches by multiple (imperfect) annotators.                                                             |
 | 15  | [active_learning_single_annotator](active_learning_single_annotator/active_learning_single_annotator.ipynb)                                             | Improve a classifier model by iteratively labeling batches of currently-unlabeled data.  This demonstrates a standard active learning pipeline with *at most one label* collected for each example (unlike our multi-annotator active learning notebook which allows re-labeling).                                                            |
 | 16  | [active_learning_transformers](active_learning_transformers/active_learning.ipynb)                                             | Improve a Transformer model for classifying politeness of text by iteratively labeling and re-labeling batches of data using multiple annotators.  If you haven't done active learning with re-labeling, try the [active_learning_multiannotator](active_learning_multiannotator/active_learning.ipynb) notebook first.                                          |