reorder multilabel <-> token examples

README.md

@@ -18,8 +18,8 @@ To quickly learn the basics of running cleanlab on your own data, we recommend f
 | 8   | [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/experimental/fasttext.py)                                                                                                    |
 | 9   | [multiannotator_cifar10](multiannotator_cifar10/multiannotator_cifar10.ipynb)                                             | Iteratively improve consensus labels and trained classifier from data labeled by mulitple annotators.                                                            |
 | 10  | [outlier_detection_cifar10](outlier_detection_cifar10/outlier_detection_cifar10.ipynb)                                             | Train AutoML for image classification and use it to detect out-of-distribution images.                                                                                                 |
-| 11  | [entity_recognition](entity_recognition/entity_recognition_training.ipynb)                                             | Train Transformer model  for Named Entity Recognition and produce out-of-sample `pred_probs` for cleanlab.token_classification.      |
-| 12  | [multilabel_classification](multilabel_classification/image_tagging.ipynb)                                               | Find label errors in an image tagging dataset ([CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)) using a [Pytorch model](multilabel_classification/pytorch_network_training.ipynb) you can easily train for multi-label classification. |
+| 11  | [multilabel_classification](multilabel_classification/image_tagging.ipynb)                                               | Find label errors in an image tagging dataset ([CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)) using a [Pytorch model](multilabel_classification/pytorch_network_training.ipynb) you can easily train for multi-label classification. |
+| 12  | [entity_recognition](entity_recognition/entity_recognition_training.ipynb)                                             | Train Transformer model  for Named Entity Recognition and produce out-of-sample `pred_probs` for cleanlab.token_classification.      |
 | 13  | [cnn_coteaching_cifar10](cnn_coteaching_cifar10)                                               | Train a [Convolutional Neural Network](https://github.com/cleanlab/cleanlab/blob/master/cleanlab/experimental/cifar_cnn.py) on noisily labeled Cifar10 image data using cleanlab with [coteaching](https://github.com/cleanlab/cleanlab/blob/master/cleanlab/experimental/coteaching.py).  |