Entity recognition link in table of contents (#84)

README.md

@@ -25,7 +25,7 @@ To quickly learn how to run cleanlab on your own data, first check out the [quic
 | 15  | [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.                                          |
 | 16  | [outlier_detection_cifar10](outlier_detection_cifar10/outlier_detection_cifar10.ipynb)                                             | Train AutoML for image classification and use it to detect out-of-distribution images.                                                                                                 |
 | 17  | [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. |
-| 18  | [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**.      |
+| 18  | [entity_recognition](entity_recognition/)                                             | Train Transformer model  for Named Entity Recognition and produce out-of-sample `pred_probs` for **cleanlab.token_classification**.      |
 | 19  | [transformer_sklearn](transformer_sklearn/transformer_sklearn.ipynb)                                             | How to use `KerasWrapperModel` to make any Keras model sklearn-compatible, demonstrated here for a BERT Transformer.      |
 | 20  | [cnn_coteaching_cifar10](cnn_coteaching_cifar10/README.md)                                               | 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).  |
 | 21  | [non_iid_detection](non_iid_detection/non_iid_detection.ipynb)  | Use Datalab to detect non-IID sampling (e.g. drift) in datasets based on numeric features or embeddings. |