update links in readme to latest source code

Author: jwmueller

README.md

@@ -16,7 +16,7 @@ To quickly learn how to run cleanlab on your own data, first check out the [quic
 | 6   | [find_tabular_errors](find_tabular_errors/find_tabular_errors.ipynb) | Handle mislabeled [tabular data](https://github.com/cleanlab/s/blob/master/student-grades-demo.csv) to improve a XGBoost classifier.                         |
 | 7   | [cnn_mnist](cnn_mnist/find_label_errors_cnn_mnist.ipynb)                                                                         | Finding label errors in MNIST image data with a [Convolutional Neural Network](https://github.com/cleanlab/cleanlab/blob/master/cleanlab/experimental/mnist_pytorch.py).                                                                                          |
 | 8   | [huggingface_keras_imdb](huggingface_keras_imdb/huggingface_keras_imdb.ipynb)                                             |  CleanLearning for text classification with Keras Model + pretrained BERT backbone and Tensorflow Dataset.         |
-| 9   | [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   | [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).                                                                                                    |
 | 10   | [multiannotator_cifar10](multiannotator_cifar10/multiannotator_cifar10.ipynb)                                             | Iteratively improve consensus labels and trained classifier from data labeled by multiple annotators.                                                            |
 | 11  | [active_learning_multiannotator](active_learning_multiannotator/active_learning.ipynb)                                             | Improve model performance by iteratively collecting additional labels from annotators. This active learning pipeline allows for examples labeled in batches by multiple annotators.                                                             |
 | 12  | [outlier_detection_cifar10](outlier_detection_cifar10/outlier_detection_cifar10.ipynb)                                             | Train AutoML for image classification and use it to detect out-of-distribution images.                                                                                                 |