fix TOC contents link to fasttext wrapper

Author: jwmueller

README.md

@@ -18,7 +18,7 @@ To quickly learn how to run cleanlab on your own data, first check out the [quic
 | 8   | [fine_tune_LLM](fine_tune_LLM/LLM_with_noisy_labels_cleanlab.ipynb) | Fine-tuning OpenAI language models with noisily labeled text data                         |
 | 9   | [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).                                                                                          |
 | 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).                                                                                                    |
+| 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](fasttext_amazon_reviews/fasttext_wrapper.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).                                                                                |
 | 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.                                                             |