From f85155bb4e5d5643f878a2ccaa9363a1896619ce Mon Sep 17 00:00:00 2001 From: Jonas Mueller <1390638+jwmueller@users.noreply.github.com> Date: Thu, 21 Dec 2023 19:58:46 -0800 Subject: [PATCH] Entity recognition link in table of contents (#84) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 955d6ba..223030f 100644 --- a/README.md +++ b/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. |