Update README.md

README.md

@@ -77,50 +77,22 @@ dataset_info:
 license: cc
 language:
 - en
-pretty_name: breast-cancer-wisconsin
+pretty_name: Breast Cancer Wisconsin
 size_categories:
 - n<1K
+task_categories:
+- text-classification
+tags:
+- biology
 ---
-# breast-cancer-wisconsin
+# Breast Cancer Wisconsin (Teeny-Tiny Castle)
 
-## Overview 
+This dataset is part of the tutorial tied to the [Teeny-Tiny Castle](https://github.com/Nkluge-correa/TeenyTinyCastle), an open-source repository containing educational tools for AI Ethics and Safety research. 
 
-The dataset contains features computed from digitized images of breast cancer biopsies, which are used to predict 
-whether a breast mass is benign (non-cancerous) or malignant (cancerous).
-
-## Dataset Details
-
-The original dataset is the [Breast Cancer Wisconsin (Diagnostic)](https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic). This file concerns credit card applications. 
-The dataset is based on features computed from digitized images of breast mass tissue samples. These features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. 
-Based on these features, the goal is to predict whether the mass is benign or malignant.
-
-```latex
-@inproceedings{Street1993NuclearFE,
-  title={Nuclear feature extraction for breast tumor diagnosis},
-  author={William Nick Street and William H. Wolberg and Olvi L. Mangasarian},
-  booktitle={Electronic imaging},
-  year={1993},
-  url={https://api.semanticscholar.org/CorpusID:14922543}
-}
-```
-
-- Dataset Name: breast-cancer-wisconsin
-- Language:  English
-- Total Size: 569 demonstrations
-
-## Contents
-The dataset consists of a data frame with 30 columns + diagnosis = M (37,3%) and B (62,7%).
-
-
-## How to use
+## How to Use
 
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("AiresPucrs/breast-cancer-wisconsin", split='train')
-
+dataset = load_dataset("AiresPucrs/breast-cancer-wisconsin", split = 'train')
 ```
-
-## License
-
-This dataset is licensed under a [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0) license.