Update README.md

README.md

@@ -16,54 +16,21 @@ configs:
     path: data/train-*
 language:
 - en
-pretty_name: stopwords-en
+pretty_name: Stopwords EN
 size_categories:
 - n<1K
 license: apache-2.0
 task_categories:
 - text-classification
 ---
-# stopwords-en
+# Stopwords EN (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 stopword-en dataset contains a stopword list of frequently used in the English language.
-These words do not carry significant meaning and are often removed from text data during preprocessing and training in shallower models
-on a text classification task.
-
-## Dataset Details
-
-```
-- Dataset Name: stopwords-en
-- Total Size: 220 demonstrations
-```
-
-## Contents
-
-The dataset consists of one column with strings like all the letters of the Roman alphabet, numbers from 1 to 10, 
-and words frequently used in the English language, such as "day", "days", "know", "went", "like", etc.
-
-## How to use
+## How to Use
 
 ```python
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-# Download the English stopword list.
-stopwords = load_dataset('AiresPucrs/stopwords-en', split='train')['stopwords']
-
-# Create a vectorization object via `TfidfVectorizer`
-vectorizer = TfidfVectorizer(min_df=10,
-                             max_features=100000,
-                             analyzer='word',
-                             ngram_range=(1, 2),
-                             stop_words=stopwords, # Our list of stopwords.
-                             lowercase=True)
-
-# Fit the TfidfVectorizer to our dataset.
-vectorizer.fit(dataset['text'])
+from datasets import load_dataset
 
+dataset = load_dataset("AiresPucrs/stopwords-en", split = 'train')
 ```
-
-## License
-
-This dataset is licensed under the Apache License, version 2.0.