Omarrran/BBC_Eng_News_Articles_dataset

BBC News Articles Dataset

Dataset Description

A collection of 2,225 news articles from BBC, suitable for text classification, summarization, and NLP tasks.

Dataset Summary

Metric	Value
Total Articles	2,225
Unique Articles	2,092
Columns	filename, article_text
Language	English
Source	BBC News

Dataset Structure

Data Fields

Field	Type	Description
filename	string	Unique identifier/filename for each article
article_text	string	Full text content of the news article

Text Statistics

Metric	Min	Max	Mean	Median	Std
Characters	470	25,453	2,232	1,935	1,364
Words	84	4,428	379	326	238
Sentences	4	248	19	16	13

Vocabulary Statistics

Metric	Value
Total Words (corpus)	815,279
Unique Words (vocabulary)	27,205
Vocabulary (excl. stopwords)	27,070
Lexical Diversity	0.0334
Avg Words per Article	366.4

Top 10 Most Frequent Words

Word	Frequency
said	7,253
mr	2,994
would	2,628
also	2,156
people	2,041
new	1,898
us	1,818
year	1,813
one	1,752
could	1,534

Usage

Loading with Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset("Omarrran/BBC_Eng_News_Articles_dataset")

# Access training data
train_data = dataset['train']

# View first article
print(train_data[0]['article_text'][:500])

Loading with Pandas

import pandas as pd
from datasets import load_dataset

dataset = load_dataset("Omarrran/BBC_Eng_News_Articles_dataset")
df = dataset['train'].to_pandas()

# Basic exploration
print(f"Total articles: {len(df)}")
print(df.head())

Text Classification Example

from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

# Load data
df = dataset['train'].to_pandas()

# If categories available from filename
df['category'] = df['filename'].apply(lambda x: x.split('/')[0])

# Split
X_train, X_test, y_train, y_test = train_test_split(
    df['article_text'], df['category'], test_size=0.2, random_state=42
)

# Vectorize and train
vectorizer = TfidfVectorizer(max_features=5000, stop_words='english')
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)

clf = MultinomialNB()
clf.fit(X_train_vec, y_train)
print(f"Accuracy: {clf.score(X_test_vec, y_test):.2%}")

Summarization Example

from transformers import pipeline

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Summarize first article
article = dataset['train'][0]['article_text']
summary = summarizer(article[:1024], max_length=130, min_length=30)
print(summary[0]['summary_text'])

Suitable Tasks

This dataset is ideal for:

• Text Classification: Categorize articles by topic
• Summarization: Generate article summaries
• Named Entity Recognition: Extract entities from news
• Keyword Extraction: Identify key topics
• Topic Modeling: Discover latent themes
• Sentiment Analysis: Analyze article tone
• Text Generation: Fine-tune language models
• Information Retrieval: Build search systems

Data Quality

Check	Status
Empty/null articles	0 found
Encoding issues	Clean (UTF-8)

Limitations

• Dataset is limited to BBC News articles
• May contain temporal bias based on collection period
• English language only
• News domain specific vocabulary

Citation

@dataset{bbc_news_articles,
  title = {BBC_Eng_News_Articles_dataset_hnm},
  Author ={Haq Nawaz Malik}
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Omarrran/BBC_Eng_News_Articles_dataset/}}
}

License

This dataset is provided for research and educational purposes under the MIT License.

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