Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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TechCrunch News Articles Dataset

📊 Dataset Overview

This dataset contains 10,265 high-quality news articles scraped from TechCrunch, one of the leading technology news websites. The dataset includes comprehensive article content, metadata, and quality assessments suitable for various NLP tasks including text classification, sentiment analysis, summarization, and content generation.

🎯 Key Features

10,265 articles with full text content
High-quality filtering with quality scores (avg: 0.96)
Content cleaned and optimized for ML applications
Rich metadata including publish date, bias analysis, and quality metrics
Comprehensive bias analysis for ethical AI development
Ready for NLP tasks including classification, summarization, and generation
HuggingFace optimized format for seamless integration

📈 Dataset Statistics

Metric	Value
Total Articles	10,265
Average Content Length	4,263 characters
Average Word Count	686 words
Average Quality Score	0.957
Date Range	2023-04-10 to 2025-07-09
File Size (JSON)	51.8 MB
File Size (Parquet)	26.3 MB

🏗️ Data Structure

The dataset contains the following columns:

title: Article headline
content: Full article text content (cleaned)
publish_date: Publication timestamp
url: Original article URL
word_count: Number of words in content
quality_score: Automated quality assessment (0-1)
content_hash: MD5 hash for deduplication
bias_analysis: JSON string containing comprehensive bias assessment

🔍 Quality Assurance

This dataset implements multiple quality control measures:

Content Filtering: Minimum 16 words per article
Content Cleaning: Navigation elements and website artifacts removed
Quality Scoring: Automated assessment based on content length, structure, and readability
Deduplication: Content hash-based duplicate removal
Bias Analysis: Comprehensive bias detection and scoring
Manual Review: Sample validation for quality assurance

Quality Score Distribution

Minimum: 0.600
Average: 0.957
Maximum: 1.000

🚀 Getting Started

Loading the Dataset

# Using datasets library (recommended)
from datasets import load_dataset
dataset = load_dataset('abhilash88/techcrunch-articles')

# Using pandas for Parquet format
import pandas as pd
df = pd.read_parquet('path/to/techcrunch_articles.parquet')

# Access the training split
df = dataset['train'].to_pandas()

Basic Analysis

# Dataset overview
print(f"Total articles: {len(dataset['train']):,}")
print(f"Features: {dataset['train'].features}")

# Convert to pandas for analysis
df = dataset['train'].to_pandas()
print(f"Average word count: {df['word_count'].mean():.0f}")
print(f"Date range: {df['publish_date'].min()} to {df['publish_date'].max()}")

# Quality distribution
print(f"Quality score distribution:")
print(df['quality_score'].describe())

📜 Legal and Attribution

Data Source

Original Source: TechCrunch (techcrunch.com)
Scraping Method: Respectful automated collection with rate limiting
Usage Rights: Educational and research purposes
Attribution: Please cite this dataset in academic work

Recommended Citation

@dataset{techcrunch_articles_2025,
  title={TechCrunch News Articles Dataset},
  author={Abhilash Sahoo},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/abhilash88/techcrunch-articles},
  note={10,265 high-quality technology news articles with bias analysis}
}

📞 Contact

Creator: Abhilash Sahoo
Hugging Face: @abhilash88
Issues: Please use the dataset discussion section

🚀 Ready to build amazing NLP applications with high-quality technology news content!

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