Datasets:

pohsjxx
/

fineweb-med-test

FineWeb-Med is a high-quality dataset of medical and healthcare-related web content, extracted and processed from Common Crawl using the FineWeb methodology with specialized medical filtering.

Dataset Summary

This dataset contains 9 documents with approximately 0 tokens, focusing exclusively on medical, healthcare, and related topics from the web. It serves as a specialized complement to general web datasets like FineWeb for training medical AI models.

Data Processing

The dataset was created using the 🏭 datatrove library with enhanced medical-specific processing. You can find the complete processing script in our repository.

Processing Pipeline

Data Source: Common Crawl dump CC-MAIN-2023-40
URL Filtering: Remove malicious and NSFW websites using blocklists and subword detection
Text Extraction: Trafilatura for high-quality text extraction from raw HTML WARC files
Language Filtering: FastText language detection, keeping only English content (score > 0.65)
Medical Content Filtering: Documents must contain at least one of 26 medical keywords
Length Filtering: Documents must be at least 200 words to ensure substantial content
Quality Filtering:
- Gopher repetition and quality filters
- C4 quality filters (excluding terminal punctuation rule)
- FineWeb custom filters for list-like documents and formatting issues
Token Counting: GPT-2 tokenizer for token statistics

Medical Keywords

The dataset employs specialized filtering for medical content using these keywords:

Core Medical Terms: medical, diagnosis, treatment, patient, doctor, symptom, therapy, prescription, clinical, healthcare

Healthcare Facilities: hospital, clinic, nurse, surgery, pharmacy, pharmaceutical

Health Conditions: disease, disorder, condition, medication, drug, vaccine, epidemic, pandemic

Wellness Terms: health, wellness

Data Format

Each example is a JSON object with the following fields:

Core Fields

text (string): The extracted and cleaned text content
id (string): Unique identifier from the original WARC record
metadata (dict): Extended metadata information

Metadata Fields

dump (string): Common Crawl dump identifier (e.g., "CC-MAIN-2023-50")
dataset (string): Dataset identifier ("fineweb-med")
url (string): Original webpage URL
date (string): Crawl timestamp in ISO format
file_path (string): S3 path to source WARC file
language (string): Detected language (always "en" for this dataset)
language_score (float): Language detection confidence score
token_count (int): Number of tokens using GPT-2 tokenizer

Usage

Loading the Dataset

from datasets import load_dataset

# Load the complete dataset
dataset = load_dataset("pohsjxx/fineweb-med-test")

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

# Example usage
for example in train_data:
    print(f"Text: {example['text'][:100]}...")
    print(f"URL: {example['metadata']['url']}")
    print(f"Tokens: {example['metadata']['token_count']}")
    break

Medical-Specific Filtering

# Filter for clinical documents
clinical_docs = [doc for doc in dataset['train']
                 if 'clinical' in doc['text'].lower()]

# Filter by token count for model training
suitable_docs = [doc for doc in dataset['train']
                 if 512 <= doc['metadata']['token_count'] <= 2048]

Statistics

Metric	Value
Total Documents	9
Total Tokens	0
Average Tokens/Document	0.0
Token Range	0 - 0
Median Tokens/Document	0
Source Dump	CC-MAIN-2023-40
Language	English only
Medical Focus	Healthcare & medical content

Dataset Creation

Curation Rationale

While FineWeb provides excellent general web text data, specialized domains like healthcare require targeted datasets. FineWeb-Med addresses this need by applying medical-specific filtering to create a high-quality, domain-focused dataset suitable for:

Training medical language models
Fine-tuning healthcare AI applications
Medical text analysis and NLP research
Healthcare chatbot development

Source Data

Primary Source: Common Crawl web crawl data

Dump: CC-MAIN-2023-40
Time Period: 2023-2024 web crawl
Content Type: Public web pages with medical relevance

Annotations

We augment samples with automatic annotations:

language & language_score: Generated by FastText language classifier
token_count: Calculated using GPT-2 tokenizer

Considerations for Using the Data

Social Impact

This dataset enables more accessible development of healthcare AI applications, potentially improving medical text understanding and patient care through better language models.

Discussion of Biases

The dataset inherits biases from web-sourced medical content, which may reflect:

Geographic biases in healthcare information availability
Language biases (English-only content)
Platform biases from different healthcare websites

Limitations

Code Content: Limited due to filtering steps; supplement with code-specific datasets if needed
Medical Accuracy: Web content may contain outdated or inaccurate medical information
PII Concerns: Despite anonymization, some personal health information may remain
Specialized Domains: May not cover all medical specialties equally

Additional Information

Licensing Information

License: Apache 2.0 Additional Terms: Subject to Common Crawl's Terms of Use

Personal and Sensitive Information

We anonymize:

Email addresses → email@example.com or firstname.lastname@example.org
Public IP addresses → Randomly assigned non-responsive IPs

For PII removal requests, please create an issue in our repository.

Future Work

We plan to expand FineWeb-Med with:

Additional medical domains and specialties
Multi-language medical content
Enhanced quality filtering for medical text
Integration with medical knowledge bases

Citation Information

@dataset{fineweb_med,
  title={FineWeb-Med: Medical-Focused Web Dataset},
  author={Generated using datatrove FineWeb methodology with medical filtering},
  year={2024},
  url={https://huggingface.co/datasets/pohsjxx/fineweb-med-test}
}

Built with ❤️ using the FineWeb methodology and datatrove

🍷🏥 FineWeb-Med: Medical-Focused Web Dataset