🌐 Essential-Web: Complete 24-Trillion Token Dataset (Upload in-progress...)

📋 Dataset Description

Essential-Web is a 24-trillion-token web dataset with document-level metadata designed for flexible dataset curation. The dataset provides metadata including subject matter classification, web page type, content complexity, and document quality scores for each of the 23.6 billion documents.

Researchers can filter and curate specialized datasets using the provided metadata, reducing the need for custom preprocessing pipelines and domain-specific classifiers.

🔍 Free Decimal Correspondence (FDC) Taxonomy

Essential-Web uses the Free Decimal Correspondence, a Dewey Decimal-inspired open taxonomy with 12 main categories for classifying web content. This systematic approach enables precise domain filtering and dataset curation.

For help navigating FDC codes, see: https://www.librarything.com/mds

⚙️ Dataset Creation

Essential-Web was created using a comprehensive processing pipeline starting from Common Crawl data:

📥 Source Data

DCLM Pool: 89 resiliparse-extracted Common Crawl WARC snapshots (CC-MAIN-2013-20 to CC-MAIN-2022-49)
Additional Snapshots: 12 additional snapshots extracted from CC-MAIN-2023-06 to CC-MAIN-2024-38 using resiliparse
Total: 101 Common Crawl snapshots processed

🔧 Processing Pipeline

Document ID Generation: Using xxhash.xxh3_64_intdigest for unique document identification
Global Deduplication: Hash-based deduplication across all 101 snapshots
Minhash LSH Deduplication: Snapshot-level deduplication with Jaccard threshold of 0.7 (14 bands, 9 rows per band)
Quality Annotation: Statistical and model-based quality signals using RedPajama-Data-V2 pipeline variant, including DCLM-baseline fastText classifier
Quality Filtering: Manual tuned filters to retain high-quality English documents while preserving math and code content
Taxonomy Labeling: Classification of every document using EAI-Taxonomy-0.5b (~90,000 AMD MI300x GPU-hours)

🎯 Performance & Validation

We've curated example domain-specific datasets from Essential-Web using simple metadata filters, showing competitive performance relative to top performing web-curated datasets:

🧮 Math: within 8.0% of web-curated baselines
💻 Web Code: 14.3% above web-curated baselines
🔬 STEM: 24.5% above web-curated baselines
🩺 Medical: 8.6% above web-curated baselines

Note: These represent initial examples with significant room for further curation and improvement. Comparisons are against web-sourced datasets rather than specialized synthetic datasets.

🚀 Related Datasets & Models

Domain-Specific Datasets

We've curated high-quality domain-specific datasets from Essential-Web:

Math: EssentialAI/eai-taxonomy-math-w-fm
Code: EssentialAI/eai-taxonomy-code-w-dclm
Medical: EssentialAI/eai-taxonomy-med-w-dclm
STEM: EssentialAI/eai-taxonomy-stem-w-dclm

Classification Model

EAI-Taxonomy-0.5b: EssentialAI/eai-taxonomy-0.5b - The efficient classifier used to label Essential-Web documents

🎯 Intended Use

Essential-Web enables researchers to:

🚀 Rapid Curation: Create multi-billion-token domain-specific datasets in minutes using SQL-like filters
🔍 Flexible Exploration: Explore web content across subjects, quality levels, and content types
🏗️ Custom Pipelines: Build specialized training corpora without custom classification infrastructure
🔄 Iterative Improvement: Easily modify and refine dataset composition based on training results
📊 Quality Control: Filter out low-quality content (ads, product listings) while preserving reasoning-dense documents

Dataset Schema Documentation

Overview

This dataset contains web-crawled text data with comprehensive metadata, quality signals, and taxonomic classifications. Each record represents a document extracted from web archives with detailed provenance tracking and quality assessment metrics.

Core Fields

Field	Type	Description	Path
`id`	`Int64`	Unique identifier based on document hash	`id`
`text`	`String`	The main textual content of the document	`text`

EAI Taxonomy Classification

Comprehensive hierarchical classification system with primary and secondary labels - the most important feature of this dataset. The taxonomy is designed to provide detailed subject categorization, document type identification, content quality assessment, and extraction quality indicators.

Free Decimal Correspondence (FDC)

A Dewey Decimal-inspired classification system with 3-level hierarchical labels. The FDC provides nested categories where each successive level refines its parent category. It's designed to be compatible with the Dewey Decimal System for library cataloging.

Level Structure:

Level 1: Top-level categories (0-9) covering broad subject areas like General works, Philosophy, Religion, Social Sciences, etc.
Level 2: Sub-divisions (00-99) that refine Level 1 categories
Level 3: Specific categories (000-999) that further refine Level 2 categories

Component	Description	Path
Primary Code	Main classification code	`eai_taxonomy.free_decimal_correspondence.primary.code`
Primary Level 1	Top-level category (0=General works, 1=Philosophy, 2=Religion, 3=Social Sciences, 4=Language, 5=Science, 6=Technology, 7=Arts, 8=Literature, 9=History/Geography)	`eai_taxonomy.free_decimal_correspondence.primary.labels.level_1`
Primary Level 2	Mid-level category	`eai_taxonomy.free_decimal_correspondence.primary.labels.level_2`
Primary Level 3	Specific category	`eai_taxonomy.free_decimal_correspondence.primary.labels.level_3`
Secondary Code	Alternative classification code	`eai_taxonomy.free_decimal_correspondence.secondary.code`
Secondary Level 1	Alternative top-level category	`eai_taxonomy.free_decimal_correspondence.secondary.labels.level_1`
Secondary Level 2	Alternative mid-level category	`eai_taxonomy.free_decimal_correspondence.secondary.labels.level_2`
Secondary Level 3	Alternative specific category	`eai_taxonomy.free_decimal_correspondence.secondary.labels.level_3`

We recommend this viewer for easily navigating the FDC categories when curating filters: https://www.librarything.com/mds

Bloom's Taxonomy Integration

Based on Anderson and Krathwohl's 2001 revision of Bloom's Taxonomy of Educational Objectives, providing two complementary categorization dimensions for educational content analysis.

Knowledge Domain

Categorizes the type of knowledge demonstrated in the document:

Component	Description	Path
Primary Code	Main knowledge domain code	`eai_taxonomy.bloom_knowledge_domain.primary.code`
Primary Label	Main knowledge domain label	`eai_taxonomy.bloom_knowledge_domain.primary.label`
Secondary Code	Alternative knowledge domain code	`eai_taxonomy.bloom_knowledge_domain.secondary.code`
Secondary Label	Alternative knowledge domain label	`eai_taxonomy.bloom_knowledge_domain.secondary.label`

Possible Values:

Code	Label	Description
`-1`	Abstain	Unable to determine
`1`	Factual	Basic elements to learn or solve problems
`2`	Conceptual	Interrelationships between basic elements within larger context
`3`	Procedural	Methods and techniques in the discipline
`4`	Metacognitive	Awareness of how learning works in relation to oneself

Cognitive Processing Level

Assesses the learning and thinking skill levels demonstrated by the document author:

Component	Description	Path
Primary Code	Main cognitive process code	`eai_taxonomy.bloom_cognitive_process.primary.code`
Primary Label	Main cognitive process label	`eai_taxonomy.bloom_cognitive_process.primary.label`
Secondary Code	Alternative cognitive process code	`eai_taxonomy.bloom_cognitive_process.secondary.code`
Secondary Label	Alternative cognitive process label	`eai_taxonomy.bloom_cognitive_process.secondary.label`

Possible Values:

Code	Label	Description
`-1`	Abstain	Unable to determine
`1`	Remember	Retrieve relevant knowledge from memory
`2`	Understand	Determine meaning of instructional messages
`3`	Apply	Use a procedure in a given situation
`4`	Analyze	Break materials into components and determine relationships
`5`	Evaluate	Make judgments based on criteria and standards
`6`	Create	Create new or original work

Document Characteristics

Document Type v1

In-house classification of common web document types and formats:

Component	Description	Path
Primary Code	Main document type code	`eai_taxonomy.document_type_v1.primary.code`
Primary Label	Main document type label	`eai_taxonomy.document_type_v1.primary.label`
Secondary Code	Alternative document type code	`eai_taxonomy.document_type_v1.secondary.code`
Secondary Label	Alternative document type label	`eai_taxonomy.document_type_v1.secondary.label`

Possible Values:

Code	Label	Examples
`-1`	Abstain	Unable to classify
`1`	News/Editorial	CNN articles, opinion columns
`2`	Academic/Research	ArXiv papers, research articles
`3`	Reference/Encyclopedic/Educational	FAQs, Wikipedia entries
`4`	Code/Software	GitHub repos, code examples
`5`	Social/Forum	Conversation threads, Q&A boards
`6`	Promotional/Advertisement	Product pages, calls to action
`7`	Search/Directory/Bibliography	Link pages, search results
`8`	Adult/Pornographic	Adult content
`9`	Personal/Misc	Blogs, user profiles
`10`	Machine-Generated	Lorem ipsum, garbled text
`11`	Legal/Regulatory	Contracts, terms of service
`12`	Government/Political	Legislation, press releases
`13`	Literary/Creative	Poems, short stories
`14`	Reviews/Critiques	Film critiques, product reviews
`15`	E-Commerce/Marketplace	eBay listings, Amazon pages
`16`	Images/Videos/Audio	YouTube videos, Imgur pages
`17`	Other/Unclassified	Documents that resist classification

Document Type v2

Updated classification based on WebOrganizer taxonomy with refined categories for improved document classification accuracy:

Component	Description	Path
Primary Code	Main document type code (v2)	`eai_taxonomy.document_type_v2.primary.code`
Primary Label	Main document type label (v2)	`eai_taxonomy.document_type_v2.primary.label`
Secondary Code	Alternative document type code (v2)	`eai_taxonomy.document_type_v2.secondary.code`
Secondary Label	Alternative document type label (v2)	`eai_taxonomy.document_type_v2.secondary.label`

Complete Value Mapping:

Code	Label	Examples
`-1`	Abstain	Documents requiring human review
`1`	About (Org.)	Company about pages, mission statements
`2`	About (Personal)	Personal bios, LinkedIn profiles
`3`	Academic Writing	Research papers, abstracts, dissertations
`4`	Audio Transcript	Interview transcripts, court records, captions
`5`	Comment Section	Reddit threads, blog comments
`6`	Content Listing	Site maps, product catalogs, directory listings
`7`	Creative Writing	Song lyrics, novel excerpts, poetry
`8`	Documentation	API docs, README files, user manuals
`9`	FAQ	FAQ pages, Q&A lists
`10`	Knowledge Article	Wikipedia articles, Britannica entries
`11`	Legal Notices	Privacy policies, license agreements, terms of service
`12`	Listicle	Buzzfeed-style articles, "Top 10" lists
`13`	News (Org.)	Government blog posts, corporate announcements
`14`	News Article	Newspaper articles, CNN content, breaking news
`15`	Nonfiction Writing	Editorials, obituaries, memoirs, opinion pieces
`16`	Personal Blog	Personal journals, diary entries, lifestyle blogs
`17`	Product Page	Product descriptions, course offerings, sales pages
`18`	Q&A Forum	Quora posts, Stack Exchange discussions
`19`	Spam / Ads	SEO keyword stuffing, promotional spam
`20`	Structured Data	Datasheets, glossaries, JSON files, databases
`21`	Customer Support	Help articles, troubleshooting guides
`22`	Truncated	Paywalled sites, image galleries, partial content
`23`	Tutorial	Cooking recipes, WikiHow pages, step-by-step guides
`24`	User Review	Yelp reviews, TripAdvisor feedback, product reviews
`25`	Other/Unclassified	Miscellaneous documents not fitting other categories

Extraction Artifacts

Assessment of technical extraction quality, identifying issues from HTML-to-text conversion:

Component	Description	Path
Primary Code	Main extraction artifact code	`eai_taxonomy.extraction_artifacts.primary.code`
Primary Label	Main extraction artifact label	`eai_taxonomy.extraction_artifacts.primary.label`
Secondary Code	Alternative extraction artifact code	`eai_taxonomy.extraction_artifacts.secondary.code`
Secondary Label	Alternative extraction artifact label	`eai_taxonomy.extraction_artifacts.secondary.label`

Possible Values:

Code	Label	Description
`-1`	Abstain	Unable to determine
`0`	No Artifacts	Clean text with no leftover HTML or irrelevant elements
`1`	Leftover HTML	HTML/code artifacts remaining after extraction
`2`	Text Extraction Errors	Broken math expressions, encoding errors, improperly parsed tables
`3`	Irrelevant Content	Headers, footers, nav menus extracted by mistake
`4`	Indeterminate	Insufficient content to judge

Missing Content

Assessment of content completeness and extraction success:

Component	Description	Path
Primary Code	Main missing content code	`eai_taxonomy.missing_content.primary.code`
Primary Label	Main missing content label	`eai_taxonomy.missing_content.primary.label`
Secondary Code	Alternative missing content code	`eai_taxonomy.missing_content.secondary.code`
Secondary Label	Alternative missing content label	`eai_taxonomy.missing_content.secondary.label`

Possible Values:

Code	Label	Description
`-1`	Abstain	Unable to determine
`0`	No Missing Content	Complete and coherent text
`1`	Truncated Snippets	Obvious "...", incomplete paragraphs, cut-off text
`2`	Click Here References	"Download here", "Click here" without linked content
`3`	Incoherent Flow	Unreadable or illogical flow due to missing context
`4`	Missing Images or Figures	Placeholders or references to missing visual content
`5`	Missing Referenced Data	References to absent tables/datasets (e.g., "See Table 3")
`6`	Indeterminate	Insufficient content to judge

Text Structure Information

Field	Type	Description	Path
Line Start Indices	`List[Int32]`	Starting indices of each line	`line_start_n_end_idx.line_start_idx`
Line End Indices	`List[Int32]`	Ending indices of each line	`line_start_n_end_idx.line_end_idx`

Content Quality Dimensions

Quality assessment inspired by NaturalReasoning and FineWeb efforts to categorize web data by information sophistication.

Reasoning Depth

Assesses the complexity and sophistication of logical reasoning in the document:

Component	Description	Path
Primary Code	Main reasoning depth code	`eai_taxonomy.reasoning_depth.primary.code`
Primary Label	Main reasoning depth label	`eai_taxonomy.reasoning_depth.primary.label`
Secondary Code	Alternative reasoning depth code	`eai_taxonomy.reasoning_depth.secondary.code`
Secondary Label	Alternative reasoning depth label	`eai_taxonomy.reasoning_depth.secondary.label`

Possible Values:

Code	Label	Description
`-1`	Abstain	Unable to determine
`1`	No Reasoning	Facts present but no evidence of reasoning
`2`	Basic Reasoning	Basic analysis with minimal explanation and summarization
`3`	Intermediate Reasoning	Some logical steps connecting ideas and structured thinking
`4`	Advanced Reasoning	Multi-step reasoning and thorough analysis with well-developed explanations
`5`	Exceptional Reasoning	Novel abstractions, theoretical frameworks, long chain-of-thought, original insights, or proofs
`6`	Indeterminate	Insufficient context to judge

Technical Correctness

Evaluates the accuracy and precision of technical information:

Component	Description	Path
Primary Code	Main technical correctness code	`eai_taxonomy.technical_correctness.primary.code`
Primary Label	Main technical correctness label	`eai_taxonomy.technical_correctness.primary.label`
Secondary Code	Alternative technical correctness code	`eai_taxonomy.technical_correctness.secondary.code`
Secondary Label	Alternative technical correctness label	`eai_taxonomy.technical_correctness.secondary.label`

Possible Values:

Code	Label	Description
`-1`	Abstain	Unable to determine
`1`	Technically Flawed	Significant errors undermining content validity
`2`	Partially Correct	Some correctness but contains flaws, omissions, or errors
`3`	Mostly Correct	Technical correctness with minor flaws or incomplete explanations
`4`	Highly Correct	High technical correctness with precise definitions and clear explanations
`5`	Exceptionally Correct	Exceptional technical correctness with formal proofs and flawless content
`6`	Not Applicable/Indeterminate	No technical content or insufficient context

Education Level

Assesses the appropriate educational background required to comprehend the content:

Component	Description	Path
Primary Code	Main education level code	`eai_taxonomy.education_level.primary.code`
Primary Label	Main education level label	`eai_taxonomy.education_level.primary.label`
Secondary Code	Alternative education level code	`eai_taxonomy.education_level.secondary.code`
Secondary Label	Alternative education level label	`eai_taxonomy.education_level.secondary.label`

Possible Values:

Code	Label	Description
`-1`	Abstain	Unable to determine
`1`	General Audience	Accessible to anyone with basic literacy; simple terms
`2`	High School Level	Requires high school education; specialized terminology explained for non-experts
`3`	Undergraduate Level	Requires college education; uses specialized terminology and assumes background knowledge
`4`	Graduate/Expert Level	Requires graduate education or domain expertise; assumes deep background knowledge
`5`	Indeterminate	Insufficient content to judge educational level

Metadata

Metadata Structure

The metadata field contains a nested structure with web archive information:

Field	Type	Description	Path
URL Information
URL	`String`	Original URL of the document	`metadata.url`
Source Domain	`String`	Domain name of the source	`metadata.source_domain`
Snapshot ID	`String`	Identifier for the web archive snapshot	`metadata.snapshot_id`
WARC Metadata		WARC (Web ARChive) format metadata
Content Length	`String`	Size of the content	`metadata.warc_metadata.Content-Length`
Content Type	`String`	MIME type of the content	`metadata.warc_metadata.Content-Type`
Block Digest	`String`	Checksum of the WARC block	`metadata.warc_metadata.WARC-Block-Digest`
Concurrent To	`String`	Related WARC records	`metadata.warc_metadata.WARC-Concurrent-To`
Date	`String`	Timestamp of the crawl	`metadata.warc_metadata.WARC-Date`
IP Address	`String`	Source server IP address	`metadata.warc_metadata.WARC-IP-Address`
Payload Type	`String`	Identified content type	`metadata.warc_metadata.WARC-Identified-Payload-Type`
Payload Digest	`String`	Checksum of the payload	`metadata.warc_metadata.WARC-Payload-Digest`
Record ID	`String`	Unique WARC record identifier	`metadata.warc_metadata.WARC-Record-ID`
Target URI	`String`	Original target URL	`metadata.warc_metadata.WARC-Target-URI`
Truncated	`String`	Truncation status	`metadata.warc_metadata.WARC-Truncated`
Type	`String`	WARC record type	`metadata.warc_metadata.WARC-Type`
Warcinfo ID	`String`	Associated warcinfo record	`metadata.warc_metadata.WARC-Warcinfo-ID`
Additional Info
WARC Info	`String`	Additional WARC information	`metadata.warc_info`

Quality Signals

The dataset includes two comprehensive quality assessment frameworks:

Red Pajama v2 Quality Metrics

Text quality indicators derived from the Red Pajama v2 filtering pipeline:

Content Structure Metrics

Metric	Description	Path
Original Length	Original document length	`quality_signals.red_pajama_v2.ccnet_original_length`
Original Lines	Number of lines in original document	`quality_signals.red_pajama_v2.ccnet_original_nlines`
Sentence Count	Total sentence count	`quality_signals.red_pajama_v2.rps_doc_num_sentences`
Word Count	Total word count	`quality_signals.red_pajama_v2.rps_doc_word_count`
Mean Word Length	Average word length	`quality_signals.red_pajama_v2.rps_doc_mean_word_length`

Language Quality Metrics

Metric	Description	Path
Stop Word Fraction	Proportion of stop words	`quality_signals.red_pajama_v2.rps_doc_stop_word_fraction`
Unique Words Fraction	Fraction of unique words	`quality_signals.red_pajama_v2.rps_doc_frac_unique_words`
All Caps Words	Fraction of words in all capitals	`quality_signals.red_pajama_v2.rps_doc_frac_all_caps_words`
Non-Alphabetic Words	Fraction of non-alphabetic words	`quality_signals.red_pajama_v2.rps_doc_frac_no_alph_words`
Unigram Entropy	Entropy measure of word distribution	`quality_signals.red_pajama_v2.rps_doc_unigram_entropy`

Content Pattern Analysis

Metric	Description	Path
Curly Bracket Density	Curly bracket density (code indicator)	`quality_signals.red_pajama_v2.rps_doc_curly_bracket`
Symbol-to-Word Ratio	Symbol-to-word ratio	`quality_signals.red_pajama_v2.rps_doc_symbol_to_word_ratio`
Ellipsis Line Endings	Lines ending with ellipsis	`quality_signals.red_pajama_v2.rps_doc_frac_lines_end_with_ellipsis`
Lorem Ipsum Detection	Lorem ipsum text detection	`quality_signals.red_pajama_v2.rps_doc_lorem_ipsum`
Offensive Content	Potentially offensive content detection	`quality_signals.red_pajama_v2.rps_doc_ldnoobw_words`
UT1 Blacklist	UT1 blacklist filtering score	`quality_signals.red_pajama_v2.rps_doc_ut1_blacklist`

Duplication Detection

Metric	Description	Path
5-gram Duplication	Character-level duplication for 5-grams	`quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_5grams`
6-gram Duplication	Character-level duplication for 6-grams	`quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_6grams`
7-gram Duplication	Character-level duplication for 7-grams	`quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_7grams`
8-gram Duplication	Character-level duplication for 8-grams	`quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_8grams`
9-gram Duplication	Character-level duplication for 9-grams	`quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_9grams`
10-gram Duplication	Character-level duplication for 10-grams	`quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_10grams`
Top 2-gram Coverage	Most frequent 2-gram coverage	`quality_signals.red_pajama_v2.rps_doc_frac_chars_top_2gram`
Top 3-gram Coverage	Most frequent 3-gram coverage	`quality_signals.red_pajama_v2.rps_doc_frac_chars_top_3gram`
Top 4-gram Coverage	Most frequent 4-gram coverage	`quality_signals.red_pajama_v2.rps_doc_frac_chars_top_4gram`

Domain Importance Scores

Metric	Description	Path
Books Importance	Similarity to book content	`quality_signals.red_pajama_v2.rps_doc_books_importance`
Books Importance (Length Corrected)	Length-corrected books similarity	`quality_signals.red_pajama_v2.rps_doc_books_importance_length_correction`
OpenWebText Importance	Similarity to OpenWebText	`quality_signals.red_pajama_v2.rps_doc_openwebtext_importance`
OpenWebText Importance (Length Corrected)	Length-corrected OpenWebText similarity	`quality_signals.red_pajama_v2.rps_doc_openwebtext_importance_length_correction`
Wikipedia Importance	Similarity to Wikipedia	`quality_signals.red_pajama_v2.rps_doc_wikipedia_importance`
Wikipedia Importance (Length Corrected)	Length-corrected Wikipedia similarity	`quality_signals.red_pajama_v2.rps_doc_wikipedia_importance_length_correction`

FastText Classification Scores

Domain and content type classification probabilities:

Metric	Description	Path
DCLM Score	DataComp-LM classifier score	`quality_signals.fasttext.dclm`
English Confidence	English language confidence	`quality_signals.fasttext.english`
Educational Content	Educational content approximation	`quality_signals.fasttext.fineweb_edu_approx`
General Math	General mathematics content	`quality_signals.fasttext.eai_general_math`
Web Math	OWM Web-based mathematics content	`quality_signals.fasttext.eai_open_web_math`
Code Content	Code content detection	`quality_signals.fasttext.eai_web_code`

How to Load the Dataset

This section provides examples of how to load the EssentialAI/essential-web-v1.0 dataset using different Python libraries and frameworks.

Using Hugging Face Datasets (Standard Method)

The simplest way to load the dataset is using the Hugging Face datasets library:

from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("EssentialAI/essential-web-v1.0")

# View dataset structure
print(dataset)
print(f"Number of examples: {len(dataset['train'])}")

You can also load the dataset in streaming mode to avoid downloading the entire dataset at once:

from datasets import load_dataset

# Load in streaming mode
dataset = load_dataset("EssentialAI/essential-web-v1.0", streaming=True)
data_stream = dataset["train"]

# Iterate through examples
for example in data_stream.take(5):
    print(example)

Using PySpark

For large-scale distributed processing, you can load the dataset using PySpark with the pyspark_huggingface library:

# First install the required library:
# pip install pyspark_huggingface

import pyspark_huggingface
from pyspark.sql import SparkSession

# Initialize Spark session
spark = SparkSession.builder.appName("EAI-Taxonomy-Web").getOrCreate()

# Load the dataset using the "huggingface" data source
df = spark.read.format("huggingface").load("EssentialAI/essential-web-v1.0")

# Basic dataset exploration
print(f"Dataset shape: {df.count()} rows, {len(df.columns)} columns")
df.show(10)
df.printSchema()

# Load only specific columns for efficiency
df_subset = (
    spark.read.format("huggingface")
    .option("columns", '["column1", "column2"]')  # Replace with actual column names
    .load("EssentialAI/essential-web-v1.0")
)

# Run SQL queries on the dataset
df.createOrReplaceTempView("eai_web_dataset")
result = spark.sql("""
    SELECT COUNT(*) as total_examples
    FROM eai_web_dataset
""")
result.show()

Using Daft

Daft provides a modern DataFrame library optimized for machine learning workloads. You can load the dataset directly from Hugging Face:

import daft

# Load the entire dataset
df = daft.read_parquet("hf://datasets/EssentialAI/essential-web-v1.0")

# Basic exploration
print("Dataset schema:")
df.schema()

print("First 5 rows:")
df.show(5)

If you need to access private datasets or use authentication:

import daft
from daft.io import IOConfig, HTTPConfig

io_config = IOConfig(http=HTTPConfig(bearer_token="your_token"))
df = daft.read_parquet("hf://datasets/EssentialAI/essential-web-v1.0", io_config=io_config)

Installation Requirements

Make sure you have the required libraries installed:

# For Hugging Face datasets
pip install datasets

# For PySpark with Hugging Face integration
pip install pyspark_huggingface

# For Daft
pip install daft

📝 Citation

@misc{ai2025essentialwebv1024ttokens,
      title={Essential-Web v1.0: 24T tokens of organized web data}, 
      author={Essential AI and : and Andrew Hojel and Michael Pust and Tim Romanski and Yash Vanjani and Ritvik Kapila and Mohit Parmar and Adarsh Chaluvaraju and Alok Tripathy and Anil Thomas and Ashish Tanwer and Darsh J Shah and Ishaan Shah and Karl Stratos and Khoi Nguyen and Kurt Smith and Michael Callahan and Peter Rushton and Philip Monk and Platon Mazarakis and Saad Jamal and Saurabh Srivastava and Somanshu Singla and Ashish Vaswani},
      year={2025},
      eprint={2506.14111},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.14111}, 
}

🌐 Essential-Web: Complete 24-Trillion Token Dataset (Upload in-progress...)

📋 Dataset Description

🔍 Free Decimal Correspondence (FDC) Taxonomy

⚙️ Dataset Creation

📥 Source Data

🔧 Processing Pipeline

🎯 Performance & Validation

🚀 Related Datasets & Models

Domain-Specific Datasets

Classification Model

🎯 Intended Use

Dataset Schema Documentation

Overview

Core Fields

EAI Taxonomy Classification

Knowledge Domain

Cognitive Processing Level

Document Type v1

Document Type v2

Extraction Artifacts

Missing Content

Text Structure Information

Reasoning Depth

Technical Correctness

Education Level

Metadata Structure

Red Pajama v2 Quality Metrics

Content Structure Metrics

Language Quality Metrics

Content Pattern Analysis

Duplication Detection

Domain Importance Scores

FastText Classification Scores

How to Load the Dataset

Using Hugging Face Datasets (Standard Method)

Using PySpark

Using Daft

Installation Requirements

📝 Citation

Collection including EssentialAI/essential-web-v1.0