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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+# 🌐 Essential-Web: FDC Level-2 Partitioned Dataset
+
+## 📋 Dataset Description
+
+This dataset contains a 1 trillion token sample from Essential-Web, partitioned by Free Decimal Correspondence (FDC) level-2 categories. Essential-Web is a 24-trillion-token web dataset with extensive document-level metadata designed to enable rapid dataset curation through SQL-like filtering.
+
+## 🔍 Free Decimal Correspondence (FDC)
+
+The FDC taxonomy is an open classification system inspired by the Dewey Decimal System. Level-2 categories provide broad subject matter classifications that enable researchers to quickly identify and filter relevant content domains.
+
+For help navigating FDC codes, see: https://www.librarything.com/mds
+
+## ⚙️ Dataset Creation
+
+The source documents were classified using EAI-Taxonomy-0.5b, a classifier trained on synthetic labels generated by open-weight LLMs. The classification process involved inference across 23.6 billion web documents, requiring approximately 90,000 AMD MI300x GPU-hours.
+
+## 🎯 Performance
+
+Datasets curated from Essential-Web using simple metadata filters have demonstrated competitive performance:
+- 🧮 Math domain: within 15.3% of state-of-the-art
+- 💻 Web code: 14.3% above state-of-the-art  
+- 🔬 STEM: 24.5% above state-of-the-art
+- 🩺 Medical: 8.6% above state-of-the-art
+
+## 🏗️ Dataset Structure
+
+The dataset is organized by FDC level-2 categories, which provide a Dewey Decimal-inspired taxonomy for classifying web content by subject matter. Files are organized in the `data/` directory with partitions like:
+
+```
+data/fdc_level=02/
+data/fdc_level=05/
+data/fdc_level=10/
+...
+```
+
+Each partition contains documents labeled with their corresponding FDC classification along with associated metadata.
+
+
+# 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 for each document | `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.
+
+<details>
+<summary><strong>Free Decimal Correspondence (FDC)</strong></summary>
+
+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
+
+</details>
+
+<details>
+<summary><strong>Bloom's Taxonomy Integration</strong></summary>
+
+Based on Anderson and Krathwohl's 2001 revision of Bloom's Taxonomy of Educational Objectives, providing two complementary categorization dimensions for educational content analysis.
+
+### Cognitive Process
+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 |
+
+### 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 |
+
+</details>
+
+<details>
+<summary><strong>Document Characteristics</strong></summary>
+
+### 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:
+
+| 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` |
+
+**Selected Values:**
+| Code | Label | Examples |
+|------|-------|----------|
+| `3` | Academic Writing | Research papers, abstracts |
+| `7` | Creative Writing | Song lyrics, novel excerpts |
+| `8` | Documentation | API docs, README files |
+| `10` | Knowledge Article | Wikipedia, Britannica |
+| `14` | News Article | Newspaper articles, CNN content |
+| `18` | Q&A Forum | Quora, Stack Exchange |
+| `23` | Tutorial | Cooking recipes, WikiHow pages |
+| `25` | Other/Unclassified | Documents that resist classification |
+
+### 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 |
+
+</details>
+
+<details>
+<summary><strong>Content Quality Dimensions</strong></summary>
+
+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 |
+
+</details>
+
+<details>
+<summary><strong>Schema Structure</strong></summary>
+
+## 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` |
+
+## 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` |
+
+</details>
+
+<details>
+<summary><strong>Quality Signals</strong></summary>
+
+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 | Web-based mathematics content | `quality_signals.fasttext.eai_open_web_math` |
+| Code Content | Code content detection | `quality_signals.fasttext.eai_web_code` |
+
+</details>
+
+## How to Load the Dataset
+
+This section provides examples of how to load the `Research-EAI/eai-taxonomy-math-w-fm` 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:
+
+```python
+from datasets import load_dataset
+
+# Load the entire dataset
+dataset = load_dataset("Research-EAI/eai-taxonomy-math-w-fm")
+
+# 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:
+
+```python
+from datasets import load_dataset
+
+# Load in streaming mode
+dataset = load_dataset("Research-EAI/eai-taxonomy-math-w-fm", 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:
+
+```python
+# 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-Math").getOrCreate()
+
+# Load the dataset using the "huggingface" data source
+df = spark.read.format("huggingface").load("Research-EAI/eai-taxonomy-math-w-fm")
+
+# 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("Research-EAI/eai-taxonomy-math-w-fm")
+)
+
+# Run SQL queries on the dataset
+df.createOrReplaceTempView("eai_math_dataset")
+result = spark.sql("""
+    SELECT COUNT(*) as total_examples
+    FROM eai_math_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:
+
+```python
+import daft
+
+# Load the entire dataset
+df = daft.read_parquet("hf://datasets/Research-EAI/eai-taxonomy-math-w-fm")
+
+# Basic exploration
+print("Dataset schema:")
+df.schema()
+
+print("First 5 rows:")
+df.show(5)
+```
+
+If you need to access private datasets or use authentication:
+
+```python
+import daft
+import os
+
+# Set your Hugging Face token
+os.environ["HF_TOKEN"] = "your_huggingface_token_here"
+
+# Load with authentication
+df = daft.read_parquet(
+    "hf://datasets/Research-EAI/eai-taxonomy-math-w-fm",
+    hf_token=os.environ["HF_TOKEN"]
+)
+```
+
+### Installation Requirements
+
+Make sure you have the required libraries installed:
+
+```bash
+# For Hugging Face datasets
+pip install datasets
+
+# For PySpark with Hugging Face integration
+pip install pyspark_huggingface
+
+# For Daft
+pip install daft
+```
+
+## 🎓 Citation
+
+If you use this dataset, please cite our EssentialWeb paper:
+
+```bibtex
+@article{essentialweb2025,
+  title={Essential-Web: 24T tokens of organized web data},
+  author={[Authors]},
+  year={2025}
+}
+```