Update README.md

README.md

@@ -41,6 +41,426 @@ Our approach simplifies math dataset creation:
 1. **Traditional Method**: Train high-recall classifiers → Run on billions of documents
 2. **Our Method**: Query taxonomy metadata for `51 - Mathematics` → Apply FineMath classifier to all recalled documents → Select top-scoring content
 
+# 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.
+
+## EAI Taxonomy Classification
+
+Comprehensive hierarchical classification system with primary and secondary labels - the most important feature of this dataset:
+
+### Free Decimal Correspondence
+Dewey Decimal-inspired classification with 3-level hierarchical labels:
+
+| Component | Description | Path |
+|-----------|-------------|------|
+| Primary Code | Main classification code | `eai_taxonomy.free_decimal_correspondence.primary.code` |
+| Primary Level 1 | Top-level category | `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` |
+
+### Bloom's Taxonomy Integration
+
+#### Cognitive Process
+Learning and thinking skill levels:
+
+| 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 |
+|------|-------|
+| `-1` | Abstain |
+| `1` | Remember |
+| `2` | Understand |
+| `3` | Apply |
+| `4` | Analyze |
+| `5` | Evaluate |
+| `6` | Create |
+
+#### Knowledge Domain
+Subject matter categorization:
+
+| 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 |
+|------|-------|
+| `-1` | Abstain |
+| `1` | Factual |
+| `2` | Conceptual |
+| `3` | Procedural |
+| `4` | Metacognitive |
+
+### Document Characteristics
+
+#### Document Type v1
+Format and structure classification:
+
+| 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 |
+|------|-------|
+| `-1` | Abstain |
+| `1` | News/Editorial |
+| `2` | Academic/Research |
+| `3` | Reference/Encyclopedic/Educational |
+| `4` | Code/Software |
+| `5` | Social/Forum |
+| `6` | Promotional/Advertisement |
+| `7` | Search/Directory/Bibliography |
+| `8` | Adult/Pornographic |
+| `9` | Personal/Misc |
+| `10` | Machine-Generated |
+| `11` | Legal/Regulatory |
+| `12` | Government/Political |
+| `13` | Literary/Creative |
+| `14` | Reviews/Critiques |
+| `15` | E-Commerce/Marketplace |
+| `16` | Images/Videos/Audio |
+| `17` | Other/Unclassified |
+
+#### Document Type v2
+Updated format and structure classification:
+
+| 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` |
+
+**Possible Values:**
+| Code | Label |
+|------|-------|
+| `-1` | Abstain |
+| `1` | About (Org.) |
+| `2` | About (Personal) |
+| `3` | Academic Writing |
+| `4` | Audio Transcript |
+| `5` | Comment Section |
+| `6` | Content Listing |
+| `7` | Creative Writing |
+| `8` | Documentation |
+| `9` | FAQ |
+| `10` | Knowledge Article |
+| `11` | Legal Notices |
+| `12` | Listicle |
+| `13` | News (Org.) |
+| `14` | News Article |
+| `15` | Nonfiction Writing |
+| `16` | Personal Blog |
+| `17` | Product Page |
+| `18` | Q&A Forum |
+| `19` | Spam / Ads |
+| `20` | Structured Data |
+| `21` | Customer Support |
+| `22` | Truncated |
+| `23` | Tutorial |
+| `24` | User Review |
+| `25` | Other/Unclassified |
+
+#### Extraction Artifacts
+Technical extraction quality indicators:
+
+| 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 |
+|------|-------|
+| `-1` | Abstain |
+| `0` | No Artifacts |
+| `1` | Leftover HTML |
+| `2` | Text Extraction Errors |
+| `3` | Irrelevant Content |
+| `4` | Indeterminate |
+
+#### Missing Content
+Content completeness assessment:
+
+| 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 |
+|------|-------|
+| `-1` | Abstain |
+| `0` | No missing content |
+| `1` | Truncated Snippets |
+| `2` | Click Here References |
+| `3` | Incoherent Flow |
+| `4` | Missing Images or Figures |
+| `5` | Missing Referenced Data |
+| `6` | Indeterminate |
+
+### Content Quality Dimensions
+
+#### Reasoning Depth
+Complexity of logical reasoning:
+
+| 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 |
+|------|-------|
+| `-1` | Abstain |
+| `1` | No Reasoning |
+| `2` | Basic Reasoning |
+| `3` | Intermediate Reasoning |
+| `4` | Advanced Reasoning |
+| `5` | Exceptional Reasoning |
+| `6` | Indeterminate |
+
+#### Technical Correctness
+Accuracy 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 |
+|------|-------|
+| `-1` | Abstain |
+| `1` | Technically Flawed |
+| `2` | Partially Correct |
+| `3` | Mostly Correct |
+| `4` | Highly Correct |
+| `5` | Exceptionally Correct |
+| `6` | Not Applicable/Indeterminate |
+
+#### Education Level
+Appropriate educational grade level:
+
+| 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 |
+|------|-------|
+| `-1` | Abstain |
+| `1` | General Audience |
+| `2` | High School Level |
+| `3` | Undergraduate Level |
+| `4` | Graduate/Expert Level |
+| `5` | Indeterminate |
+
+## Schema Structure
+
+### Core Fields
+
+| Field | Type | Description | Path |
+|-------|------|-------------|------|
+| `id` | `Int64` | Unique identifier for each document | `id` |
+| `text` | `String` | The main textual content of the document | `text` |
+
+### 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` |
+
+## 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 | Web-based mathematics content | `quality_signals.fasttext.eai_open_web_math` |
+| Code Content | Code content detection | `quality_signals.fasttext.eai_web_code` |
+
+## Data Provenance
+
+All documents originate from web crawls with full WARC metadata preservation, enabling:
+- Source verification and attribution
+- Temporal analysis of web content
+- Content deduplication across crawls
+- Quality assessment pipeline reconstruction
+
+## Usage Examples
+
+**Filter by quality score:**
+```python
+df.filter(df["quality_signals.red_pajama_v2.rps_doc_stop_word_fraction"] > 0.3)
+```
+
+**Filter by domain:**
+```python
+df.filter(df["metadata.source_domain"].contains("wikipedia"))
+```
+
+**Filter by education level:**
+```python
+df.filter(df["eai_taxonomy.education_level.primary.code"] == "2")  # High School Level
+```
+
+**Filter by content type:**
+```python
+df.filter(df["quality_signals.fasttext.eai_web_code"] > 0.8)
+```
+
+**Filter by document quality:**
+```python
+df.filter(
+    (df["quality_signals.red_pajama_v2.rps_doc_word_count"] > 100) &
+    (df["quality_signals.red_pajama_v2.rps_doc_stop_word_fraction"] > 0.2) &
+    (df["quality_signals.red_pajama_v2.rps_doc_frac_unique_words"] > 0.3)
+)
+```
+
+**Filter by reasoning depth:**
+```python
+df.filter(df["eai_taxonomy.reasoning_depth.primary.code"].isin(["4", "5"]))  # Advanced or Exceptional
+```
+
+**Filter by document type:**
+```python
+df.filter(df["eai_taxonomy.document_type_v2.primary.code"] == "3")  # Academic Writing
+```
+
+**Filter high-quality educational content:**
+```python
+df.filter(
+    (df["eai_taxonomy.education_level.primary.code"].isin(["2", "3"])) &  # High School or Undergraduate
+    (df["eai_taxonomy.technical_correctness.primary.code"].isin(["4", "5"])) &  # Highly or Exceptionally Correct
+    (df["eai_taxonomy.extraction_artifacts.primary.code"] == "0") &  # No Artifacts
+    (df["quality_signals.fasttext.fineweb_edu_approx"] > 0.7)
+)
+```
+
 ## 🎓 Citation
 
 If you use this dataset, please cite our EssentialWeb paper: