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README.md

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+---
+language:
+- en
+license: apache-2.0
+size_categories:
+- 10M<n<100M
+task_categories:
+- text-generation
+tags:
+- pretraining
+- educational
+- pedagogical
+- synthetic
+- sutra
+- multi-domain
+- 10B
+pretty_name: Sutra 10B Pretraining Dataset
+---
+
+# Sutra 10B Pretraining Dataset
+
+A high-quality pedagogical dataset designed for LLM pretraining, containing 10,193,029 educational entries totaling over 10 billion tokens. This is the largest dataset in the Sutra series, designed to demonstrate that dense, curated datasets can provide best-in-class pretraining performance for small language models.
+
+## Dataset Description
+
+This dataset was generated using the Sutra framework, which creates structured educational content optimized for language model pretraining. Each entry is designed to maximize learning efficiency through:
+
+- **Clear pedagogical structure**: Content follows proven educational patterns
+- **Cross-domain connections**: Concepts are linked across disciplines
+- **Varied complexity levels**: From foundational (level 1) to advanced (level 10)
+- **Quality-controlled generation**: All entries meet minimum quality thresholds
+- **Diverse content types**: 33 different pedagogical formats
+- **Rich metadata**: Every entry annotated with 13 structured fields
+
+## Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total Entries | 10,193,029 |
+| Total Tokens | 10,218,677,925 |
+| Avg Tokens/Entry | 1002 |
+| Avg Quality Score | 0.701 |
+| Tokenizer | SmolLM2 (HuggingFaceTB/SmolLM2-135M) |
+
+### Domain Distribution
+
+| Domain | Entries | Tokens | Percentage |
+|--------|---------|--------|------------|
+| interdisciplinary | 3,561,052 | 3570.0M | 34.9% |
+| technology | 2,154,481 | 2159.9M | 21.1% |
+| science | 1,456,708 | 1460.3M | 14.3% |
+| social_studies | 862,288 | 864.4M | 8.5% |
+| mathematics | 830,414 | 832.5M | 8.1% |
+| life_skills | 559,667 | 561.1M | 5.5% |
+| arts_and_creativity | 455,738 | 456.9M | 4.5% |
+| language_arts | 235,957 | 236.5M | 2.3% |
+| philosophy_and_ethics | 76,724 | 76.9M | 0.8% |
+
+
+### Content Type Distribution (Top 15)
+
+| Content Type | Count | Percentage |
+|--------------|-------|------------|
+| historical_context | 3,082,957 | 30.2% |
+| concept_introduction | 928,244 | 9.1% |
+| data_analysis | 776,495 | 7.6% |
+| worked_examples | 697,861 | 6.8% |
+| problem_set | 676,977 | 6.6% |
+| tutorial | 620,163 | 6.1% |
+| technical_documentation | 520,246 | 5.1% |
+| research_summary | 494,023 | 4.8% |
+| code_implementation | 473,056 | 4.6% |
+| practical_application | 438,157 | 4.3% |
+| creative_writing | 337,065 | 3.3% |
+| reasoning_demonstration | 227,343 | 2.2% |
+| qa_pairs | 200,076 | 2.0% |
+| ethical_analysis | 157,882 | 1.5% |
+| experiment_design | 141,859 | 1.4% |
+
+
+## Data Sources
+
+This dataset combines multiple high-quality sources:
+
+| Source | Description | Approximate Tokens |
+|--------|-------------|--------------------|
+| Sutra 7.5B | Core pedagogical dataset generated with Sutra framework | ~7.5B |
+| Sutra 3B (unique) | Additional skill-tagged educational content | ~0.5B |
+| Sutra 1B (unique) | Metadata-rich pedagogical entries | ~0.3B |
+| Nemotron-CC-Math v1 | High-quality mathematical content (NVIDIA) | ~1.0B |
+| OpenWebMath | Mathematical web content | ~0.5B |
+| Wikipedia (English) | Encyclopedic knowledge | ~0.9B |
+| Cosmopedia | Synthetic educational content (multiple subsets) | ~1.0B |
+| FineWeb-Edu | High-quality educational web content | ~0.5B |
+
+## Data Fields
+
+Each entry contains 13 structured fields:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string | Unique identifier (UUID) |
+| `concept_name` | string | The concept being taught (2-5 words) |
+| `domain` | string | Primary knowledge domain (9 domains) |
+| `content_type` | string | Type of pedagogical content (33 types) |
+| `text` | string | The main educational content |
+| `quality_score` | float | Quality assessment score (0.0-1.0) |
+| `information_density` | string | Measure of information per token (low/medium/high) |
+| `complexity_level` | integer | Difficulty level (1-10) |
+| `token_count` | integer | Number of tokens (SmolLM2 tokenizer) |
+| `prerequisites` | list[string] | Required prior knowledge concepts |
+| `builds_to` | list[string] | Advanced concepts this enables |
+| `cross_domain_connections` | list[string] | Related knowledge domains |
+| `quality_assessment` | object | Multi-dimensional quality scores |
+
+### Quality Assessment Sub-fields
+
+| Sub-field | Type | Description |
+|-----------|------|-------------|
+| `clarity` | float | How clear and readable (0.0-1.0) |
+| `accuracy` | float | Factual correctness (0.0-1.0) |
+| `pedagogy` | float | Educational structure quality (0.0-1.0) |
+| `engagement` | float | How engaging the content is (0.0-1.0) |
+| `depth` | float | Depth of coverage (0.0-1.0) |
+| `creativity` | float | Creative presentation (0.0-1.0) |
+
+### Valid Domains (9)
+
+`mathematics`, `science`, `technology`, `language_arts`, `social_studies`, `arts_and_creativity`, `life_skills`, `philosophy_and_ethics`, `interdisciplinary`
+
+### Valid Content Types (33)
+
+`concept_introduction`, `reasoning_demonstration`, `code_implementation`, `technical_documentation`, `tutorial`, `cross_domain_bridge`, `worked_examples`, `qa_pairs`, `common_misconceptions`, `meta_learning`, `synthesis`, `prerequisite_scaffolding`, `code_explanation`, `diagnostic_assessment`, `code_debugging`, `historical_context`, `research_summary`, `problem_set`, `case_study`, `analogy`, `experiment_design`, `proof`, `algorithm_analysis`, `data_analysis`, `ethical_analysis`, `comparative_analysis`, `creative_writing`, `debate_argument`, `practical_application`, `thought_experiment`, `visualization`, `system_design`, `review_summary`
+
+## Data Cleaning
+
+The dataset underwent comprehensive cleaning:
+
+- **Deduplication**: SHA-256 hash-based exact duplicate removal across all sources
+- **Quality Filtering**: Entries below quality_score 0.3 removed
+- **Length Filtering**: Entries shorter than 50 tokens or longer than 65,536 tokens removed
+- **Garbage Detection**: Repetitive content, control characters, non-English content filtered
+- **Field Validation**: All 13 fields validated and normalized
+
+## Metadata Generation
+
+Metadata was generated using a combination of approaches:
+- Entries from Sutra-1B retained their existing rich metadata
+- All other entries (~95%) were classified using heuristic keyword-based classification
+- Domain and content type classification via pattern matching and text analysis
+- Quality scores computed from text statistics (vocabulary diversity, structure, length)
+- Token counts computed using SmolLM2 tokenizer for accuracy
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+ds = load_dataset("codelion/sutra-10B", split="train")
+
+# Stream for large-scale training
+ds = load_dataset("codelion/sutra-10B", split="train", streaming=True)
+
+# Filter by domain
+math_ds = ds.filter(lambda x: x["domain"] == "mathematics")
+
+# Filter by quality
+high_quality = ds.filter(lambda x: x["quality_score"] > 0.7)
+
+# Filter by complexity
+beginner = ds.filter(lambda x: x["complexity_level"] <= 3)
+```
+
+## Scaling Trajectory
+
+The Sutra dataset series demonstrates the impact of dataset scaling:
+
+| Dataset | Tokens | Best Benchmark |
+|---------|--------|----------------|
+| Sutra-1B | 1B | Baseline |
+| Sutra-3B | 3B | +2.1% avg |
+| Sutra-5B | 5B | +3.5% avg |
+| Sutra-7.5B | 7.5B | +4.8% avg |
+| **Sutra-10B** | **10B** | **TBD** |
+
+## Intended Use
+
+This dataset is designed for:
+
+- **LLM Pretraining**: High-quality educational content for foundational model training
+- **Domain-specific fine-tuning**: Subset by domain for specialized training
+- **Educational AI research**: Studying pedagogical content generation
+- **Curriculum learning**: Progressive complexity for staged training
+- **Small model optimization**: Demonstrating data quality > quantity for small LMs
+
+## Related Datasets
+
+- [sutra-1B](https://huggingface.co/datasets/codelion/sutra-1B): 1B token pretraining dataset
+- [sutra-100M](https://huggingface.co/datasets/codelion/sutra-100M): 100M token subset
+- [sutra-30k-seeds](https://huggingface.co/datasets/codelion/sutra-30k-seeds): Instruction prompts for post-training
+- [sutra-magpie-sft](https://huggingface.co/datasets/codelion/sutra-magpie-sft): SFT dataset
+
+## License
+
+Apache 2.0
+
+## Citation
+
+```bibtex
+@dataset{sutra-10b,
+  title={Sutra 10B: A Pedagogical Pretraining Dataset},
+  author={Sutra Team},
+  year={2026},
+  url={https://huggingface.co/datasets/codelion/sutra-10B}
+}
+```