---
license: odc-by
task_categories:
  - text-generation
language:
  - en
size_categories:
  - 1M<n<10M
tags:
  - diverse
  - curated
  - deduplication
  - multi-domain
  - stem
  - legal
  - scientific
  - encyclopedic
  - source-text
configs:
  - config_name: default
    data_files:
      - split: train
        path: cot_diverse_2.5m.parquet
pretty_name: Diverse Source Text Dataset (2.5M)
dataset_info:
  features:
    - name: text
      dtype: string
    - name: id
      dtype: string
    - name: url
      dtype: string
    - name: source
      dtype: string
    - name: quality_score
      dtype: float64
  splits:
    - name: train
      num_examples: 2500000
---

# Diverse Source Text Dataset (2.5M)

A curated, deduplicated, multi-domain English text dataset blending 7 sources across STEM, legal, scientific, encyclopedic, Q&A, and general knowledge domains. Designed as high-quality, diverse source material for downstream NLP tasks such as synthetic data generation, fine-tuning, and text analysis.

## Dataset Summary

| | |
|---|---|
| **Total samples** | 2,500,000 |
| **Estimated tokens** | ~2.8B (GPT-2) / ~2.4B (modern tokenizers) |
| **Language** | English |
| **Format** | Parquet (ZSTD compressed) |
| **File size** | 4.28 GB |
| **Text length** | 200 - 50,000 characters |
| **Mean length** | 4,656 characters (~1,107 tokens) |
| **Median length** | 2,439 characters |

## Source Breakdown

| Source | Samples | Share | Avg Chars | Avg Tok/Doc | Quality Score | Domain |
|--------|--------:|------:|----------:|------------:|--------------:|--------|
| FineWeb EDU (broad, 3.0-4.0) | 750,000 | 30% | 4,997 | 1,063 | 3.39 | General educational |
| DCLM-baseline | 500,000 | 20% | 2,295 | 572 | 0.89 | Commonsense / explanatory |
| FineWeb EDU (high, >= 4.0) | 375,000 | 15% | 4,923 | 1,023 | 4.18 | STEM / high-quality educational |
| Pile - FreeLaw | 250,000 | 10% | 14,458 | 3,781 | N/A | Legal (court opinions, filings) |
| Pile - PubMed Abstracts | 250,000 | 10% | 1,335 | 292 | N/A | Biomedical / scientific |
| Pile - StackExchange | 200,000 | 8% | 2,190 | 761 | N/A | Technical Q&A |
| Pile - Wikipedia (en) | 175,000 | 7% | 2,923 | 685 | N/A | Encyclopedic |

## Schema

```
text:           string   # The document text (200-50,000 chars)
id:             string   # Unique document identifier from source
url:            string   # Source URL (null for Pile sources)
source:         string   # One of 7 source labels
quality_score:  float64  # Source-specific quality score (null for Pile sources)
```

## Methodology

### Collection
- **FineWeb EDU**: Streamed from [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) across 12 Common Crawl dumps, filtered by educational quality score
- **DCLM-baseline**: Streamed from [mlfoundations/dclm-baseline-1.0](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0) with fasttext quality score >= 0.65
- **Pile subsets**: Streamed from [monology/pile-uncopyrighted](https://huggingface.co/datasets/monology/pile-uncopyrighted), filtered by subset name

### Filtering
- Minimum 200 characters, maximum 50,000 characters
- 20% over-fetch to absorb deduplication losses

### Deduplication (3-stage)
1. **Exact text dedup**: MD5 hash of normalized text (lowercased, whitespace-collapsed) - removed 70,433 (2.3%)
2. **URL dedup**: Normalized URL matching - removed 19,283
3. **Near-dedup (anchor pairs)**: Three passes using MD5 hashes of text start/mid/end 500-char anchors - removed 3,353

Total removed: 93,069 / 3,000,000 (3.1%)

### Final Assembly
- Each source trimmed to exact target count, prioritizing highest quality scores
- Globally shuffled via deterministic hash (seed=42)
- Written as single Parquet file with ZSTD compression

## Usage

```python
from datasets import load_dataset

ds = load_dataset("blythet/diverse-2.5m", split="train")
print(ds)
# Dataset({
#     features: ['text', 'id', 'url', 'source', 'quality_score'],
#     num_rows: 2500000
# })

# Filter by source
stem = ds.filter(lambda x: x["source"] == "fineweb_edu_high")

# Filter by quality
high_quality = ds.filter(lambda x: x["quality_score"] is not None and x["quality_score"] >= 4.0)
```

## Intended Use

This dataset provides high-quality, diverse English text suitable for:

- Synthetic data generation (e.g., chain-of-thought, instruction tuning)
- Fine-tuning language models across multiple domains
- Text analysis and NLP research
- Domain-specific data extraction (legal, scientific, educational, technical)

The domain diversity covers STEM, legal reasoning, scientific literature, technical Q&A, encyclopedic knowledge, and general commonsense explanations.

## Limitations

- Quality scores are only available for FineWeb EDU and DCLM sources; Pile subsets have `null` quality scores
- URLs are only available for FineWeb EDU and DCLM sources
- Text is English-only
- The dataset inherits any biases present in the upstream sources

## License

This dataset is released under **ODC-By** (Open Data Commons Attribution License), consistent with the upstream source licenses:
- FineWeb EDU: ODC-By
- DCLM-baseline: ODC-By
- Pile (uncopyrighted subsets): Public domain / permissive

## Citation

```bibtex
@dataset{diverse_2.5m,
  title={Diverse Source Text Dataset},
  author={blythet},
  year={2025},
  url={https://huggingface.co/datasets/blythet/diverse-2.5m},
  note={2.5M curated, deduplicated multi-domain English texts}
}
```