---
license: cc-by-4.0
task_categories:
  - text-generation
language:
  - en
tags:
  - dclm
  - synthetic-data
  - pretraining
  - format-aware
pretty_name: DCLM Cross-Over Source
---

# DCLM Cross-Over Source

Subset of [DCLM-Baseline](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0)
selected for synthetic augmentation with format-aware prompt routing.

## Selection

- Picked every 3th shard (9313 of 27938 shards)
- Word count filter: 50-8000
- Per-site cap: 10,000
- Format detection: skip prompts that duplicate native document format

## Stats

| Metric | Value |
|--------|-------|
| Source docs scanned | 54,947,699 |
| Selected | 54,017,165 |
| Total words | 44,119,449,000 |
| Avg words/doc | 816 |
| Length filtered | 930,534 |
| Site capped | 0 |
| All formats native | 0 |
| Output shards | 9313 |

## Prompt Applicability

| Prompt | Applicable | Would Skip |
|--------|-----------|------------|
| FAQ | 53,731,426 | 285,739 |
| Math | 53,781,379 | 235,786 |
| Table | 54,012,976 | 4,189 |
| Tutorial | 50,824,756 | 3,192,409 |

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `id` | str | Stable hash |
| `text` | str | Document text |
| `url` | str | Source URL |
| `quality_score` | float | DCLM fastText score |
| `word_count` | int | Word count |
| `apply_prompts` | str (JSON list) | Prompts to run |
| `skip_prompts` | str (JSON list) | Prompts to skip |
| `num_applicable_prompts` | int | How many prompts apply |

## Usage

```python
from datasets import load_dataset
import json

ds = load_dataset("essobi/dclm-crossover-source", split="train")

# Docs for FAQ prompt only
faq_docs = ds.filter(lambda x: "faq" in json.loads(x["apply_prompts"]))

# Docs suitable for all 4 prompts (best megadoc candidates)
full = ds.filter(lambda x: x["num_applicable_prompts"] == 4)
```

## License

CC-BY-4.0