Datasets:
File size: 1,863 Bytes
f266479 54bc1fc f266479 54bc1fc f266479 54bc1fc f266479 54bc1fc f266479 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ---
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
|