Datasets:
metadata
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 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
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