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 (3 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 | 255,841 |
| Selected | 251,661 |
| Total words | 196,694,035 |
| Avg words/doc | 781 |
| Length filtered | 4,180 |
| Site capped | 0 |
| All formats native | 0 |
| Output shards | 3 |
Prompt Applicability
| Prompt | Applicable | Would Skip |
|---|---|---|
| FAQ | 250,245 | 1,416 |
| Math | 250,428 | 1,233 |
| Table | 251,632 | 29 |
| Tutorial | 239,300 | 12,361 |
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