davanstrien/fineweb-edu-showcase

FineWeb-Edu scores via NeMo Curator on HF Jobs

400,000 English web documents from FineWeb (the first 400k rows of the sample/10BT subset, file 000_00000.parquet), scored for educational quality with the FineWeb-Edu classifier running as NVIDIA NeMo Curator's FineWebEduClassifier stage on Hugging Face Jobs.

This is a demonstration artifact, not a curated corpus: it exists as the worked example for a tutorial on running Curator array workloads on HF Jobs. The scores are real and reusable, but the document selection is simply "the first file of a FineWeb sample" — don't read anything into what's included.

How it was produced

The 400k rows were split into 16 input files (25k rows each) and processed by 4 independent GPU Jobs (l4x1) using Curator's own Slurm-array sharding machinery — the NEMO_CURATOR_SLURM_ARRAY_* environment variables, which turn out to be scheduler-agnostic — with zero changes to the library. File-to-shard assignment is hash-based, which is why the four shard directories hold 3/4/5/4 files rather than 4/4/4/4.

One shard was interrupted mid-run (simulating a preemption). Curator's stock retry planner (tutorials/slurm/retry_array.py, unmodified) identified the missing shard from completion manifests written to a Hugging Face storage bucket, and a retry wave completed it — the retried shard re-derived exactly its original file assignment.

Throughput was 292–303 rows/s per L4; the whole run, including the interrupted shard and its retry, cost ≈ $0.50.

Data structure

One train split, 16 JSONL files under data/shard{0..3}/.

column	type	description
id	string	FineWeb document id (urn:uuid:...)
text	string	document text, unchanged from FineWeb
fineweb-edu-score-float	float	raw classifier score (≈0–5)
fineweb-edu-score-int	int	rounded score
fineweb-edu-score-label	string	low_quality / high_quality

Score distribution over all 400,000 rows:

score (int)	label	rows
0	low_quality	48,061
1	low_quality	248,737
2	low_quality	80,309
2	high_quality	81
3	high_quality	19,832
4	high_quality	2,961
5	high_quality	19

The expected FineWeb skew: 94% of documents score ≤2, and only 5.7% carry high_quality.

The label column is thresholded on the float score, not the rounded int — hence the 81 rows with score-int = 2 but label = high_quality. If you filter, use fineweb-edu-score-float or the label; mixing them with the int column will give slightly inconsistent subsets.

Using it

from datasets import load_dataset

ds = load_dataset("davanstrien/fineweb-edu-showcase", split="train")
high = ds.filter(lambda r: r["fineweb-edu-score-label"] == "high_quality")

Plausible uses beyond the tutorial: a ready-made small testbed for score-threshold experiments against the FineWeb-Edu classifier, or a quick source of quality-stratified English web text. For serious educational-quality filtering use FineWeb-Edu itself — that's the full-scale version of exactly this pipeline.

Licence and credit

Text is from FineWeb and carries its ODC-By 1.0 licence (with CommonCrawl's terms of use upstream of that). The score columns are model outputs from the FineWeb-Edu classifier.

Source data by HuggingFaceFW (FineWeb, FineWeb-Edu classifier); pipeline stage by NVIDIA NeMo Curator. Scored and repackaged by Daniel van Strien.

@inproceedings{penedo2024fineweb,
  title={The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale},
  author={Penedo, Guilherme and Kydl{\'\i}{\v{c}}ek, Hynek and Lozhkov, Anton and Mitchell, Margaret and Raffel, Colin and Von Werra, Leandro and Wolf, Thomas and others},
  booktitle={NeurIPS Datasets and Benchmarks},
  year={2024}
}