---
license: odc-by
language:
  - en
task_categories:
  - text-classification
size_categories:
  - 100K<n<1M
source_datasets:
  - HuggingFaceFW/fineweb
pretty_name: FineWeb-Edu scores via NeMo Curator on HF Jobs
tags:
  - fineweb
  - nemo-curator
  - hf-jobs
  - educational-quality
  - demonstration
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*/*.jsonl
---

# FineWeb-Edu scores via NeMo Curator on HF Jobs

400,000 English web documents from [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) (the first 400k rows of the `sample/10BT` subset, file `000_00000.parquet`), scored for educational quality with the [FineWeb-Edu classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier) running as [NVIDIA NeMo Curator](https://github.com/NVIDIA-NeMo/Curator)'s `FineWebEduClassifier` stage on [Hugging Face Jobs](https://huggingface.co/docs/huggingface_hub/en/guides/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](https://github.com/davanstrien/nemo-curator/tree/tutorial/hf-jobs-array/tutorials/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`.

> [!NOTE]
> 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

```python
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](https://huggingface.co/datasets/HuggingFaceFW/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](https://opendatacommons.org/licenses/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](https://huggingface.co/HuggingFaceFW) (FineWeb, FineWeb-Edu classifier); pipeline stage by [NVIDIA NeMo Curator](https://github.com/NVIDIA-NeMo/Curator). Scored and repackaged by [Daniel van Strien](https://huggingface.co/davanstrien).

```bibtex
@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}
}
```