README.md

metadata

language:
  - en
license: odc-by
tags:
  - fineweb
  - web-data
  - pretraining
  - quality-filtered
  - ensemble
dataset_info:
  features:
    - name: text
      dtype: string
    - name: id
      dtype: string
    - name: url
      dtype: string
    - name: dump
      dtype: string
    - name: language_score
      dtype: float64
    - name: token_count
      dtype: int64
    - name: dclm_score
      dtype: float64
    - name: edu_score
      dtype: float64
    - name: edu_int_score
      dtype: int64
    - name: wq_vocabulary_richness
      dtype: float64
    - name: wq_info_density
      dtype: float64
    - name: wq_sentence_quality
      dtype: float64
    - name: wq_structure_score
      dtype: float64
    - name: wq_composite
      dtype: float64
    - name: ensemble_score
      dtype: float64
    - name: quality_tier
      dtype: int64

BetterWeb: An Improved FineWeb

BetterWeb is a quality-filtered version of FineWeb that applies ensemble quality scoring inspired by state-of-the-art web data curation research.

What makes it better?

BetterWeb combines three complementary quality signals, inspired by the findings of multiple research papers:

1. DCLM FastText Quality Classifier

From DataComp-LM (arXiv:2406.11794):

• Trained on OpenHermes-2.5 + ELI5 (positive) vs RefinedWeb (negative)
• Best-performing single classifier: 7B model trained on DCLM-filtered data achieved MMLU 63.7
• Measures general instruction-following quality and reasoning clarity

2. FineWeb-Edu Educational Classifier

From FineWeb (arXiv:2406.17557):

• Linear regression on Snowflake-arctic-embed-m embeddings
• Trained on 460K LLM-annotated samples (0-5 educational scale)
• FineWeb-Edu reaches FineWeb's MMLU score 10x faster (38B vs 350B tokens)

3. Writing Quality Heuristics

Novel set of heuristic metrics measuring:

• Vocabulary richness: Guiraud's corrected type-token ratio
• Information density: Content-word ratio (excluding stop words)
• Sentence quality: Length distribution and variation
• Structural quality: Penalizes list-heavy content

Ensemble Strategy (from Nemotron-CC)

From Nemotron-CC (arXiv:2412.02595):

• FineWeb-Edu and DCLM classifiers only overlap on ~10% of documents
• Using union (keep if EITHER classifier says high-quality) recovers 2.5x more HQ tokens
• This dataset uses union mode: keep if dclm_score >= 0.5 OR edu_int_score >= 3

Quality Tiers

Each document has a quality_tier (0-4) for flexible filtering:

Tier	Label	Criteria
4	Exceptional	DCLM ≥ 0.80 AND edu ≥ 4
3	High	DCLM ≥ 0.65 OR edu ≥ 4
2	Good	DCLM ≥ 0.50 OR edu ≥ 3
1	Moderate	DCLM ≥ 0.30 OR edu ≥ 2
0	Low	Below all thresholds

Usage

from datasets import load_dataset

# Load all BetterWeb data
ds = load_dataset("hynky/betterweb")

# Filter by quality tier
ds_high = ds.filter(lambda x: x["quality_tier"] >= 3)

# Custom filtering using individual scores
ds_custom = ds.filter(lambda x: x["dclm_score"] >= 0.7 and x["edu_int_score"] >= 3)

Scores Explanation

Column	Range	Description
dclm_score	0-1	DCLM fastText P(high_quality)
edu_score	0-5	FineWeb-Edu continuous score
edu_int_score	0-5	FineWeb-Edu rounded integer score
wq_vocabulary_richness	0-1	Guiraud's corrected TTR
wq_info_density	0-1	Content word density
wq_sentence_quality	0-1	Sentence structure quality
wq_structure_score	0-1	Anti-list-content score
wq_composite	0-1	Weighted writing quality
ensemble_score	0-1	Final composite score
quality_tier	0-4	Discrete quality bucket

Source

Filtered from FineWeb sample-10BT (10 billion tokens).

License

ODC-By 1.0 (same as FineWeb)

Citation

If you use this dataset, please cite the underlying research:

@article{penedo2024fineweb,
  title={The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale},
  author={Penedo, Guilherme and Kydlíček, Hynek and others},
  journal={NeurIPS 2024 Datasets and Benchmarks Track},
  year={2024}
}

@article{li2024datacomp,
  title={DataComp-LM: In search of the next data frontier for language models},
  author={Li, Jeffrey and others},
  journal={NeurIPS 2024},
  year={2024}
}

@article{su2024nemotron,
  title={Nemotron-CC: Transforming Web Data into High-Quality Synthetic Data},
  author={Su, Weixin and others},
  year={2024}
}