---
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](https://huggingface.co/datasets/HuggingFaceFW/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)](https://arxiv.org/abs/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)](https://arxiv.org/abs/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)](https://arxiv.org/abs/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

```python
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](https://huggingface.co/datasets/HuggingFaceFW/fineweb) (10 billion tokens).

## License

ODC-By 1.0 (same as FineWeb)

## Citation

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

```bibtex
@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}
}
```