---
license: apache-2.0
tags:
  - text-classification
  - data-filtering
  - education
language:
  - en
---

# LittleCurriculum Filter — Artifacts

Classifier weights and lexical resources for the **LittleCurriculum** five-stage
K–5 text filter.

These files are the runtime dependencies of
[`littlelearner-ll/littlecurriculum-filter`](https://github.com/littlelearner-ll/littlecurriculum-filter).

## Usage

```bash
git clone https://github.com/littlelearner-ll/littlecurriculum-filter
cd littlecurriculum-filter
pip install -r requirements.txt
python download_artifacts.py          # fetches this repo
python filter_k5.py --in shard.parquet --out kept.parquet
```

## Contents

| Path | Size | Used by | What it is |
|------|------|---------|------------|
| `data/aoa.parquet` | 0.5 MB | Stage 1 | Age-of-Acquisition norms|
| `data/word_log_odds.parquet` | 1.9 MB | Stage 5 | Beyond-K–5 association scores |
| `models/fasttext_grade.bin` | 57 MB | Stage 2 | fastText classifier |
| `models/modernbert_grade/` | 299 MB | Stage 3 | ModernBERT classifier |

Both classifiers predict one of `K5`, `K8`, `K12`, `OOS`
(`id2label = {0: "K5", 1: "K8", 2: "K12", 3: "OOS"}`); the filter retains
documents predicted `K5`.

## Training

The classifiers are distilled from LLM-as-a-judge annotations of FineWeb-Edu,
generated with Google Gemini using prompts initialised from the Common Core
State Standards and refined with automatic prompt optimisation. Full
annotation of FineWeb-Edu would have been prohibitively expensive, which is
what motivates the cascaded design: a cheap lexical stage, then fastText, then
the ~50× more expensive ModernBERT.

## Intended Usage

**Trained for web prose.** Labels come from FineWeb-Edu documents. On
substantially different distributions, retraining
is recommended.

**Fixed to the K–5 boundary.** Retargeting to a different grade band requires
retraining these classifiers.

**Expect whole documents.** The classifiers estimate a document's overall
grade level and have little to work with in a single sentence. Application to
full documents is recommended.

## Citation

```bibtex
@misc{li2026littlelearner,
      title={LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure},
      author={Fanfei Li and Jana Zeller and Manuel Prada-Corral and Thaddäus Wiedemer and Prasanna Mayilvahanan and Ryan Cotterell and Wieland Brendel},
      year={2026},
      eprint={2608.13545},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2608.13545},
}
```

Age-of-Acquisition norms: Kuperman, Stadthagen-Gonzalez & Brysbaert (2012),
*Age-of-acquisition ratings for 30,000 English words*, Behavior Research
Methods 44(4).

## License

Apache-2.0, matching the ModernBERT base model and fastText.