castorini/NanoKnow-Fineweb-Edu-Index

NanoKnow FineWeb-Edu Lucene Index

[Paper] [Code]

A pre-built Lucene BM25 index over karpathy/fineweb-edu-100b-shuffle—the exact pre-training corpus used by the nanochat family of language models. Built with Anserini.

This index is part of the NanoKnow project: github.com/castorini/NanoKnow

Index Details

Property	Value
Corpus	karpathy/fineweb-edu-100b-shuffle
Documents	97,230,848
Index Size	~325 GB (extracted)
Index Type	Lucene (BM25)
Built With	Anserini / Pyserini
Distribution	6 × tar.part.* files (~324 GB total), 680 Lucene segment files when extracted

Document ID Format

Each document has a unique ID: shard_XXXXX_YYYYY

• XXXXX: zero-padded shard number (0-1822)
• YYYYY: row offset within the parquet shard

For example, shard_00151_20323 refers to row 20,323 in shard 151 of the FineWeb-Edu parquet files.

Usage

Download

The index is distributed as 6 split tar parts. Download all 6 parts and reassemble:

# Download all 6 parts (each ~64 GB; part.05 is ~4.4 GB)
for i in 00 01 02 03 04 05; do
  wget https://huggingface.co/datasets/castorini/NanoKnow-Fineweb-Edu-Index/resolve/main/lucene-inverted.fineweb-edu-100b-karpathy.20260416.tar.part.$i
done

# (Optional) Verify checksums
md5sum -c <<'EOF'
309e75651d954a4d81edc6bc5b8f1d38  lucene-inverted.fineweb-edu-100b-karpathy.20260416.tar.part.00
313260d601b88ec443d2e7db94df08df  lucene-inverted.fineweb-edu-100b-karpathy.20260416.tar.part.01
a2b446e7a40d89b1975c95f1abbd8683  lucene-inverted.fineweb-edu-100b-karpathy.20260416.tar.part.02
1e647f11aa01016a53f6c0847ce7ae86  lucene-inverted.fineweb-edu-100b-karpathy.20260416.tar.part.03
47a49ee4b2c7344b625e999c9658f817  lucene-inverted.fineweb-edu-100b-karpathy.20260416.tar.part.04
65ec80b055978356e5bd1772bdf18151  lucene-inverted.fineweb-edu-100b-karpathy.20260416.tar.part.05
EOF

# Reassemble + extract (streaming; never materializes the 325 GB tar on disk)
cat lucene-inverted.fineweb-edu-100b-karpathy.20260416.tar.part.* | tar -xf -

# This creates the directory:
#   lucene-inverted.fineweb-edu-100b-karpathy.20260416/

Alternatively, you can use the Hugging Face CLI to fetch all 6 parts in one shot:

hf download castorini/NanoKnow-Fineweb-Edu-Index --repo-type dataset --local-dir ./fineweb-edu-index
cd ./fineweb-edu-index
cat lucene-inverted.fineweb-edu-100b-karpathy.20260416.tar.part.* | tar -xf -

Search with Pyserini

from pyserini.search.lucene import LuceneSearcher

searcher = LuceneSearcher("./lucene-inverted.fineweb-edu-100b-karpathy.20260416")
print(f"Index contains {searcher.num_docs:,} documents")

hits = searcher.search("What is the capital of France?", k=10)
for hit in hits:
    print(f"{hit.docid}: {hit.score:.4f}")

Retrieve Document Text

import json

doc = searcher.doc("shard_00151_20323")
text = json.loads(doc.raw())["contents"]
print(text[:500])

Reproducing BM25 Effectiveness

This index reproduces the published Anserini regression for NanoKnow v1 (NQ-Open validation): R@20 = 0.3283 with default BM25 (k1=0.9, b=0.4). See the Anserini documentation for the full reproduction recipe.

Related Resources

• Benchmark Qrels: LingweiGu/NanoKnow_Benchmark — Pre-built relevance judgments that partition SQuAD and NQ questions into supported/unsupported splits based on this corpus.
• Code: github.com/castorini/NanoKnow — Scripts to project new benchmarks onto this index, evaluate nanochat checkpoints, and analyze frequency effects.

Citation

@article{gu2026nanoknow,
  title={NanoKnow: How to Know What Your Language Model Knows},
  author={Gu, Lingwei and Jedidi, Nour and Lin, Jimmy},
  journal={arXiv preprint arXiv:2602.20122},
  year={2026}
}

License

Apache 2.0