FineWeb-Edu (Lance Format)

FineWeb-edu dataset with over 1.5 billion rows. Each passage ships with cleaned text, metadata, and 384-dim text embeddings for retrieval-heavy workloads.

Load via datasets.load_dataset

import datasets

hf_ds = datasets.load_dataset(
    "lance-format/fineweb-edu",
    split="train",
    streaming=True,
)
# Take first three rows and print titles
for row in hf_ds.take(3):
    print(row["title"])

Use Lance's native connector when you need ANN search, FTS, or direct access to embeddings while still pointing to the copy hosted on Hugging Face:

import lance

ds = lance.dataset("hf://datasets/lance-format/fineweb-edu/data/train.lance")print(f"Total passages: {ds.count_rows():,}")

These tables can also be consumed by LanceDB, the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.

import lancedb

db = lancedb.connect("hf://datasets/lance-format/fineweb-edu/data")
tbl = db.open_table("train")
print(f"LanceDB table opened with {len(tbl)} passages")

The dataset hosted on Hugging Face Hub does not currently have pre-built ANN (vector) or FTS (full-text search) indices.

• For any search or similarity workloads, you should download the dataset locally and build indices yourself.

# Download once
huggingface-cli download lance-format/fineweb-edu --repo-type dataset --local-dir ./fineweb-edu

# Then load locally and build indices
import lance
ds = lance.dataset("./fineweb-edu")
# ds.create_index(...)

Why Lance?

• Optimized for AI workloads: Lance keeps multimodal data and vector search-ready storage in the same columnar format designed for accelerator-era retrieval (see lance.org).
• Images + embeddings + metadata travel as one tabular dataset.
• On-disk, scalable ANN index means
• Schema evolution lets you add new features/columns (moderation tags, embeddings, etc.) without rewriting the raw data.

Quick Start (Lance Python)

import lance
import pyarrow as pa

lance_ds = lance.dataset("hf://datasets/lance-format/fineweb-edu/data/train.lance")

# Browse titles & language without touching embeddings
rows = lance_ds.scanner(
    columns=["title", "language"],
    limit=5
).to_table().to_pylist()

# Vector similarity from the on-dataset ANN index
ref = lance_ds.take([0], columns=["text_embedding", "title"])
query_vec = pa.array([ref.to_pylist()[0]["text_embedding"]],
                     type=ref.schema.field("text_embedding").type)

results = lance_ds.scanner(
    nearest={
        "column": "text_embedding",
        "q": query_vec[0],
        "k": 5,
        "nprobes": 8,
        "refine_factor": 20,
    },
    columns=["title", "language", "text"],
).to_table().to_pylist()

Hugging Face Streaming Note

• Streaming uses conservative ANN parameters (nprobes, refine_factor) to stay within HF rate limits.
• Prefer local copies (huggingface-cli download lance-format/fineweb-edu --local-dir ./fineweb) for heavy workloads, then point Lance at ./fineweb.

Dataset Schema

Common columns you'll find in this Lance dataset:

• text – cleaned passage content.
• title – page/article title when available.
• url – canonical source URL.
• language + language_probability – detector outputs for filtering.
• Quality metadata from FineWeb-Edu (e.g., heuristic scores or length stats).
• text_embedding – 384-dimension float32 vector for retrieval.

Usage Examples

Search snippets for reference The vector/FTS examples below show the Lance APIs you’ll use once indexes are available. The hosted dataset doesn’t yet ship ANN/FTS indexes—download locally (or build indexes yourself) before running them. Pre-built indexes are coming soon.

1. Sample documents without embeddings

scanner = ds.scanner(
    columns=["title", "language", "text"],
    filter="language = 'en'",
    limit=5,
)
for doc in scanner.to_table().to_pylist():
    print(doc["title"], doc["language"])
    print(doc["text"][:200], "...\n")

2. Vector search for semantically similar passages

ref_doc = ds.take([123], columns=["text_embedding", "title", "text"]).to_pylist()[0]
emb_type = ds.to_table(columns=["text_embedding"], limit=1).schema.field("text_embedding").type
query = pa.array([ref_doc["text_embedding"]], type=emb_type)

neighbors = ds.scanner(
    nearest={
        "column": "text_embedding",
        "q": query[0],
        "k": 6,
        "nprobes": 8,
        "refine_factor": 20,
    },
    columns=["title", "language", "text"],
).to_table().to_pylist()[1:]

LanceDB Vector Search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/fineweb-edu/data")
tbl = db.open_table("train")

# Get a passage to use as a query
ref_passage = tbl.limit(1).offset(123).select(["text_embedding", "text"]).to_pandas().to_dict('records')[0]
query_embedding = ref_passage["text_embedding"]

results = tbl.search(query_embedding) \
    .limit(5) \
    .to_list()

3. Full-text search with Lance FTS

hits = ds.scanner(
    full_text_query="quantum computing",
    columns=["title", "language", "text"],
    limit=10,
    fast_search=True,
).to_table().to_pylist()

LanceDB Full-Text Search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/fineweb-edu/data")
tbl = db.open_table("train")

results = tbl.search("quantum computing") \
    .select(["title", "language", "text"]) \
    .limit(10) \
    .to_list()

See fineweb_edu/example.py on lance-huggingface repo for a complete walkthrough that combines HF streaming batches with Lance-powered retrieval.

Dataset Evolution

Lance supports flexible schema and data evolution (docs). You can add/drop columns, backfill with SQL or Python, rename fields, or change data types without rewriting the whole dataset. In practice this lets you:

• Introduce fresh metadata (moderation labels, embeddings, quality scores) as new signals become available.
• Add new columns to existing datasets without re-exporting terabytes of video.
• Adjust column names or shrink storage (e.g., cast embeddings to float16) while keeping previous snapshots queryable for reproducibility.

import lance
import pyarrow as pa
import numpy as np

# Assume ds is a local Lance dataset
# ds = lance.dataset("./fineweb_edu_local")

base = pa.table({"id": pa.array([1, 2, 3]), "text": pa.array(["A", "B", "C"])})
dataset = lance.write_dataset(base, "fineweb_evolution", mode="overwrite")

# 1. Add a schema-only column (data to be added later)
dataset.add_columns(pa.field("subject", pa.string()))

# 2. Add a column with data
dataset.add_columns({"quality_bucket": "'unknown'"})

# 3. Generate rich columns via Python batch UDFs
@lance.batch_udf()
def random_embedding(batch):
    vecs = np.random.rand(batch.num_rows, 384).astype("float32")
    return pa.RecordBatch.from_arrays(
        [pa.FixedSizeListArray.from_arrays(vecs.ravel(), 384)],
        names=["text_embedding"],
    )

dataset.add_columns(random_embedding)

# 4. Bring in  annotations with merge
labels = pa.table({"id": pa.array([1, 2, 3]), "label": pa.array(["math", "history", "science"])})
dataset.merge(labels, "id")

# 5. Rename or cast columns as needs change
dataset.alter_columns({"path": "subject", "name": "topic"})
dataset.alter_columns({"path": "text_embedding", "data_type": pa.list_(pa.float16(), 384)})

You can iterate on embeddings, quality tags, or moderation fields while keeping earlier dataset versions available for reproducible experiments.