FineWeb-Edu (Lance Format)

FineWeb-edu dataset consists of over 1.5 billion rows of educational web pages filtered from the FineWeb dataset. Each passage ships with cleaned text, metadata, and 384-dim text embeddings for retrieval-heavy workloads.

Why Lance?

Lance is an open-source format designed for multimodal AI data, offering significant advantages over traditional formats for modern AI workloads.

• Blazing Fast Random Access: Optimized for fetching scattered rows, making it ideal for random sampling, real-time ML serving, and interactive applications without performance degradation.
• Native Multimodal Support: Store text, embeddings, and other data types together in a single file. Large binary objects are loaded lazily, and vectors are optimized for fast similarity search.
• Efficient Data Evolution: Add new columns and backfill data without rewriting the entire dataset. This is perfect for evolving ML features, adding new embeddings, or introducing moderation tags over time.
• Versatile Querying: Supports combining vector similarity search, full-text search, and SQL-style filtering in a single query, accelerated by on-disk indexes.

Quick Start

Load with 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"])

Load with Lance

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():,}")

⚠️ HuggingFace Streaming Note

You may hit rate limits on HuggingFace's free tier. For best performance and to avoid rate limits, pass a token for an account with a Pro, Teams or Enterprise subscription (which come with much higher rate limits), or download the dataset locally:

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

# Then load locally
ds = lance.dataset("./fineweb-edu")

Streaming is recommended only for quick exploration and testing.

Load with LanceDB

These tables can also be consumed by LanceDB, the multimodal lakehouse for AI (built on top of Lance). LanceDB provides several convenience APIs for search, index creation and data updates on top of the Lance format.

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")

Index Creation

This dataset does not currently come with a pre-built ANN (vector).

To run vector search queries, you should download the dataset locally and build the index yourself. The following steps show how to do this.

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

# Load the local dataset in Lance
import lance
ds = lance.dataset("./fineweb-edu")

# Build a vector index as needed
# ds.create_index(...)

See the Lance documentation for the index building API.

Quick Start

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.

Usage Examples

The steps below assume you've created an index on the dataset locally.

1. Sample documents

You can project specific columns (excluding the embeddings) and run filter queries on them.

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

The example below shows a vector search on the text_embedding column.

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:]

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()

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.

LanceDB

LanceDB users can follow the following examples to run search queries on the dataset.

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()

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()

Citation

You can cite the paper from orginal dataset (https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) https://arxiv.org/abs/2406.17557 or this dataset:

@misc{lozhkov2024fineweb-edu,
    author       = { Lozhkov, Anton and Ben Allal, Loubna and von Werra, Leandro and Wolf, Thomas },  
    title        = { FineWeb-Edu: the Finest Collection of Educational Content }, 
    year         = 2024,  
    url          = { https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu },  
    doi          = { 10.57967/hf/2497 },
    publisher    = { Hugging Face }
}