Update README.md

README.md

@@ -72,7 +72,7 @@ tbl = db.open_table("train")
 print(f"LanceDB table opened with {len(tbl)} passages")
 ```
 
-
+## Index Creation
 
 > The dataset hosted on Hugging Face Hub does **not** currently have pre-built ANN (vector) or FTS (full-text search) indices.
 >
@@ -90,18 +90,7 @@ print(f"LanceDB table opened with {len(tbl)} passages")
 > ```
 >
 
-
-## 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, all accelerated by on-disk indexes.
-
-
-## Quick Start (Lance Python)
+## Quick Start
 
 ```python
 import lance
@@ -136,16 +125,6 @@ results = lance_ds.scanner(
 > - 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**