Update README.md

README.md

@@ -1,10 +1,8 @@
 ---
 pretty_name: Spotify Public Playlists (Nov 2025)
 dataset_summary: >-
-  CSV export of 91,906 Spotify public playlists captured from a PostgreSQL dump
-  in November 2025, including playlist metadata, owner info, follower counts,
-  cover images, JSON blobs with the raw Spotify payload, and historical
-  snapshots.
+  CSV export of 91,906 Spotify public playlists with playlist metadata, owner
+  info, follower counts, cover images, JSON payloads, and historical signals.
 language:
   - en
 license: cc-by-4.0
@@ -21,31 +19,21 @@ task_categories:
 
 # Spotify Public Playlists (November 2025)
 
-This repository contains an open dataset with **91,906** Spotify playlists that
-were present in a PostgreSQL backup captured in **November 2025**. Each row
-represents a playlist and includes high-level metadata (name, owner, follower
-count), cover image URLs, JSON payloads with the full Spotify response, and
-various bookkeeping columns (status, popularity histograms, history, etc.).
-
-The public release ships as a ready-to-use CSV (`playlists.csv`, ~9 GB) plus
-supporting documentation. The original PostgreSQL dump and extractor script are
-kept private but can be shared on request for verification purposes.
-
-> ⚠️ **Attribution + ToS**: This dataset aggregates Spotify metadata originally
-> provided via their APIs. Redistribution is offered under CC BY 4.0, but you are
-> responsible for complying with Spotify's terms of service and ensuring any
-> downstream use respects user privacy.
+This repository contains an open dataset with **91,906** Spotify playlists.
+Each row represents a playlist and includes high-level metadata (name, owner,
+follower count), cover image URLs, JSON payloads with the full Spotify
+response, and various bookkeeping columns (status, popularity histograms,
+history, etc.). The release ships as a ready-to-use CSV (`playlists.csv`,
+~9 GB) plus a small preview file for browsing.
 
 ## Files
 
 | Path | Description |
 | --- | --- |
 | `playlists.csv` | Final 91,906-row CSV (17 columns, ~9 GB) with one playlist per row. |
+| `playlists_preview.csv` | 200-row slice (64 MB) for quick inspection / Hugging Face preview. |
 | `LICENSE` | CC BY 4.0 license text covering this dataset. |
 
-> The original SQL dump and exporter script remain private. Contact the
-> maintainer if you require the raw source data for verification.
-
 ## Schema
 
 | Column | Type | Notes |
@@ -68,16 +56,12 @@ kept private but can be shared on request for verification purposes.
 | `status` | string | ETL status flag from the upstream system. |
 | `popularity_distribution` | JSON (string) | Histogram of track popularity buckets. |
 
-## Regenerating the CSV
-
-The private repository includes an extraction script that streams the SQL dump,
-filters the `public.playlists` table, and writes the CSV with progress
-reporting and batching. Reach out if you need access to reproduce the export.
-
 ## Loading the Dataset
 
 Once the CSV is available (locally or via Hugging Face), you can load it with
-your favorite data tooling:
+your favorite data tooling. For quick experiments or to power the Hugging Face
+table preview, use `playlists_preview.csv` (200 rows). When you need the full
+corpus, switch to `playlists.csv`:
 
 ```python
 import pandas as pd
@@ -97,13 +81,13 @@ chunked `pandas.read_csv(..., chunksize=10_000)`.
 
 1. **Create dataset repo**: `huggingface-cli repo create <namespace>/spotify-playlists-2025 --type dataset`.
 2. **Clone with Git LFS**: `GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/<namespace>/spotify-playlists-2025` then `cd` into it and run `git lfs install`.
-3. **Copy assets**: Bring over `playlists.csv`, `README.md`, and `LICENSE`.
+3. **Copy assets**: Bring over `playlists.csv`, `playlists_preview.csv`, `README.md`, and `LICENSE` (track the large CSVs with Git LFS).
 4. **Track large files**: `git lfs track "*.csv" "*.sql"`. Commit the updated `.gitattributes`.
 5. **Push**: `git add . && git commit -m "Add Spotify playlists dataset" && git push`.
 6. **Add metadata**: The `README.md` you are reading doubles as the Hugging Face dataset card, so it will render on the hub automatically.
 
-Optional: Upload `playlists_full.sql` as a release artifact or keep it in LFS if
-you want to allow full reproducibility for others.
+Optional: Add any additional metadata files (schema diagrams, notebooks, etc.)
+to enrich the dataset card and help users onboard faster.
 
 ## License