Upload dataset/DATASET_README.md with huggingface_hub

dataset/DATASET_README.md

@@ -9,7 +9,7 @@ This document lists the datasets under `RecBole/dataset/` used in this project:
 | Dataset (folder) | Task | Main file | Raw source |
 |------------------|------|-----------|------------|
 | 30music | SBR | `30music.inter` | ReMAP Lab (link below) |
-| nowp | SBR | `nowp.inter` | Zenodo |
+| NowP | SBR | `nowp.inter` | Zenodo |
 | amazon_reviews_books | SBR | `amazon_reviews_books.inter` | Amazon Reviews (Books) |
 | amazon_reviews_grocery_and_gourmet_food | CF | `amazon_reviews_grocery_and_gourmet_food.inter` | Amazon Reviews (Grocery) |
 | movielens | CF | `movielens.inter` | MovieLens (Kaggle/GroupLens) |
@@ -41,7 +41,7 @@ This document lists the datasets under `RecBole/dataset/` used in this project:
 
 ---
 
-## 2. nowp (Nowplaying)
+## 2. NowP (NowPlaying)
 
 - **Folder:** `dataset/nowp/`
 - **Task:** SBR
@@ -50,7 +50,7 @@ This document lists the datasets under `RecBole/dataset/` used in this project:
 
 **Download**
 
-- **Source:** [NOWP (Zenodo)](https://zenodo.org/records/2594483) — music listening dataset.
+- **Source:** [NowP (Zenodo)](https://zenodo.org/records/2594483) — music listening dataset.
 - Place the CSV that contains session-level data in `dataset/nowp/`. The script expects **`sessions_2018.csv`** with columns including `user_id`, `session_id`, `timestamp`, and an item identifier (e.g. `musicbrainz_id`).
 
 **Building from raw (this repo)**
@@ -79,7 +79,7 @@ Both **amazon_reviews_books** and **amazon_reviews_grocery_and_gourmet_food** us
 
 ---
 
-## 3. amazon_reviews_books
+## 3. Books
 
 - **Folder:** `dataset/amazon_reviews_books/`
 - **Task:** SBR (with sessions and rating)
@@ -115,7 +115,7 @@ Both **amazon_reviews_books** and **amazon_reviews_grocery_and_gourmet_food** us
 
 ---
 
-## 4. amazon_reviews_grocery_and_gourmet_food
+## 4. Food
 
 - **Folder:** `dataset/amazon_reviews_grocery_and_gourmet_food/`
 - **Task:** CF (no sessions)
@@ -142,7 +142,7 @@ Both **amazon_reviews_books** and **amazon_reviews_grocery_and_gourmet_food** us
 
 ---
 
-## 5. movielens
+## 5. MovieLens
 
 - **Folder:** `dataset/movielens/`
 - **Task:** CF
@@ -173,7 +173,7 @@ Both **amazon_reviews_books** and **amazon_reviews_grocery_and_gourmet_food** us
 
 ---
 
-## 6. rsc15 (RecSys Challenge 2015)
+## 6. RSC15 (RecSys Challenge 2015)
 
 - **Folder:** `dataset/rsc15/`
 - **Task:** SBR (session-based; session = user_id in some configs)
@@ -202,7 +202,7 @@ Both **amazon_reviews_books** and **amazon_reviews_grocery_and_gourmet_food** us
 
 ---
 
-## 7. tafeng (NBR)
+## 7. TaFeng (NBR)
 
 - **Folder:** `dataset/tafeng/`
 - **Task:** Next-basket recommendation (NBR)
@@ -223,7 +223,7 @@ Both **amazon_reviews_books** and **amazon_reviews_grocery_and_gourmet_food** us
 
 ---
 
-## 8. dunnhumby (NBR)
+## 8. Dunnhumby (NBR)
 
 - **Folder:** `dataset/dunnhumby/`
 - **Task:** NBR
@@ -248,7 +248,7 @@ Both **amazon_reviews_books** and **amazon_reviews_grocery_and_gourmet_food** us
 
 ---
 
-## 9. instacart (NBR)
+## 9. Instacart (NBR)
 
 - **Folder:** `dataset/instacart/`
 - **Task:** NBR