Full upload: dataset with FAISS, NPY, Parquet, JSON, etc.

.gitattributes

@@ -62,3 +62,6 @@ meta.json filter=lfs diff=lfs merge=lfs -text
 *.index filter=lfs diff=lfs merge=lfs -text
 items_beauty_concat.faiss filter=lfs diff=lfs merge=lfs -text
 items_beauty_weighted.faiss filter=lfs diff=lfs merge=lfs -text
+faiss/items_beauty_concat.faiss filter=lfs diff=lfs merge=lfs -text
+faiss/items_beauty_weighted.faiss filter=lfs diff=lfs merge=lfs -text
+json/meta.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,38 +1,162 @@
----
-license: cc-by-nc-4.0
----
-Here is a shorter version of the README.md suitable for the Hugging Face dataset card view (top-level summary users see when browsing your dataset):
-
-⸻
-
-
-# 📦 Amazon Beauty Subset for MMR-Agentic-CoVE
-
-This dataset powers the **MMR-Agentic-CoVE** recommender system and contains a compact, multimodal slice of the Amazon Beauty product data. It includes:
-
-- ✅ JSON configs & sequences
-- ✅ NPY embeddings (text, image, meta, CoVE)
-- ✅ Parquet structured tables
-- ✅ PEFT model weights (LoRA/adapter)
-- ✅ FAISS indexes for fast retrieval
-
-## 🧭 Folder Structure
-
-json/      → ID maps, defaults, sequences
-npy/       → Embeddings & logits
-parquet/   → Metadata & user-item tables
-model/     → Fine-tuned model weights
-faiss/     → Item FAISS indexes
-
-## 🔌 Paired Spaces
-
-- **API Backend** → [CoVE API](https://huggingface.co/spaces/mickey1976/cove-api)
-- **Gradio UI** → [CoVE UI](https://huggingface.co/spaces/mickey1976/cove-ui)
-
-## 📚 Citation
-
-> Ni, J., et al. (2019). *Amazon Review Dataset*. UCSD.  
-> https://nijianmo.github.io/amazon/index.html
-
-Maintained by [@mickey1976](https://huggingface.co/mickey1976)
-
+---
+tags:
+- recommender
+- multimodal
+- amazon
+- beauty
+- json
+- npy
+- parquet
+- faiss
+- lora
+- huggingface-dataset
+---
+
+# 📦 Amazon Beauty Subset for MMR-Agentic-CoVE
+
+This dataset contains preprocessed files for the "Beauty" category from the Amazon Reviews dataset.
+It supports the MMR-Agentic-CoVE recommender system, including FAISS indexes, LoRA-tuned model weights, and multimodal features.
+
+Use this dataset with the backend [`cove-api`](https://huggingface.co/spaces/mickey1976/cove-api) and frontend [`cove-ui`](https://huggingface.co/spaces/mickey1976/cove-ui) for live testing.
+
+
+-
+license: cc-by-nc-4.0
+---
+Here is a revised and complete version of your README.md for the Hugging Face dataset repo mayankc-amazon_beauty_subset, reflecting the reorganized folder structure, usage examples, and links to your API/UI Spaces:
+
+⸻
+
+
+# Amazon Beauty Subset – Structured Dataset for MMR-Agentic-CoVE Recommender
+
+This is a clean, categorized subset of the **Amazon Beauty Products Dataset** curated for the [MMR-Agentic-CoVE](https://huggingface.co/spaces/mickey1976/cove-ui) recommender system. It includes multimodal item data (text, image, metadata), user interactions, FAISS indexes, model outputs, and embedding vectors — all organized for efficient retrieval by the API and UI spaces.
+
+## 🗂️ Folder Structure
+
+.
+├── json/        # Configs, maps, user/item sequences
+├── npy/         # Embedding arrays (text, image, meta, CoVE)
+├── parquet/     # Tabular structured data
+├── model/       # PEFT/LoRA model weights
+├── faiss/       # FAISS index files for nearest neighbor search
+└── README.md
+
+## 📁 Key Files
+
+### `json/`
+- `defaults.json`: Weight config for fusion modes
+- `item_ids.json`, `user_seq.json`, `cove_item_ids.json`: ID mappings and test sets
+
+### `npy/`
+- `text.npy`, `image.npy`, `meta.npy`: Item modality embeddings
+- `cove_logits.npy`, `full_cove_embeddings.npy`: CoVE model outputs
+
+### `parquet/`
+- `reviews.parquet`, `items_catalog.parquet`: Base product metadata
+- `user_text_emb.parquet`: User text embedding vectors
+
+### `model/`
+- `model.safetensors`, `adapter_model.safetensors`: LoRA fine-tuned weights
+
+### `faiss/`
+- `items_beauty_concat.faiss`, `items_beauty_weighted.faiss`: FAISS indexes for fast item retrieval
+
+---
+
+## 🔌 Paired Spaces
+
+- **API Backend (FastAPI):** [CoVE API](https://huggingface.co/spaces/mickey1976/cove-api)
+- **UI Frontend (Gradio):** [CoVE UI](https://huggingface.co/spaces/mickey1976/cove-ui)
+
+These Spaces dynamically fetch data from this dataset repo using `huggingface_hub`.
+
+---
+
+## 🐍 Example: Load Embeddings via `huggingface_hub`
+
+```python
+from huggingface_hub import hf_hub_download
+import numpy as np
+
+# Load text embeddings
+text_emb_path = hf_hub_download(
+    repo_id="mickey1976/mayankc-amazon_beauty_subset",
+    repo_type="dataset",
+    filename="npy/text.npy"
+)
+
+text_embeddings = np.load(text_emb_path)
+
+
+⸻
+
+📖 Citation
+
+Data originally from:
+
+Ni, J., et al. (2019). Amazon Review Dataset. UCSD.
+https://nijianmo.github.io/amazon/index.html
+
+Used here in support of MMR-Agentic-CoVE multimodal recommender architecture.
+
+⸻
+
+🛠 Maintained by
+
+Mayank Choudhary
+GitHub | Hugging Face
+
+---
+
+### ✅ Instructions to Save
+
+1. Overwrite the current `README.md` in your dataset root directory:
+```bash
+nano README.md
+
+(Paste the content above, save with Ctrl + O, exit with Ctrl + X)
+	2.	Commit and push:
+
+git add README.md
+git commit -m "Update README with folder structure and usage guide"
+git push
+
+
+Here is a shorter version of the README.md suitable for the Hugging Face dataset card view (top-level summary users see when browsing your dataset):
+
+⸻
+
+
+# 📦 Amazon Beauty Subset for MMR-Agentic-CoVE
+
+This dataset powers the **MMR-Agentic-CoVE** recommender system and contains a compact, multimodal slice of the Amazon Beauty product data. It includes:
+
+- ✅ JSON configs & sequences
+- ✅ NPY embeddings (text, image, meta, CoVE)
+- ✅ Parquet structured tables
+- ✅ PEFT model weights (LoRA/adapter)
+- ✅ FAISS indexes for fast retrieval
+
+## 🧭 Folder Structure
+
+json/      → ID maps, defaults, sequences
+npy/       → Embeddings & logits
+parquet/   → Metadata & user-item tables
+model/     → Fine-tuned model weights
+faiss/     → Item FAISS indexes
+
+## 🔌 Paired Spaces
+
+- **API Backend** → [CoVE API](https://huggingface.co/spaces/mickey1976/cove-api)
+- **Gradio UI** → [CoVE UI](https://huggingface.co/spaces/mickey1976/cove-ui)
+
+## 📚 Citation
+
+> Ni, J., et al. (2019). *Amazon Review Dataset*. UCSD.  
+> https://nijianmo.github.io/amazon/index.html
+
+Maintained by [@mickey1976](https://huggingface.co/mickey1976)
+
+
+⸻

faiss/items_beauty_concat.faiss

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faiss/items_beauty_weighted.faiss

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file_manifest.json

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+{
+  "faiss": {
+    "concat": "faiss/items_beauty_concat.faiss",
+    "weighted": "faiss/items_beauty_weighted.faiss"
+  },
+  "defaults": "json/defaults.json",
+  "json": [
+    "json/reviews.json",
+    "json/meta.json",
+    "json/leave_one_out_test.json"
+  ],
+  "npy": [
+    "npy/text.npy",
+    "npy/image.npy",
+    "npy/meta.npy",
+    "npy/cove.npy"
+  ],
+  "parquet": [
+    "parquet/items_catalog.parquet",
+    "parquet/reviews.parquet"
+  ],
+  "model": {
+    "adapter": "model/adapter_model.safetensors",
+    "full": "model/model.safetensors"
+  }
+}

json/config.json

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+{
+  "model_type": "gpt2"
+}

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