README.md

metadata

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 and frontend 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 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
• UI Frontend (Gradio): CoVE UI

These Spaces dynamically fetch data from this dataset repo using huggingface_hub.

---

🐍 Example: Load Embeddings via huggingface_hub

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)


⸻