| --- |
| license: other |
| license_name: academic-non-commercial |
| task_categories: |
| - text-classification |
| - tabular-regression |
| language: |
| - en |
| tags: |
| - sample-product-reviews |
| - price-prediction |
| - llm-engineering |
| - e-commerce |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Dataset Card for `items_raw_lite` |
|
|
| A curated, balanced subset of product metadata derived from the **Amazon Reviews 2023** dataset. This dataset is prepared for training and evaluating Large Language Models (LLMs) on price prediction and value estimation tasks. |
|
|
| --- |
|
|
| ## Dataset Summary |
|
|
| - **Total Datapoints:** 22,000 products |
| - **Splits:** |
| - `train`: 20,000 items |
| - `validation`: 1,000 items |
| - `test`: 1,000 items |
| - **Price Range:** $0.50 – $999.49 |
| - **Minimum Text Length:** 600 characters |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| Each datapoint represents a product with cleaned metadata: |
|
|
| | Feature | Type | Description | |
| | :--- | :--- | :--- | |
| | `title` | `string` | The product title | |
| | `category` | `string` | Product category (e.g., `Appliances`, `Tools_and_Home_Improvement`, `Automotive`) | |
| | `price` | `float` | Price in USD (ground-truth target) | |
| | `full` | `string` | Cleansed concatenation of title, description, features, and specs (model input prompt) | |
| | `weight` | `float` | Item shipping weight normalized to pounds (if available, otherwise `0.0`) | |
|
|
| --- |
|
|
| ## Preprocessing & Curation |
|
|
| The raw metadata was scrubbed and balanced to produce high-signal inputs for LLM fine-tuning: |
|
|
| 1. **Noise Removal:** |
| - Uninformative metadata keys (such as `Part Number`, `Best Sellers Rank`, `Item model number`, `Batteries Required?`) were eliminated. |
| - Long alphanumeric serial codes and model IDs were stripped with regex to prevent the model from memorizing product codes. |
| - Excessive whitespace and formatting artifacts were normalized. |
|
|
| 2. **Quality Filtering:** |
| - Products without valid prices or outside the `$0.50 – $999.49` range were dropped. |
| - Products with fewer than 600 characters of total descriptive text were excluded to ensure sufficient context for price estimation. |
|
|
| 3. **Weighted Rebalancing:** |
| - Real-world e-commerce data is heavily skewed toward cheap products ($5–$20). |
| - Quadratic price weighting ($w \propto \text{normalized\_price}^2$) with category dampening was applied during sampling to ensure strong representation of higher-priced items. |
| |
| --- |
| |
| ## Quickstart Usage |
| |
| ```python |
| from datasets import load_dataset |
|
|
| # Load dataset from Hugging Face Hub |
| dataset = load_dataset("I4031/items_raw_lite") |
| |
| # View a single example |
| print("Input Prompt:") |
| print(dataset["train"][0]["full"]) |
| print(f"Target Price: ${dataset['train'][0]['price']}") |
| ``` |
| |
| --- |
| |
| ## Source & Attribution |
| |
| Derived from the [Amazon-Reviews-2023 dataset](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023) curated by the McAuley Lab at UC San Diego. |
| |
| --- |
| |
| ## License & Terms of Use |
| |
| - **Usage:** Strictly for **non-commercial academic research and educational purposes**. |
| - **Governing Policies:** Subject to Amazon's Conditions of Use and customer reviews guidelines. Commercial use is not permitted. |
