Datasets:
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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. |