Datasets:

I4031
/

items_raw_lite

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:

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.
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.
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

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 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.

Dataset Card for items_raw_lite