Datasets:

I4031
/

items_lite

license: other
license_name: academic-non-commercial
task_categories:
  - text-generation
  - tabular-regression
  - feature-extraction
language:
  - en
tags:
  - e-commerce
  - price-prediction
  - product-summaries
  - llm-engineering
  - synthetic-data
size_categories:
  - 10K<n<100K

Dataset Card for `items_lite`

I4031/items_lite is a standardized, LLM-preprocessed product dataset created for "The Price is Right" capstone project in LLM Engineering.

Building upon I4031/items_raw_lite, this dataset transforms noisy raw e-commerce metadata into clean, structured, and concise product summaries generated by an LLM (gpt-oss-20b). This standard format strips uninformative part numbers and boilerplate, making it optimal for downstream price regression, RAG indexing, and fine-tuning frontier models.

Dataset Summary

Repository: I4031/items_lite
Total Datapoints: 22,000 products
Splits:
- train: 20,000 items
- validation: 1,000 items
- test: 1,000 items
Price Range: $0.50 – $999.49 USD
Source Data: McAuley-Lab Amazon Reviews 2023 (curated via I4031/items_raw_lite)
Preprocessing Model: openai/gpt-oss-20b (batch rewritten via Groq / LiteLLM)

Dataset Structure

Each datapoint contains the following fields:

Feature	Type	Description
`title`	`string`	Original product title
`category`	`string`	Amazon category (e.g., `Tools_and_Home_Improvement`, `Appliances`, `Electronics`)
`price`	`float`	Actual price in USD (ground-truth target)
`summary`	`string`	Standardized LLM-generated product summary (5-line structured text)
`weight`	`float`	Item shipping weight normalized to pounds (if available, else `0.0`)
`prompt`	`string` / `null`	Optional formatted prompt string for fine-tuning

Summary Format

The summary field follows a strict 5-line schema designed to maximize signal and remove noisy identifiers:

Title: Rewritten short precise title
Category: Category name
Brand: Brand name
Description: 1 sentence product description
Details: 1 sentence on features and materials

Data Pipeline & Preprocessing

Raw Curation (Day 1):
- Filtered from the McAuley-Lab/Amazon-Reviews-2023 dataset.
- Cleaned noise (best seller ranks, uninformative metadata keys, serial codes).
- Price range clamped to $0.50 – $999.49 with quadratic price-rebalancing to ensure strong representation of higher-priced goods.
LLM Standardization (Day 2):
- Every raw product description (full) was processed through openai/gpt-oss-20b with reasoning effort set to low. The processing was performed locally using Ollama and RTX 5070Ti.
- Stripped out serial numbers, ASINs, and part numbers so models learn to predict price based on intrinsic product features, brand, and build quality rather than memorized identifiers.
- Raw full and index id fields were cleared prior to Hub upload to minimize parquet footprint.

Quickstart Usage

Using Hugging Face `datasets`

from datasets import load_dataset

# Load dataset
ds = load_dataset("I4031/items_lite")

# Inspect a train record
sample = ds["train"][0]
print("Category:", sample["category"])
print("Price:   $", sample["price"])
print("\nProduct Summary:\n" + sample["summary"])

Using Course `pricer` Module

from pricer.items import Item

# Load directly into typed Item objects
train, val, test = Item.from_hub("I4031/items_lite")

print(f"Loaded {len(train):,} train, {len(val):,} val, {len(test):,} test items")
print(train[0].summary)
print(f"Target: ${train[0].price}")

Intended Uses & Applications

Price Prediction: Regression models estimating retail prices from textual product features.
LLM Fine-Tuning: Training models (e.g., Llama 3.2 3B, GPT-4o-mini) to reason about value and product pricing.
Vector Search / RAG: Building semantic embeddings and product retrieval vector stores.
Zero-Shot & Few-Shot Evaluation: Benchmarking frontier reasoning models on structured tabular and numerical extraction tasks.

Source & Attribution

Built as part of the LLM Engineering course capstone project ("The Price is Right").
Base data derived from the Amazon-Reviews-2023 dataset curated by McAuley Lab at UC San Diego.

License & Usage Terms

License: Academic Non-Commercial / Research use.
Terms: Subject to Amazon's Conditions of Use and the source dataset terms. Not intended for commercial applications.

Dataset Card for items_lite