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`](https://huggingface.co/datasets/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:

```text
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

1. **Raw Curation (Day 1):**
   - Filtered from the [McAuley-Lab/Amazon-Reviews-2023](https://huggingface.co/datasets/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.
2. **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`

```python
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

```python
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](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023) 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.

	---
	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`](https://huggingface.co/datasets/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:

	```text
	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

	1. Raw Curation (Day 1):
	- Filtered from the [McAuley-Lab/Amazon-Reviews-2023](https://huggingface.co/datasets/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.
	2. 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`

	```python
	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

	```python
	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](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023) 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.