saxon11/pricer-data-small

Pricer Data (Small)

A dataset of product descriptions paired with their prices, designed for fine-tuning language models to predict product prices from text descriptions.

Dataset Summary

Each example contains a natural language prompt asking "How much does this cost to the nearest dollar?" followed by a product title, description, and attributes. The target is the actual price as a float value.

Dataset Structure

Split	Rows
Train	20,000
Test	8,544
Total	28,544

Fields

• text (string): A prompt containing the product name, description, specifications, and the prefix "Price is $" for the model to complete.
• price (float64): The actual price of the product in USD.

Example

{
  "text": "How much does this cost to the nearest dollar?\n\n[Product title and description]\n\nPrice is $",
  "price": 82.09
}

Data Distribution

• Text lengths: Primarily between 483–580 characters (2.2% variance)
• Price range: Most prices fall between $1.11 and $101.00 (87.3% of data)

Usage

from datasets import load_dataset

dataset = load_dataset("saxon11/pricer-data-small")
train = dataset["train"]
test = dataset["test"]

Intended Use

This dataset was created to fine-tune a Llama 3.1 8B model using QLoRA and SFTTrainer for the task of product price prediction. It can be used for any text-to-number regression task framed as language model completion.

Source

Product descriptions and prices sourced from e-commerce listings, primarily covering appliance parts, accessories, and home goods.

Author

Josh Janzen · joshjanzen.com