Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
metadata
dataset_info:
features:
- name: text
dtype: string
- name: price
dtype: float64
splits:
- name: train
num_bytes: 16062338
num_examples: 20000
- name: test
num_bytes: 6829484
num_examples: 8544
download_size: 11195441
dataset_size: 22891822
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- 10K<n<100K
tags:
- price-prediction
- product-descriptions
- fine-tuning
- e-commerce
pretty_name: 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.