Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
Update README.md
Browse files
README.md
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path: data/train-*
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- split: test
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path: data/test-*
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---
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path: data/train-*
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- split: test
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path: data/test-*
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license: mit
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task_categories:
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- text-generation
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language:
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- en
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size_categories:
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- 10K<n<100K
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tags:
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- price-prediction
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- product-descriptions
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- fine-tuning
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- e-commerce
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pretty_name: Pricer Data (Small)
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---
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# Pricer Data (Small)
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A dataset of product descriptions paired with their prices, designed for fine-tuning language models to predict product prices from text descriptions.
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## Dataset Summary
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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.
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## Dataset Structure
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| Split | Rows |
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|---|---|
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| Train | 20,000 |
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| Test | 8,544 |
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| **Total** | **28,544** |
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### Fields
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- **text** (`string`): A prompt containing the product name, description, specifications, and the prefix "Price is $" for the model to complete.
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- **price** (`float64`): The actual price of the product in USD.
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### Example
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```
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{
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"text": "How much does this cost to the nearest dollar?\n\n[Product title and description]\n\nPrice is $",
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"price": 82.09
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}
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```
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## Data Distribution
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- **Text lengths:** Primarily between 483–580 characters (2.2% variance)
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- **Price range:** Most prices fall between $1.11 and $101.00 (87.3% of data)
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## Usage
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```python
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from datasets import load_dataset
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dataset = load_dataset("saxon11/pricer-data-small")
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train = dataset["train"]
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test = dataset["test"]
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```
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## Intended Use
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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.
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## Source
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Product descriptions and prices sourced from e-commerce listings, primarily covering appliance parts, accessories, and home goods.
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## Author
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[Josh Janzen](https://huggingface.co/saxon11) · [joshjanzen.com](https://www.joshjanzen.com)
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