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@@ -21,4 +21,74 @@ configs:
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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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+
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+ # Pricer Data (Small)
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+
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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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+
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+ ## Dataset Summary
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+
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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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+
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+ ## Dataset Structure
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+
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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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+
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+ ### Fields
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+
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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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+
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+ ### Example
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+
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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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+
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+ ## Data Distribution
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+
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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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+
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+ ## Usage
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+
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+ ```python
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+ from datasets import load_dataset
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+
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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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+
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+ ## Intended Use
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+
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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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+
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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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+
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+ ## Author
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+
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+ [Josh Janzen](https://huggingface.co/saxon11) · [joshjanzen.com](https://www.joshjanzen.com)