cipher982/wine-text-126k

Wine Text Dataset 126K

A comprehensive dataset of 125,787 wine records with detailed descriptions, pricing, categories, and regions. This dataset is perfect for natural language processing, recommendation systems, and wine-related machine learning tasks.

Dataset Description

This dataset contains rich textual information about wines scraped from wine retailer websites. Each record includes the wine name, detailed tasting notes and descriptions, pricing information, wine category classification, and geographic region.

Key Features

• 125,787 wine records with high-quality text descriptions
• Rich descriptions with tasting notes, production details, and wine characteristics
• Pricing information for market analysis and recommendation systems
• Wine categories: red_wine, white_wine, sparkling, rosé, dessert, other
• Geographic regions: california, france, italy, other
• Stable IDs for linking with companion image dataset

Dataset Structure

{
  "id": "wine_000001",                    # Unique wine identifier
  "name": "Dom Perignon Vintage 2008",    # Wine name
  "description": "Complex champagne with...", # Detailed description
  "price": 199.97,                        # Price in USD
  "category": "sparkling",                 # Wine type classification
  "region": "france",                      # Geographic region
  "image_id": "wine_000001"               # Links to companion image dataset
}

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("cipher982/wine-text-126k")

# Access the data
wine_data = dataset["train"]

# Example: Get wine descriptions for NLP tasks
descriptions = wine_data["description"]

# Example: Filter by wine category
sparkling_wines = wine_data.filter(lambda x: x["category"] == "sparkling")

# Example: Price analysis
import pandas as pd
df = wine_data.to_pandas()
price_stats = df.groupby("category")["price"].describe()

Data Quality

• Complete coverage: No missing values in any field
• Rich text: 91% of wines have detailed descriptions (average ~500 characters)
• Price range: $0 - $19,999 (median: $24, mean: $49)
• Geographic diversity: Wines from major wine regions worldwide
• Category balance: Good representation across wine types

Category Distribution

Category	Count	Percentage
red_wine	62,187	49.4%
other	30,509	24.3%
white_wine	26,251	20.9%
rosé	2,713	2.2%
dessert	2,520	2.0%
sparkling	1,607	1.3%

Region Distribution

Region	Count	Percentage
other	105,841	84.2%
california	10,887	8.7%
france	4,839	3.8%
italy	4,220	3.4%

Companion Datasets

This text dataset is designed to work with a companion image dataset:

• wine-images-126k (coming soon): Contains wine bottle images linked by image_id

Use Cases

• Text Classification: Wine category prediction from descriptions
• Recommendation Systems: Content-based wine recommendations
• Price Prediction: Predict wine prices from descriptions and features
• Text Generation: Generate wine descriptions and tasting notes
• Sentiment Analysis: Analyze wine review sentiment and quality indicators
• Information Extraction: Extract wine characteristics (vintage, grape varieties, etc.)

Ethical Considerations

• Data Source: Collected from public wine retailer websites
• Privacy: No personal information included
• Commercial Use: Please respect original retailers' terms of service
• Accuracy: Descriptions and prices reflect retailer data at time of collection

Citation

If you use this dataset in your research, please cite:

@dataset{wine_text_126k,
  title={Wine Text Dataset 126K},
  author={David Rose},
  year={2025},
  url={https://huggingface.co/datasets/cipher982/wine-text-126k}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC-BY-4.0).

You are free to:

• 🔄 Share — copy and redistribute the material in any medium or format
• 🔧 Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

• 📝 Attribution — You must give appropriate credit and indicate if changes were made

Data Collection Notice: The underlying wine information was collected from publicly available retailer websites for research purposes under fair use. This dataset compilation and the stable ID system is our original contribution. Users should respect the intellectual property rights of the original wine descriptions and retailer content.