Upload folder using huggingface_hub

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# HuggingFace Hub
+.huggingface/
+
+# Temporary files
+*.tmp
+*.temp
+
+# Data processing scripts (keep dataset files only)
+process_data.py
+clean_data.py
+upload.py

LICENSE

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+Creative Commons Attribution 4.0 International License
+
+Wine Text Dataset 126K
+Copyright (c) 2025 David Rose
+
+This work is licensed under the Creative Commons Attribution 4.0 International License.
+To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
+or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+===============================================================================
+
+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.
+
+The licensor cannot revoke these freedoms as long as you follow the license terms.
+
+-------------------------------------------------------------------------------
+
+Under the following terms:
+
+    Attribution — You must give appropriate credit, provide a link to the license,
+                  and indicate if changes were made. You may do so in any reasonable
+                  manner, but not in any way that suggests the licensor endorses you
+                  or your use.
+
+    No additional restrictions — You may not apply legal terms or technological
+                                measures that legally restrict others from doing
+                                anything the license permits.
+
+-------------------------------------------------------------------------------
+
+Notices:
+
+You do not have to comply with the license for elements of the material in the
+public domain or where your use is permitted by an applicable exception or limitation.
+
+No warranties are given. The license may not give you all of the permissions
+necessary for your intended use. For example, other rights such as publicity,
+privacy, or moral rights may limit how you use the material.
+
+===============================================================================
+
+DATA COLLECTION NOTICE:
+
+The underlying wine information was collected from publicly available retailer
+websites for research purposes. The dataset compilation, data cleaning, stable
+ID system, and structured format represent the original contribution covered
+by this license.
+
+Users should respect the intellectual property rights of the original wine
+descriptions and retailer content. This dataset is intended for research,
+education, and fair use applications.

README.md

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----
-license: cc-by-4.0
----
+---
+pretty_name: Wine Text Dataset 126K
+tags:
+  - wine
+  - food-and-drink
+  - text-classification
+  - text-generation
+  - recommendation-systems
+task_categories:
+  - text-classification
+  - text-generation
+  - feature-extraction
+size_categories:
+  - 100K<n<1M
+license: cc-by-4.0
+language:
+  - en
+dataset_info:
+  features:
+    - name: id
+      dtype: string
+    - name: name
+      dtype: string
+    - name: description
+      dtype: string
+    - name: price
+      dtype: float32
+    - name: category
+      dtype: string
+    - name: region
+      dtype: string
+    - name: image_id
+      dtype: string
+  config_name: default
+  splits:
+    - name: train
+      num_bytes: 30729648
+      num_examples: 125787
+  download_size: 30729648
+  dataset_size: 30729648
+---
+
+# 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
+
+```python
+{
+  "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
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("YOUR_USERNAME/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:
+
+```bibtex
+@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.

test_dataset.py

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+#!/usr/bin/env python3
+"""
+Test the wine dataset before uploading to HuggingFace.
+This script validates the data quality and structure.
+"""
+
+import pandas as pd
+from datasets import Dataset, Features, Value
+
+def test_dataset():
+    """Test the dataset structure and quality."""
+
+    print("🍷 Testing Wine Text Dataset")
+    print("=" * 40)
+
+    # Load the parquet file
+    print("📁 Loading dataset...")
+    df = pd.read_parquet("wine_text_126k.parquet")
+
+    # Basic structure validation
+    print(f"✅ Dataset loaded: {len(df):,} records")
+    print(f"✅ Columns: {list(df.columns)}")
+
+    # Check schema matches expected
+    expected_columns = ['id', 'name', 'description', 'price', 'category', 'region', 'image_id']
+    if list(df.columns) == expected_columns:
+        print("✅ Schema matches expected structure")
+    else:
+        print(f"❌ Schema mismatch. Expected: {expected_columns}")
+        return False
+
+    # Data quality checks
+    print("\n📊 Data Quality Report:")
+
+    # Check for missing values
+    missing = df.isnull().sum()
+    if missing.any():
+        print("❌ Missing values found:")
+        for col, count in missing.items():
+            if count > 0:
+                print(f"  {col}: {count:,} missing")
+        return False
+    else:
+        print("✅ No missing values")
+
+    # Check ID format
+    id_pattern_valid = df['id'].str.match(r'^wine_\d{6}$').all()
+    if id_pattern_valid:
+        print("✅ ID format is correct (wine_XXXXXX)")
+    else:
+        print("❌ Invalid ID format found")
+        return False
+
+    # Check price range
+    price_min, price_max = df['price'].min(), df['price'].max()
+    print(f"✅ Price range: ${price_min:.2f} - ${price_max:.2f}")
+
+    # Check categories
+    categories = df['category'].value_counts()
+    print(f"✅ Wine categories ({len(categories)} types):")
+    for cat, count in categories.head().items():
+        print(f"  {cat}: {count:,}")
+
+    # Check regions
+    regions = df['region'].value_counts()
+    print(f"✅ Regions ({len(regions)} types):")
+    for region, count in regions.items():
+        print(f"  {region}: {count:,}")
+
+    # Test HuggingFace Dataset creation
+    print("\n🤗 Testing HuggingFace Dataset creation...")
+    try:
+        features = Features({
+            "id": Value("string"),
+            "name": Value("string"),
+            "description": Value("string"),
+            "price": Value("float32"),
+            "category": Value("string"),
+            "region": Value("string"),
+            "image_id": Value("string"),
+        })
+
+        dataset = Dataset.from_pandas(df, features=features, preserve_index=False)
+        print(f"✅ HuggingFace Dataset created successfully")
+        print(f"✅ Dataset info: {dataset}")
+
+        # Sample a few records
+        print("\n📋 Sample records:")
+        for i in range(min(3, len(dataset))):
+            record = dataset[i]
+            print(f"  ID: {record['id']}")
+            print(f"  Name: {record['name'][:50]}...")
+            print(f"  Price: ${record['price']:.2f}")
+            print(f"  Category: {record['category']}")
+            print()
+
+        return True
+
+    except Exception as e:
+        print(f"❌ HuggingFace Dataset creation failed: {e}")
+        return False
+
+if __name__ == "__main__":
+    success = test_dataset()
+
+    if success:
+        print("🎉 All tests passed! Dataset is ready for upload.")
+        print("\n📤 To upload to HuggingFace Hub:")
+        print("1. pip install datasets huggingface_hub pyarrow")
+        print("2. huggingface-cli login")
+        print("3. python upload_to_hf.py --username YOUR_USERNAME")
+    else:
+        print("❌ Tests failed. Please fix the issues above before uploading.")

upload_to_hf.py

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+#!/usr/bin/env python3
+"""
+Upload the wine text dataset to HuggingFace Hub.
+
+Prerequisites:
+1. Install required packages: pip install datasets huggingface_hub pyarrow
+2. Login to HF Hub: huggingface-cli login
+3. Replace YOUR_USERNAME with your actual HF username
+
+Usage:
+    python upload_to_hf.py --username YOUR_USERNAME [--private]
+"""
+
+import argparse
+import pandas as pd
+from datasets import Dataset, Features, Value
+from huggingface_hub import HfApi
+import os
+
+def create_dataset_features():
+    """Define the dataset schema."""
+    return Features({
+        "id": Value("string"),
+        "name": Value("string"),
+        "description": Value("string"),
+        "price": Value("float32"),
+        "category": Value("string"),
+        "region": Value("string"),
+        "image_id": Value("string"),
+    })
+
+def load_and_prepare_dataset(parquet_file="wine_text_126k.parquet"):
+    """Load the parquet file and create a HuggingFace Dataset."""
+
+    print(f"Loading dataset from {parquet_file}...")
+    df = pd.read_parquet(parquet_file)
+
+    # Verify data quality
+    print(f"Dataset shape: {df.shape}")
+    print(f"Columns: {list(df.columns)}")
+
+    # Check for any missing values
+    missing = df.isnull().sum()
+    if missing.any():
+        print("Warning: Missing values found:")
+        print(missing[missing > 0])
+    else:
+        print("✅ No missing values found")
+
+    # Create HuggingFace Dataset with proper features
+    features = create_dataset_features()
+    dataset = Dataset.from_pandas(df, features=features, preserve_index=False)
+
+    print(f"✅ Created HuggingFace Dataset with {len(dataset):,} records")
+    return dataset
+
+def upload_dataset(dataset, username, repo_name="wine-text-126k", private=False):
+    """Upload the dataset to HuggingFace Hub."""
+
+    full_repo_name = f"{username}/{repo_name}"
+
+    print(f"\n🚀 Uploading to HuggingFace Hub: {full_repo_name}")
+    print(f"Privacy: {'Private' if private else 'Public'}")
+
+    try:
+        # Create the repository (this will fail gracefully if it already exists)
+        api = HfApi()
+        try:
+            api.create_repo(
+                repo_id=full_repo_name,
+                repo_type="dataset",
+                private=private
+            )
+            print(f"✅ Created repository: {full_repo_name}")
+        except Exception as e:
+            if "already exists" in str(e).lower():
+                print(f"ℹ️  Repository already exists: {full_repo_name}")
+            else:
+                print(f"⚠️  Repository creation warning: {e}")
+
+        # Upload the dataset
+        print("📤 Uploading dataset files...")
+        dataset.push_to_hub(
+            full_repo_name,
+            private=private,
+            commit_message="Initial upload of wine text dataset"
+        )
+
+        print(f"\n🎉 Upload completed successfully!")
+        print(f"🔗 Dataset URL: https://huggingface.co/datasets/{full_repo_name}")
+        print(f"\n📖 Usage:")
+        print(f'from datasets import load_dataset')
+        print(f'dataset = load_dataset("{full_repo_name}")')
+
+        return True
+
+    except Exception as e:
+        print(f"❌ Upload failed: {e}")
+        print("\nTroubleshooting:")
+        print("1. Make sure you're logged in: huggingface-cli login")
+        print("2. Check your internet connection")
+        print("3. Verify the username is correct")
+        print("4. Ensure you have write permissions")
+        return False
+
+def main():
+    parser = argparse.ArgumentParser(description="Upload wine dataset to HuggingFace Hub")
+    parser.add_argument("--username", required=True, help="Your HuggingFace username")
+    parser.add_argument("--private", action="store_true", help="Make the dataset private")
+    parser.add_argument("--parquet-file", default="wine_text_126k.parquet",
+                       help="Path to the parquet file")
+
+    args = parser.parse_args()
+
+    # Check if parquet file exists
+    if not os.path.exists(args.parquet_file):
+        print(f"❌ Error: Parquet file not found: {args.parquet_file}")
+        print("Make sure you're running this script from the wine-text-126k directory")
+        return
+
+    print("🍷 Wine Text Dataset - HuggingFace Upload")
+    print("=" * 50)
+
+    # Load and prepare dataset
+    dataset = load_and_prepare_dataset(args.parquet_file)
+
+    # Upload to HuggingFace
+    success = upload_dataset(dataset, args.username, private=args.private)
+
+    if success:
+        print("\n✅ All done! Your dataset is now live on HuggingFace Hub.")
+        print("Don't forget to update the README.md with your actual username!")
+    else:
+        print("\n❌ Upload failed. Please check the error messages above.")
+
+if __name__ == "__main__":
+    main()

wine_text_126k.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5eb086a2d7d0783fd1f6c9da78caf92cb0b041fbedb66511895bba9456cdced1
+size 30732050