Gliner Large V2.5 Groceries

🚀 Grocery Named Entity Recognition Model

A fine - tuned GLiNER model for identifying grocery items and food categories in text.

This model is designed to precisely identify various grocery items and food categories within text, offering a practical solution for text - related tasks in the grocery domain.

⚠️ Important Note

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Take a look here and try the model in action!

🚀 Quick Start

This is a fine - tuned GLiNER model for identifying grocery items and food categories in text. You can try it out by following the usage example below.

✨ Features

• This model is fine - tuned on the grocery - ner - dataset to identify 14 different categories of grocery items including fruits, vegetables, dairy products, and more.
• It can effectively identify various grocery - related entity types in text.

Supported Entity Types

• Fruits Vegetables
• Lactose, Diary, Eggs, Cheese, Yoghurt
• Meat, Fish, Seafood
• Frozen, Prepared Meals
• Baking, Cooking
• Cereals, Grains, Canned, Seeds
• Breads
• Snacks, Pastries, Treats
• Frozen Desserts
• Hot Drinks, Chilled Drinks
• Alcoholic Drinks
• Spices, Sauces
• World Foods
• Dietary Restrictions, Health, Allergens, Lifestyle

📦 Installation

!pip install gliner

💻 Usage Examples

Basic Usage

from gliner import GLiNER

# Load model
model = GLiNER.from_pretrained("empathyai/gliner_large-v2.5-groceries")

labels = [
    "Fruits Vegetables",
    "Lactose, Diary, Eggs, Cheese, Yoghurt",
    "Meat, Fish, Seafood",
    "Frozen, Prepared Meals",
    "Baking, Cooking",
    "Cereals, Grains, Canned, Seeds",
    "Breads",
    "Snacks, Pastries, Treats",
    "Frozen Desserts",
    "Hot Drinks, Chilled Drinks",
    "Alcoholic Drinks",
    "Spices, Sauces",
    "World Foods",
    "Dietary Restrictions, Health, Allergens, Lifestyle"
  ]

# Example text
text = "I need to buy milk, bread, and fresh apples"

# Get predictions
predictions = model.predict_entities(text, labels=labels)
print(predictions)

📚 Documentation

Model Description

This model is fine - tuned on the grocery - ner - dataset to identify 14 different categories of grocery items including fruits, vegetables, dairy products, and more.

Training Details

🔧 Technical Details

This model is based on the GLiNER framework and is fine - tuned on the grocery - ner - dataset. Through specific training strategies and parameter settings, it can accurately identify 14 different categories of grocery items in text.

📄 License

This model is released under the Apache 2.0 license.

💡 Usage Tip

• This model is optimized for English language text only.
• It performs best on grocery shopping and food - related contexts.
• It may not recognize brand names or regional food items not present in training data.