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Distilbert Base Uncased Ner Mit Restaurant
This model is a Named Entity Recognition (NER) model fine-tuned on the MIT Restaurant dataset based on DistilBERT, specifically designed for entity recognition tasks in the restaurant domain.
Downloads 15
Release Time : 3/2/2022
Model Overview
This is a lightweight BERT model optimized for named entity recognition in restaurant reviews, capable of identifying menu items, prices, locations, and other restaurant-related entities.
Model Features
Efficient and Lightweight
Based on the DistilBERT architecture, it is 40% smaller and 60% faster than standard BERT while maintaining 97% of its performance.
Domain-Specific
Fine-tuned specifically for restaurant review data, optimized for recognizing food, prices, and other restaurant-related entities.
High Performance
Achieves an F1 score of 0.7988 and an accuracy of 0.9119 on the MIT Restaurant test set.
Model Capabilities
Named entity recognition in restaurant reviews
Food name recognition
Price recognition
Location information extraction
Use Cases
Restaurant Industry Analysis
Menu Item Analysis
Automatically extract mentioned dish names from online reviews
Can identify over 90% of dish mentions
Price Monitoring
Extract price information from reviews for competitive analysis
Accurately identifies over 85% of price mentions
Customer Feedback Analysis
Service Evaluation Analysis
Identify entities such as service staff and wait times mentioned in reviews
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