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Distilbert Base Uncased Squad2 With Ner Mit Restaurant With Neg With Repeat

Developed by andi611
This model is based on the DistilBERT architecture, fine-tuned on the SQuAD2 and MIT Restaurant datasets, supporting both question answering and named entity recognition tasks.
Downloads 19
Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the squad_v2 and mit_restaurant datasets, primarily used for question answering and named entity recognition tasks.

Model Features

Lightweight Model
Based on the DistilBERT architecture, it is more lightweight than the original BERT model, offering faster inference speed.
Multi-task Support
Supports both question answering and named entity recognition tasks, suitable for various NLP application scenarios.
Domain-specific Optimization
Fine-tuned on the MIT Restaurant dataset, it performs well in named entity recognition for the catering domain.

Model Capabilities

Question Answering System
Named Entity Recognition
Text Classification

Use Cases

Intelligent Customer Service
Catering Q&A System
Used in intelligent customer service systems to answer user questions about restaurant menus, business hours, etc.
Information Extraction
Restaurant Review Analysis
Extracts key information such as dish names and prices from restaurant reviews.
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