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Distilbert Base Multi Cased Ner

Developed by vgorce
This is a multilingual named entity recognition (NER) model based on DistilBERT, supporting 10 languages, and quantized for compatibility with Transformers.js.
Downloads 16
Release Time : 11/29/2023

Model Overview

This model is a multilingual variant of DistilBERT, specifically designed for named entity recognition tasks. After quantization, the model size is smaller, making it suitable for use in browser environments.

Model Features

Multilingual Support
Supports named entity recognition in 10 languages
Quantization
Quantized with 02 quantization, resulting in a smaller model size suitable for front-end deployment
Transformers.js Compatibility
Can be used directly in browser environments
Based on DistilBERT
Uses the lightweight DistilBERT architecture, maintaining performance while reducing computational resource requirements

Model Capabilities

Text Entity Recognition
Multilingual Processing
Browser-side Inference

Use Cases

Information Extraction
Multilingual Document Analysis
Extracts entity information such as person names, locations, and organization names from multilingual documents
Accurately identifies named entities in 10 languages
Content Classification
News Classification
Classifies news content based on entity information
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