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Mbert Multiconer22 Bn
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Mbert Multiconer22 Bn

Developed by sumitrsch
This model is designed for the Bengali track of the SemEval Multiconer task, focusing on natural language processing tasks such as Named Entity Recognition (NER).
Sequence Labeling
Transformers
#Bengali NER#Multilingual Entity Recognition#SemEval Competition
Downloads 39
Release Time : 7/6/2022

Model Overview

This is a Named Entity Recognition model specifically designed for Bengali, intended for the SemEval Multiconer competition task, capable of identifying various named entities in text.

Model Features

Bengali Language Support
Named Entity Recognition capabilities specifically optimized for Bengali.
Competition Optimization
Specially designed and fine-tuned for the SemEval Multiconer competition task.

Model Capabilities

Named Entity Recognition
Text Analysis
Bengali Language Processing

Use Cases

Academic Research
SemEval Competition
Participating in the Bengali track of the SemEval Multiconer competition
Commercial Applications
Bengali Text Analysis
Used for Named Entity Recognition tasks in Bengali text processing
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