roberta-base-ner-demo Open-source Model - Free Deployment for Mongolian Named Entity Recognition

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Roberta Base Ner Demo

Developed by Buyandelger

Named Entity Recognition (NER) model fine-tuned based on the Mongolian RoBERTa-base model

Sequence Labeling

Transformers

Other#Mongolian Named Entity Recognition #High F1 Score #RoBERTa Fine-tuning

Downloads 15

Release Time : 7/1/2022

Model Overview

This model is a fine-tuned version of Mongolian RoBERTa-base for NER tasks, used to identify named entities in Mongolian text

Model Features

Dedicated Mongolian Model

Fine-tuned based on the Mongolian pre-trained RoBERTa model, specifically designed for processing Mongolian text

High-Performance NER

Achieves an F1 score of 0.8876 on the evaluation set, demonstrating excellent performance

Lightweight Fine-tuning

Only requires 1 training epoch to achieve good results

Model Capabilities

Mongolian Text Processing

Named Entity Recognition

Sequence Labeling

Use Cases

Text Analysis

Mongolian News Entity Extraction

Identify entities such as people, places, and organizations from Mongolian news

Mongolian Document Annotation

Automatically annotate key entity information in Mongolian documents

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0336	1.0	477	0.0771	0.8802	0.8951	0.8876	0.9798

Property	Details
Language	Mongolian
Tags	generated_from_trainer
Metrics	precision, recall, f1, accuracy
Model Name	roberta - base - ner - demo

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Roberta Base Ner Demo

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