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Xlm Roberta Base Finetuned Ner Wolof

Developed by mbeukman
A token classification model for Wolof named entity recognition (NER) tasks, fine-tuned on the Wolof portion of the MasakhaNER dataset based on xlm-roberta-base
Downloads 49
Release Time : 3/2/2022

Model Overview

This model is a Transformer-based model for Wolof named entity recognition, specifically designed to identify entities such as person names, locations, organizations, and dates in news texts.

Model Features

African language support
Specially optimized for Wolof, filling the gap in NLP tools for African languages
Transfer learning application
Fine-tuned based on the multilingual pre-trained model xlm-roberta-base, effectively leveraging cross-lingual knowledge
News domain optimization
Trained on news articles, delivering optimal performance in this domain

Model Capabilities

Identify named entities in Wolof texts
Distinguish entity types such as person names, locations, organizations, and dates
Process text content in the news domain

Use Cases

Natural language processing research
African language NLP research
Used to study the linguistic characteristics of Wolof and the effects of cross-lingual transfer learning
Named entity recognition technology validation
Validate the performance of Transformer architecture on low-resource languages
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