S

Serengeti E250

Developed by UBC-NLP
SERENGETI is a large-scale multilingual pre-trained model covering 517 African languages and dialects, focusing on bridging the gap in technological resources for African languages.
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Release Time : 10/17/2023

Model Overview

This model is a multilingual pre-trained language model (mPLM) designed to support various natural language understanding tasks for African languages, enhancing the ability of African communities to access information in their native languages.

Model Features

Extensive Language Coverage
Covers 517 African languages and dialects, making it the largest multilingual model in the African NLP field to date.
Afrocentric Design
Adheres to Afrocentric NLP principles, prioritizing the needs of African communities and supporting language users and researchers.
Superior Multi-Task Performance
Achieves state-of-the-art performance on 11 datasets across eight natural language understanding tasks, with an average F1 score of 82.27.

Model Capabilities

Masked Language Modeling
Multilingual Text Understanding
African Language Support

Use Cases

Language Technology
Access to Information in African Languages
Helps non-proficient speakers of other languages access critical information in their native languages.
Enhances global connectivity for African communities.
Language Preservation
Provides opportunities to preserve various African languages, promoting their continued use across multiple domains.
African languages used in NLP tasks for the first time may inspire further technological development.
Academic Research
Linguistic Research
Supports researchers such as anthropologists and linguists in studying African languages.
Offers rich linguistic data and model support.
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