Bambara FastText Embeddings

Model Description

This model provides FastText word embeddings for the Bambara language (Bamanankan), a Mande language spoken primarily in Mali. The embeddings capture semantic relationships between Bambara words and enable various NLP tasks for this low-resource African language.

Model Type: FastText Word Embeddings
Language: Bambara (bm)
License: Apache 2.0

Model Details

Model Architecture

Algorithm: FastText with subword information
Vector Dimension: 300
Vocabulary Size: 9,973 unique Bambara words
Training Method: Skip-gram with negative sampling
Subword Information: Character n-grams (enables handling of out-of-vocabulary words)

Training Data

The model was trained on Bambara text corpora, building upon the work of David Ifeoluwa Adelani's research on African language embeddings.

Intended Use

This model is designed for:

Semantic similarity tasks in Bambara
Information retrieval for Bambara documents
Cross-lingual research involving Bambara
Cultural preservation and digital humanities projects
Educational applications for Bambara language learning
Foundation for downstream NLP tasks in Bambara

Usage

  Coming soon