A Culturally-Aware NLP System for Congolese Name Analysis and Gender Inference

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Despite the growing success of gender inference models in Natural Language Processing (NLP), these tools often underperform when applied to culturally diverse African contexts due to the lack of culturally-representative training data. This project introduces a comprehensive pipeline for Congolese name analysis with a large-scale dataset of over 5 million names from the Democratic Republic of Congo (DRC) annotated with gender and demographic metadata.

Getting Started

Installation & Setup

download the dataset, if you need access please reach us at [email protected].

git clone https://github.com/bernard-ng/drc-ners-nlp.git

mkdir -p drc-ners-nlp/data/dataset
cp names.csv drc-ners-nlp/data/dataset

cd drc-ners-nlp

Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

uv sync

Macos & windows

docker compose build
docker compose exec app bash

Data Processing

This project includes a robust data processing pipeline designed to handle large datasets efficiently with batching, checkpointing, and parallel processing capabilities.

Pipeline Configuration

stages:
  - "data_cleaning"
  - "data_selection"
  - "feature_extraction"
  - "data_splitting"

Running the Pipeline

uv run ners pipeline run --env="production"

Experiments

This project provides a modular experiment (model training and evaluation) framework for systematic model comparison and research iteration. you can define model features, training parameters, and evaluation metrics in the config/research_templates.yaml file.

Running Experiments

# bigru
uv run ners research train --name="bigru" --type="baseline" --env="production"
uv run ners research train --name="bigru_native" --type="baseline" --env="production"
uv run ners research train --name="bigru_surname" --type="baseline" --env="production"

# cnn
uv run ners research train --name="cnn" --type="baseline" --env="production"
uv run ners research train --name="cnn_native" --type="baseline" --env="production"
uv run ners research train --name="cnn_surname" --type="baseline" --env="production"

# lightgbm
uv run ners research train --name="lightgbm" --type="baseline" --env="production"
uv run ners research train --name="lightgbm_native" --type="baseline" --env="production"
uv run ners research train --name="lightgbm_surname" --type="baseline" --env="production"

# logistic regression
uv run ners research train --name="logistic_regression" --type="baseline" --env="production"
uv run ners research train --name="logistic_regression_native" --type="baseline" --env="production"
uv run ners research train --name="logistic_regression_surname" --type="baseline" --env="production"

# lstm
uv run ners research train --name="lstm" --type="baseline" --env="production"
uv run ners research train --name="lstm_native" --type="baseline" --env="production"
uv run ners research train --name="lstm_surname" --type="baseline" --env="production"

# random forest
uv run ners research train --name="random_forest" --type="baseline" --env="production"
uv run ners research train --name="random_forest_native" --type="baseline" --env="production"
uv run ners research train --name="random_forest_surname" --type="baseline" --env="production"

# naive bayes
uv run ners research train --name="naive_bayes" --type="baseline" --env="production"
uv run ners research train --name="naive_bayes_native" --type="baseline" --env="production"
uv run ners research train --name="naive_bayes_surname" --type="baseline" --env="production"

# transformer
uv run ners research train --name="transformer" --type="baseline" --env="production"
uv run ners research train --name="transformer_native" --type="baseline" --env="production"
uv run ners research train --name="transformer_surname" --type="baseline" --env="production"

# xgboost
uv run ners research train --name="xgboost" --type="baseline" --env="production"
uv run ners research train --name="xgboost_native" --type="baseline" --env="production"
uv run ners research train --name="xgboost_surname" --type="baseline" --env="production"

Web Interface

This project includes a user-friendly web interface built with Streamlit, allowing non-technical users to run experiments and make predictions without needing to understand the underlying code.

Running the Web Interface

uv run ners web run --env="production"

docker compose run --rm --service-ports app ners web run --env=production

then open : http://localhost:8501/

Contributors

Acknowledgements

• Map Visualization: https://data.humdata.org/dataset/anciennes-provinces-rdc-old-provinces-drc