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

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

git clone https://github.com/bernard-ng/drc-ners-nlp.git
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. step are defined in the drc-ners-nlp/processing/steps directory. and configuration to enable them is managed through the drc-ners-nlp/config/pipeline.yaml file.

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. models are defined in the drc-ners-nlp/research/models directory. you can define model features, training parameters, and evaluation metrics in the 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

Contributors

Acknowledgements

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

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