177 lines
6.0 KiB
Markdown
177 lines
6.0 KiB
Markdown
# 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
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underperform when applied to culturally diverse African contexts due to the lack of culturally-representative training
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data.
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This project introduces a comprehensive pipeline for Congolese name analysis with a large-scale dataset of over 5
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million names from the Democratic Republic of Congo (DRC) annotated with gender and demographic metadata.
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## Getting Started
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### Installation & Setup
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Instructions and command line snippets bellow are provided to help you set up the project environment quickly and
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efficiently.
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assuming you have Python 3.11 and Git installed and working on a Unix-like system (Linux, macOS, etc.).
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**Using Makefile (Recommended)**
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```bash
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git clone https://github.com/bernard-ng/drc-ners-nlp.git
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cd drc-ners-nlp
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# Setup environment
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make setup
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make activate
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```
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**Manual Setup**
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```bash
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git clone https://github.com/bernard-ng/drc-ners-nlp.git
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cd drc-ners-nlp
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# Setup environment
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python -m venv .venv
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.venv/bin/pip install --upgrade pip
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.venv/bin/pip install -r requirements.txt
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pip install --upgrade pip
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pip install -r requirements.txt
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pip install jupyter notebook ipykernel pytest black flake8 mypy
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source .venv/bin/activate
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```
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## Data Processing
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This project includes a robust data processing pipeline designed to handle large datasets efficiently with batching,
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checkpointing, and parallel processing capabilities.
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step are defined in the `drc-ners-nlp/processing/steps` directory. and configuration to enable them is managed through
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the `drc-ners-nlp/config/pipeline.yaml` file.
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**Pipeline Configuration**
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```yaml
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stages:
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- "data_cleaning"
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- "feature_extraction"
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- "data_splitting"
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```
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**Running the Pipeline**
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```bash
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python main.py --env production
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```
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## NER Processing (Optional)
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This project implements a custom named entity recognition (NER) pipeline tailored for Congolese names.
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Its main objective is to accurately identify and tag the different components of a Congolese name,
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specifically distinguishing between the native part and the surname.
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```bash
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python ner.py --env production
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```
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Once you've built and train the NER model you can use it to annotate **COMPOSE** name in the original dataset
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**Running the Pipeline with NER Annotation**
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```yaml
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stages:
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- "data_cleaning"
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- "feature_extraction"
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- "ner_annotation"
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- "data_splitting"
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```
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**Running the Pipeline with LLM Annotation**
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```yaml
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stages:
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- "data_cleaning"
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- "feature_extraction"
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- "llm_annotation"
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- "data_splitting"
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```
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## Experiments
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This project provides a modular experiment (model training and evaluation) framework for systematic model comparison and
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research iteration. models are defined in the `drc-ners-nlp/research/models` directory.
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you can define model features, training parameters, and evaluation metrics in the `research_templates.yaml` file.
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**Running Experiments**
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```bash
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# bigru
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python train.py --name="bigru" --type="baseline" --env="production"
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python train.py --name="bigru_native" --type="baseline" --env="production"
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python train.py --name="bigru_surname" --type="baseline" --env="production"
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# cnn
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python train.py --name="cnn" --type="baseline" --env="production"
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python train.py --name="cnn_native" --type="baseline" --env="production"
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python train.py --name="cnn_surname" --type="baseline" --env="production"
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# lightgbm
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python train.py --name="lightgbm" --type="baseline" --env="production"
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python train.py --name="lightgbm_native" --type="baseline" --env="production"
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python train.py --name="lightgbm_surname" --type="baseline" --env="production"
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# logistic regression
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python train.py --name="logistic_regression" --type="baseline" --env="production"
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python train.py --name="logistic_regression_native" --type="baseline" --env="production"
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python train.py --name="logistic_regression_surname" --type="baseline" --env="production"
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# lstm
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python train.py --name="lstm" --type="baseline" --env="production"
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python train.py --name="lstm_native" --type="baseline" --env="production"
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python train.py --name="lstm_surname" --type="baseline" --env="production"
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# random forest
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python train.py --name="random_forest" --type="baseline" --env="production"
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python train.py --name="random_forest_native" --type="baseline" --env="production"
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python train.py --name="random_forest_surname" --type="baseline" --env="production"
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# svm
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python train.py --name="svm" --type="baseline" --env="production"
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python train.py --name="svm_native" --type="baseline" --env="production"
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python train.py --name="svm_surname" --type="baseline" --env="production"
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# naive bayes
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python train.py --name="naive_bayes" --type="baseline" --env="production"
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python train.py --name="naive_bayes_native" --type="baseline" --env="production"
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python train.py --name="naive_bayes_surname" --type="baseline" --env="production"
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# transformer
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python train.py --name="transformer" --type="baseline" --env="production"
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python train.py --name="transformer_native" --type="baseline" --env="production"
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python train.py --name="transformer_surname" --type="baseline" --env="production"
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# xgboost
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python train.py --name="xgboost" --type="baseline" --env="production"
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python train.py --name="xgboost_native" --type="baseline" --env="production"
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python train.py --name="xgboost_surname" --type="baseline" --env="production"
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```
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## Web Interface
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This project includes a user-friendly web interface built with Streamlit, allowing non-technical users to run
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experiments and make predictions without needing to understand the underlying code.
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### Running the Web Interface
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```bash
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streamlit run web/app.py
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```
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## Contributors
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<a href="https://github.com/bernard-ng/drc-ners-nlp/graphs/contributors" title="show all contributors">
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<img src="https://contrib.rocks/image?repo=bernard-ng/drc-ners-nlp" alt="contributors"/>
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</a>
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## Acknowledgements
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- Map Visualization: [https://data.humdata.org/dataset/anciennes-provinces-rdc-old-provinces-drc](https://data.humdata.org/dataset/anciennes-provinces-rdc-old-provinces-drc)
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