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refactor: include province and annotation pipeline

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bernard-ng
2025-07-24 12:50:30 +02:00
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README.md
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@@ -12,28 +12,44 @@ Experiments conducted on custom evaluation sets, including multilingual and code
This work demonstrates the importance of culturally grounded resources in reducing bias and improving performance in NLP systems applied to underrepresented regions. Our findings open new directions for inclusive language technologies in African contexts and contribute a valuable resource for future research in regional linguistics, onomastics, and identity-aware artificial intelligence.
# Usage
## Installation
```bash
git clone https://github.com/bernard-ng/drc-ners-nlp.git
cd drc-ners-nlp
python3 -m venv .venv
source .venv/bin/activate
cp .env .env.local
pip install -r requirements.txt
```
## Gender Inference
### 1. Dataset Preparation
## Dataset
### Preparation
| Name | Description | Default |
|------------------|--------------------------------------------------------------------|---------|
| --split_eval | Split into evaluation and featured datasets | True |
| --no-split_eval | Do not split into evaluation and featured datasets | |
| --split_by_sex | Split by sex into male/female datasets | True |
| --no-split_by_sex| Do not split by sex into male/female datasets | |
```bash
python -m processing.gender.prepare
python -m processing.annotation.prepare
python -m processing.prepare --split_eval --split_by_sex
```
### 2. Training
Arguments:
### Annotation
| Name | Description | Default |
|-------------|-----------------------------------------------------|----------------|
| --llm_model | Ollama model name to use | llama3.2:3b |
Example:
```bash
python -m processing.annotate --llm_model=mistral7b
```
## Experiments
### Training
| Name | Description | Default |
|----------------|--------------------------------------------------|--------------------|
| --dataset | Path to the dataset file | names_featured.csv |
@@ -50,22 +66,18 @@ Arguments:
Examples:
```bash
python -m ners.gender.models.lstm --size 1000000 --save
python -m ners.gender.models.logreg --size 1000000 --save
python -m ners.gender.models.transformer --size 1000000 --save
python -m pipelilne.gender.models.lstm --size 1000000 --save
python -m pipelilne.gender.models.logreg --size 1000000 --save
python -m pipelilne.gender.models.transformer --size 1000000 --save
```
```bash
python -m ners.gender.models.lstm --size 1000000 --balanced --save
python -m ners.gender.models.logreg --size 1000000 --balanced --save
python -m ners.gender.models.transformer --size 1000000 --balanced --save
python -m pipelilne.gender.models.lstm --size 1000000 --balanced --save
python -m pipelilne.gender.models.logreg --size 1000000 --balanced --save
python -m pipelilne.gender.models.transformer --size 1000000 --balanced --save
```
### 3. Evaluation
Arguments:
### Evaluation
| Name | Description | Default |
|------------|-----------------------------------------------|----------------------|
| --model | Model type: logreg, lstm, or transformer | (required) |
@@ -77,15 +89,12 @@ Arguments:
Examples:
```bash
python -m ners.gender.eval --dataset names_evaluations.csv --model logreg
python -m ners.gender.eval --dataset names_evaluations.csv --model lstm
python -m ners.gender.eval --dataset names_evaluations.csv --model transformer
python -m pipelilne.gender.eval --dataset names_evaluations.csv --model logreg
python -m pipelilne.gender.eval --dataset names_evaluations.csv --model lstm
python -m pipelilne.gender.eval --dataset names_evaluations.csv --model transformer
```
### 4. Inference
Arguments:
### Inference
| Name | Description | Default |
|-------------|------------------------------------------|-----------|
| --model | Model type: logreg, lstm, or transformer | (required)|
@@ -95,7 +104,7 @@ Arguments:
Examples:
```bash
python -m ners.gender.predict --model logreg --names "Tshisekedi"
python -m ners.gender.predict --model lstm --names "Ilunga Ngandu"
python -m ners.gender.predict --model transformer --names "musenga wa musenga"
python -m pipelilne.gender.predict --model logreg --names "Tshisekedi"
python -m pipelilne.gender.predict --model lstm --names "Ilunga Ngandu"
python -m pipelilne.gender.predict --model transformer --names "musenga wa musenga"
```