---
language:
  - lug
  - sw
  - en
license: other
tags:
  - translation
  - anti-money-laundering
  - luganda
  - swahili
  - nllb
  - east-africa
base_model: facebook/nllb-200-distilled-600M
datasets:
  - darthvader256/Simivalleyaml
---

# Simitech AML AfriNLLB Translator

Fine-tuned from `facebook/nllb-200-distilled-600M` on East African AML transaction narratives.
Specialized for translating Luganda (`lug_Latn`) and Swahili (`swh_Latn`) mobile money
transaction descriptions to English for downstream AML classification.

## Why a specialized translator?

General NLLB models miss domain-specific AML vocabulary:
- Mobile money agent terminology (float, airtime, USSD codes)
- Ugandan colloquialisms used in social engineering scams
- Financial crime typology phrases specific to EAC corridor

## Usage

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "darthvader256/simitech-aml-afrinllb-translator"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

tokenizer.src_lang = "lug_Latn"
inputs = tokenizer("nkusaba ssente z'omusawo omukisa", return_tensors="pt")
output = model.generate(
    **inputs,
    forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"],
    max_new_tokens=128,
)
print(tokenizer.decode(output[0], skip_special_tokens=True))
# → "I am asking for doctor money, please"
```

## Source

`decision-plane/app/training/nlp_finetune.py` — `AfriNLLBTranslator` class