LocaleNLP/eng_wolof

localenlp-en-wol

Fine-tuned MarianMT model for English-to-Wolof translation.

Model Card for LOCALENLP/english-wolof

This is a machine translation model for English → Wolof, developed by the LOCALENLP organization.
It is based on the pretrained Helsinki-NLP/opus-mt-en-mul MarianMT model and fine-tuned on a custom parallel corpus of ~84k sentence pairs.

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Model Details

Model Description

• Developed by: LOCALENLP
• Funded by [optional]: N/A
• Shared by: LOCALENLP
• Model type: Seq2Seq Transformer (MarianMT)
• Languages: English → Wolof
• License: MIT
• Finetuned from model: Helsinki-NLP/opus-mt-en-mul

Model Sources

• Repository: https://huggingface.co/LOCALENLP/english-wolof
• Demo [optional]: To be integrated in Gradio / Web app

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Uses

Direct Use

• Translate English text into Wolof for research, education, and communication.
• Useful for low-resource NLP tasks, digital content creation, and cultural preservation.

Downstream Use

• Can be integrated into translation apps, chatbots, and education platforms.
• Serves as a base for further fine-tuning on domain-specific Wolof corpora.

Out-of-Scope Use

• Suitable for legal and medical translations (e.g., contracts, prescriptions, medical records).
• Mistranslations may occur, like any automated system.
• Review recommended as the model can sometimes mistranslate.

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Bias, Risks, and Limitations

• Training data is from a custom collection of parallel sentences (~84k pairs).
• Some informal or culturally nuanced expressions may not be accurately translated.
• Wolof spelling and grammar variation (Latin script) may lead to inconsistencies.
• Model may underperform on domain-specific or long, complex texts.

Recommendations

• Use human post-editing for high-stakes use cases.
• Evaluate performance on your target domain before deployment.

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How to Get Started with the Model

from transformers import MarianTokenizer, AutoModelForSeq2SeqLM

model_name = "LOCALENLP/english-wolof"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

text = "Good evening, how was your day?"
inputs = tokenizer(">>wol<< " + text, return_tensors="pt", padding=True, truncation=True)
outputs = model.generate(**inputs, max_length=512, num_beams=4)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("English:", text)
print("Wolof:", translation)