Lahad/nllb200-francais-wolof

Model Card: NLLB-200 French-Wolof(🇫🇷↔️🇸🇳) Translation Model

Model Details

Model Description

A fine-tuned version of Meta's NLLB-200 (600M distilled) model specialized for French to Wolof translation. This model was trained to improve accessibility of content between French and Wolof languages.

• —Developed by: Lahad
• —Model type: Sequence-to-Sequence Translation Model
• —Language(s): French (frLatn) ↔️ Wolof (wolLatn)
• —License: CC-BY-NC-4.0
• —Finetuned from model: facebook/nllb-200-distilled-600M

Model Sources

• —Repository: Hugging Face - Lahad/nllb200-francais-wolof
• —GitHub: Fine-tuning NLLB-200 for French-Wolof

Uses

Direct Use

• —Text translation between French and Wolof
• —Content localization
• —Language learning assistance
• —Cross-cultural communication

Out-of-Scope Use

• —Commercial use without proper licensing
• —Translation of highly technical or specialized content
• —Legal or medical document translation where professional human translation is required
• —Real-time speech translation

Bias, Risks, and Limitations

1. 1.Language Variety Limitations:
2. 2.Limited coverage of regional Wolof dialects
3. 3.May not handle cultural nuances effectively

1. 1.Technical Limitations:
2. 2.Maximum context window of 128 tokens
3. 3.Reduced performance on technical/specialized content
4. 4.May struggle with informal language and slang

1. 1.Potential Biases:
2. 2.Training data may reflect cultural biases
3. 3.May perform better on standard/formal language

Recommendations

• —Use for general communication and content translation
• —Verify translations for critical communications
• —Consider regional language variations
• —Implement human review for sensitive content
• —Test translations in intended context before deployment

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Lahad/nllb200-francais-wolof")
model = AutoModelForSeq2SeqLM.from_pretrained("Lahad/nllb200-francais-wolof")

# Translation function
def translate(text, max_length=128):
    inputs = tokenizer(
        text,
        max_length=max_length,
        padding="max_length",
        truncation=True,
        return_tensors="pt"
    )
    
    outputs = model.generate(
        input_ids=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        forced_bos_token_id=tokenizer.convert_tokens_to_ids("wol_Latn"),
        max_length=max_length
    )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

Training Details

Training Data

• —Dataset: galsenai/centralizedwoloffrenchtranslationdata
• —Split: 80% training, 20% testing
• —Format: JSON pairs of French and Wolof translations

Training Procedure

Preprocessing

• —Dynamic tokenization with padding
• —Maximum sequence length: 128 tokens
• —Source/target language tags: frLatn/wolLatn

Training Hyperparameters

• —Learning rate: 2e-5
• —Batch size: 8 per device
• —Training epochs: 3
• —FP16 training: Enabled
• —Evaluation strategy: Per epoch

Evaluation

Testing Data, Factors & Metrics

• —Testing Data: 20% of dataset
• —Metrics:
• —Cloud Provider:
• —Evaluation Factors:
• —Translation accuracy
• —Semantic preservation
• —Grammar correctness

Environmental Impact

• —Hardware Type: NVIDIA T4 GPU
• —Hours used: 5
• —Cloud Provider: [Not Specified]
• —Compute Region: [Not Specified]
• —Carbon Emitted: [Not Calculated]

Technical Specifications

Model Architecture and Objective

• —Architecture: NLLB-200 (Distilled 600M version)
• —Objective: Neural Machine Translation
• —Parameters: 600M
• —Context Window: 128 tokens

Compute Infrastructure

• —Training Hardware: NVIDIA T4 GPU
• —Training Time: 5 hours
• —Software Framework: Hugging Face Transformers

Model Card Contact

For questions about this model, please create an issue on the model's Hugging Face repository.