---
library_name: transformers
license: mit
datasets:
- galsenai/centralized_wolof_french_translation_data
language:
- wo
- fr
base_model:
- facebook/nllb-200-distilled-600M
pipeline_tag: translation
---

# 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 (fr_Latn) ↔️ Wolof (wol_Latn)
- **License:** CC-BY-NC-4.0
- **Finetuned from model:** facebook/nllb-200-distilled-600M

### Model Sources
- **Repository:** [Hugging Face - Lahad/nllb200-francais-wolof](https://huggingface.co/Lahad/nllb200-francais-wolof)
- **GitHub:** [Fine-tuning NLLB-200 for French-Wolof](https://github.com/LahadMbacke/Fine-tuning_facebook-nllb-200-distilled-600M_French_to_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. Language Variety Limitations:
   - Limited coverage of regional Wolof dialects
   - May not handle cultural nuances effectively
   
2. Technical Limitations:
   - Maximum context window of 128 tokens
   - Reduced performance on technical/specialized content
   - May struggle with informal language and slang

3. Potential Biases:
   - Training data may reflect cultural biases
   - 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

```python
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/centralized_wolof_french_translation_data
- **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: fr_Latn/wol_Latn

#### 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.