Create README.md

README.md

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+---
+license: mit
+language:
+- luo
+- fr
+metrics:
+- bleu
+base_model:
+- facebook/nllb-200-distilled-600M
+pipeline_tag: translation
+---
+Here's a model card/README for the provided script, which fine-tunes the NLLB model for translating between Luo and Swahili:
+
+---
+
+# Luo-Swahili Machine Translation Model (NLLB-based)
+
+## Model Details
+
+- **Model Name**: `nllb-luo-swa-mt-v1`
+- **Base Model**: `facebook/nllb-200-distilled-600M`
+- **Language Pair**: Luo (`luo`) to Swahili (`swa`)
+- **Dataset**: `SalomonMetre13/luo_swa_arXiv_2501.11003`
+
+## Description
+
+This model is fine-tuned for translating text from Luo to Swahili using the NLLB-200 model architecture. The fine-tuning process involves extending the tokenizer's vocabulary with custom language tokens and training the model on a specific dataset.
+
+## Features
+
+- **Custom Tokenizer**: Extended with special tokens for Luo and Swahili.
+- **Training**: Fine-tuned on a dataset specifically curated for Luo-Swahili translation.
+- **Evaluation**: Uses BLEU score for performance evaluation.
+- **Inference**: Capable of translating new sentences and batches of text.
+
+## Usage
+
+### Installation
+
+Ensure you have the necessary libraries installed:
+
+```bash
+pip install datasets transformers sacrebleu huggingface_hub accelerate torch
+```
+
+### Fine-Tuning
+
+1. **Authentication**: Log in to Hugging Face to access the model and dataset.
+2. **Preprocessing**: The dataset is preprocessed to include special language tokens.
+3. **Training**: The model is fine-tuned using the `Seq2SeqTrainer` with specified training arguments.
+4. **Evaluation**: The model's performance is evaluated using the BLEU metric.
+
+### Inference
+
+- **Translate Single Sentence**: Use the `translate_custom_sentence` function to translate individual sentences.
+- **Translate Batch**: Use the `translate_batch` function for batch translation.
+
+## Performance
+
+The model's performance is evaluated using the BLEU score on the test set. The BLEU score provides an indication of the translation quality.
+
+## Limitations
+
+- The model is trained with a maximum input length of 512 tokens, which may limit its effectiveness on longer texts.
+- The dataset used for fine-tuning may influence the model's performance on specific domains or styles of text.
+
+## Future Work
+
+- Explore fine-tuning on additional datasets to improve robustness.
+- Experiment with different training parameters and architectures to enhance performance.
+
+## Contact
+
+For questions or feedback, please contact [Your Contact Information].
+
+---
+
+This README provides a comprehensive overview of the model, its features, usage instructions, performance metrics, and limitations. It serves as a guide for users who wish to fine-tune or use the model for Luo-Swahili translation tasks.