Iamjuhwan/Machine-Translation-English---Yoruba

# Machine-Translation-English---Yoruba # 🌍 Fine-tuning NLLB on Menyo-20k This project fine-tunes **Meta’s NLLB (No Language Left Behind)** model using Parameter Efficient Fine-Tuning on the **Menyo-20k** dataset to improve translation quality for African languages. Training was conducted until early stopping to prevent overfitting. --- ## 📘 Overview - **Model:** `facebook/nllb-200-distilled-600M` - **Dataset:** Menyo-20k — parallel corpus for English–African language translation - **Goal:** Enhance NLLB performance on low-resource African languages (Yoruba, Igbo, Hausa, etc.) - **Framework:** Hugging Face Transformers --- ## ⚙️ Training Setup | Setting | Value | |----------|--------| | Batch Size | 8 | | Learning Rate | 1e-4 | | Gradient Accumulation Step| 8 | | Scheduler | Cosine | | Epochs | 50 | | Early Stopping | Patience = 10 | | Environment | Kaggle (T4 GPU x 2) | --- ## 🧩 Method 1. Preprocess and tokenize Menyo-20k using NLLB tokenizer 2. Fine-tune the model using LoRA and with Huggingface Trainer 3. Apply early stopping based on validation loss 4. Evaluate with BLEU and qualitative translation tests ---