| # English-Yoruba STEM Translation Model | |
| This model is trained to translate English STEM content to Yoruba. | |
| ## Model Details | |
| - **Architecture:** Transformer-based sequence-to-sequence model | |
| - **Base Model:** Davlan/mt5-base-en-yor-mt | |
| - **Training Data:** YorubaSTEM1.0 | |
| - **Performance:** BLEU: 36.08 | |
| ## Usage | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| tokenizer = AutoTokenizer.from_pretrained("gbelewade/YorubaSTEMt5") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("gbelewade/YorubaSTEMt5") | |
| # Translate English text to Yoruba | |
| english_text = "The chemical formula for water is H2O." | |
| inputs = tokenizer(english_text, return_tensors="pt") | |
| outputs = model.generate(**inputs) | |
| yoruba_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print(yoruba_text) | |
| ## Limitations | |
| [Describe any known limitations of the model] | |
| ## Citation | |