readme: ---
  language: Yorùbá
  datasets:
  - AI4D-Africa - Yorùbá Machine Translation Challenge
  tags:
  - text
  - machine-translation
  - language-translation
  - seq2seq
  - helsinki-nlp
  license: apache-2.0
  metrics:
  - ROUGE
  ---
  ## Predicting English Translation
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  # Loading tokenizer and model
  tokenizer = AutoTokenizer.from_pretrained("kingabzpro/Helsinki-NLP-opus-yor-mul-en")
  model = AutoModelForSeq2SeqLM.from_pretrained("kingabzpro/Helsinki-NLP-opus-yor-mul-en").to('cuda')
  
  # Prediction
  a = model.generate(**tokenizer.prepare_seq2seq_batch('Nínú ìpè kan lẹ́yìn ìgbà náà, wọ́n sọ fún aṣojú iléeṣẹ́ BlaBlaCar pé ètò náà ti yí padà, pé',return_tensors='pt').to('cuda'))
  text = tokenizer.batch_decode(a)
  
  # Cleaning text
  text = str(text)
  text = re.sub("<pad> ","",text)
  text = re.sub("'","",text)
  text = text.replace("[", "")
  text = text.replace("]", "")
  text
  ```
  ## Result
  ```
  'In a statement after that hearing, the BualaCard’s representative was told that the event had changed, that he had turned up.'
  ```
  ## ROGUE Score
  **0.3025**
  