| --- | |
| Language Pair Finetuned: | |
| - en-mr | |
| Metrics: | |
| - sacrebleu | |
| - WAT 2021: 16.11 | |
| # mbart-large-finetuned-en-mr | |
| ## Model Description | |
| This is the mbart-large-50 model finetuned on En-Mr corpus. | |
| ## Intended uses and limitations | |
| Mostly useful for English to Marathi translation but the mbart-large-50 model also supports other language pairs | |
| ### How to use | |
| ```python | |
| from transformers import MBartForConditionalGeneration, MBart50TokenizerFast | |
| model = MBartForConditionalGeneration.from_pretrained("shivam/mbart-large-50-finetuned-en-mr") | |
| tokenizer = MBart50TokenizerFast.from_pretrained("shivam/mbart-large-50-finetuned-en-mr", src_lang="en_XX", tgt_lang="mr_IN") | |
| english_input_sentence = "The Prime Minister said that cleanliness, or Swachhta, is one of the most important aspects of preventive healthcare." | |
| model_inputs = tokenizer(english_input_sentence, return_tensors="pt") | |
| generated_tokens = model.generate( | |
| **model_inputs, | |
| forced_bos_token_id=tokenizer.lang_code_to_id["mr_IN"] | |
| ) | |
| marathi_output_sentence = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) | |
| print(marathi_output_sentence) | |
| #स्वच्छता हा प्रतिबंधात्मक आरोग्य सेवेतील सर्वात महत्त्वाचा पैलू आहे, असे पंतप्रधान म्हणाले. | |
| ``` | |
| #### Limitations | |
| The model was trained on Google Colab and as the training takes a lot of time the model was trained for small time and small number of epochs. | |
| ## Eval results | |
| WAT 2021: 16.11 |