(input_query: str)
| 160 | |
| 161 | |
| 162 | def MT(input_query: str): |
| 163 | model_name = "facebook/nllb-200-distilled-600M" |
| 164 | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| 165 | model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
| 166 | input_ids = tokenizer(input_query, return_tensors="pt") |
| 167 | outputs = model.generate( |
| 168 | **input_ids, |
| 169 | forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"], |
| 170 | ) |
| 171 | output = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] |
| 172 | return output |
| 173 | |
| 174 | |
| 175 | """ |