readme: ---
  license: mit
  ---
  
  [Korean BART](https://huggingface.co/hyunwoongko/kobart) model for paraphrasing. 
  The dataset utilized can be found on the *Files and versions* tab under the name dataset.csv.
  
  ```python
  import torch
  from transformers import BartForConditionalGeneration, AutoTokenizer
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  model = BartForConditionalGeneration.from_pretrained('guialfaro/korean-paraphrasing').to(device)
  tokenizer = AutoTokenizer.from_pretrained('guialfaro/korean-paraphrasing')
  
  sentence = "7층 방문을 위해 방문록 작성이 필요합니다."
  text =  f"paraphrase: {sentence} "
  
  encoding = tokenizer.batch_encode_plus(
              [text],
              max_length=256,
              pad_to_max_length=True,
              truncation=True,
              padding="max_length",
              return_tensors="pt",)
  
  source_ids = encoding["input_ids"].to(device, dtype=torch.long)
  source_mask = encoding["attention_mask"].to(device, dtype=torch.long)
  
  generated_ids = model.generate(
                  input_ids=source_ids,
                  attention_mask=source_mask,
                  max_length=150,
                  num_beams=2,
                  repetition_penalty=2.5,
                  length_penalty=1.0,
                  early_stopping=True)
  
  preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
  
  print(f"Original Sentence :: {sentence}")
  print(f"Paraphrased Sentence :: {preds[0]}")
  
  ```