transformers pytorch safetensors bart text2text-generation license:mit autotrain_compatible endpoints_compatible region:us
---
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]}")

```