Korean BART model for paraphrasing. The dataset utilized can be found on the Files and versions tab under the name dataset.csv.
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]}")
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