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