|
```python |
|
import torch |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
|
# device setting |
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
# load model and tokenizer |
|
model_name_or_path = "ddobokki/gpt2_poem" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) |
|
model = AutoModelForCausalLM.from_pretrained(model_name_or_path) |
|
model.to(device) |
|
|
|
keyword_start_token = "<k>" |
|
keyword_end_token = "</k>" |
|
text = "์ฐ ๊ผญ๋๊ธฐ๊ฐ ๋ณด์ด๋ ๊ฒฝ์น" |
|
input_text = keyword_start_token + text + keyword_end_token |
|
|
|
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device) |
|
gen_ids = model.generate( |
|
input_ids, max_length=64, num_beams=100, no_repeat_ngram_size=2 |
|
) |
|
generated = tokenizer.decode(gen_ids[0, :].tolist(), skip_special_tokens=True) |
|
>> ์ค๋ฅด๋ฝ๋ด๋ฆฌ๋ฝ |
|
์ฐ ๊ผญ๋๊ธฐ๋ฅผ ์ฌ๋ ค๋ค๋ณด๋ |
|
์๋ํ ๋ฉ๊ณ ์๋ํ |
|
๋๋ญ๊ฐ์ง์ ๋งค๋ฌ๋ฆฐ |
|
์์ ์ฐ์ ํ ๋ง๋ฆฌ |
|
์ด๋ฆ ๋ชจ๋ฅผ ํ ํํฌ๊ธฐ ์๊ณ |
|
์ด๋๋ก ๊ฐ ํ์ฉ ๋ ๋๊ฐ ๋ฒ๋ ธ๋ค |
|
``` |