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docs/source/ko/model_doc/gpt_neox_japanese.md
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docs/source/ko/model_doc/gpt_neox_japanese.md
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# GPT-NeoX-Japanese [[gpt-neox-japanese]]
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## 개요 [[overview]]
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일본어를 위한 자동회귀 언어 모델인 GPT-NeoX-Japanese를 소개합니다. 이 모델은 [https://github.com/EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox)에서 학습되었습니다. 일본어는 많은 어휘와 히라가나, 가타카나, 한자의 조합으로 이루어진 독특한 언어입니다. 이러한 일본어의 독특한 구조를 해결하기 위해 [특수 서브워드 토크나이저](https://github.com/tanreinama/Japanese-BPEEncoder_V2)를 사용했습니다. 이 유용한 토크나이저를 오픈소스로 제공해 준 *tanreinama*에게 매우 감사드립니다.
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이 모델은 Google의 [PaLM](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html) 연구 권장 사항을 따르며, 트랜스포머 블록에서 편향 파라미터를 제거하여 모델 성능을 향상시켰습니다. 자세한 내용은 [이 기사](https://medium.com/ml-abeja/training-a-better-gpt-2-93b157662ae4)를 참조하세요.
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모델 개발은 [ABEJA, Inc.](https://www.abejainc.com/)의 [신야 오타니](https://github.com/SO0529), [타카요시 마카베](https://github.com/spider-man-tm), [안주 아로라](https://github.com/Anuj040), [쿄 하토리](https://github.com/go5paopao)에 의해 주도되었습니다. 이 모델 개발 활동에 대한 자세한 내용은 [여기](https://tech-blog.abeja.asia/entry/abeja-gpt-project-202207)를 참조하세요.
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### 사용 예시 [[usage-example]]
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`generate()` 메서드를 사용하면 GPT NeoX Japanese 모델을 통해 텍스트를 생성할 수 있습니다.
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```python
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>>> from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseTokenizer
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>>> model = GPTNeoXJapaneseForCausalLM.from_pretrained("abeja/gpt-neox-japanese-2.7b")
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>>> tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
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>>> prompt = "人とAIが協調するためには、"
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>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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>>> gen_tokens = model.generate(
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... input_ids,
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... do_sample=True,
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... temperature=0.9,
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... max_length=100,
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... )
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>>> gen_text = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)[0]
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>>> print(gen_text)
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人とAIが協調するためには、AIと人が共存し、AIを正しく理解する必要があります。
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```
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## 자료 [[resources]]
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- [일상 언어 모델링 작업 가이드 ](../tasks/language_modeling)
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## GPTNeoXJapanese 설정 (GPTNeoXJapaneseConfig) [[transformers.GPTNeoXJapaneseConfig]]
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[[autodoc]] GPTNeoXJapaneseConfig
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## GPTNeoXJapanese토큰화 (GPTNeoXJapaneseTokenizer) [[transformers.GPTNeoXJapaneseTokenizer]]
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[[autodoc]] GPTNeoXJapaneseTokenizer
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## GPTNeoXJapaneseModel [[transformers.GPTNeoXJapaneseModel]]
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[[autodoc]] GPTNeoXJapaneseModel
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- forward
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## 일상 LLM 을 위한 GPTNeoXJapanese(GPTNeoXJapaneseForCausalLM) [[transformers.GPTNeoXJapaneseForCausalLM]]
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[[autodoc]] GPTNeoXJapaneseForCausalLM
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- forward
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