HF-NLP-Transformer models : Natural Language Processing

https://huggingface.co/learn/nlp-course/chapter1/2?fw=pt

Natural Language Processing - Hugging Face NLP Course

 

 

Before jumping into Transformer models, let’s do a quick overview of what natural language processing is and why we care about it.

 

Transformer 모델을 살펴보기 전에 자연어 처리가 무엇인지, 그리고 우리가 그것에 관심을 갖는 이유에 대해 간략하게 살펴보겠습니다.

 

What is NLP?

NLP is a field of linguistics and machine learning focused on understanding everything related to human language. The aim of NLP tasks is not only to understand single words individually, but to be able to understand the context of those words.

 

NLP는 인간 언어와 관련된 모든 것을 이해하는 데 초점을 맞춘 언어학 및 기계 학습 분야입니다. NLP 작업의 목표는 단일 단어를 개별적으로 이해하는 것뿐만 아니라 해당 단어의 맥락을 이해하는 것입니다.

 

The following is a list of common NLP tasks, with some examples of each:

 

다음은 일반적인 NLP 작업 목록과 각 작업의 몇 가지 예입니다.

 

• Classifying whole sentences: Getting the sentiment of a review, detecting if an email is spam, determining if a sentence is grammatically correct or whether two sentences are logically related or not
  
  
• 전체 문장 분류: 리뷰의 감정 파악, 이메일 스팸 여부 감지, 문장이 문법적으로 올바른지 또는 두 문장이 논리적으로 관련되어 있는지 확인
  
  
• Classifying each word in a sentence: Identifying the grammatical components of a sentence (noun, verb, adjective), or the named entities (person, location, organization)
  
  
• 문장의 각 단어 분류: 문장의 문법적 구성 요소(명사, 동사, 형용사) 또는 명명된 개체(사람, 위치, 조직) 식별
  
  
• Generating text content: Completing a prompt with auto-generated text, filling in the blanks in a text with masked words
  
  
• 텍스트 콘텐츠 생성: 자동 생성된 텍스트로 프롬프트 완성, 마스크된 단어로 텍스트의 공백 채우기
  
  
• Extracting an answer from a text: Given a question and a context, extracting the answer to the question based on the information provided in the context
  
  
• 텍스트에서 답변 추출: 질문과 컨텍스트가 주어지면, 컨텍스트에 제공된 정보를 기반으로 질문에 대한 답변을 추출합니다.
  
  
• Generating a new sentence from an input text: Translating a text into another language, summarizing a text
  
  
• 입력 텍스트에서 새 문장 생성: 텍스트를 다른 언어로 번역, 텍스트 요약

 

NLP isn’t limited to written text though. It also tackles complex challenges in speech recognition and computer vision, such as generating a transcript of an audio sample or a description of an image.

 

NLP는 서면 텍스트에만 국한되지 않습니다. 또한 오디오 샘플의 대본이나 이미지 설명 생성과 같은 음성 인식 및 컴퓨터 비전의 복잡한 문제를 해결합니다.

 

Why is it challenging? 왜 이것이 어려운가?

Computers don’t process information in the same way as humans. For example, when we read the sentence “I am hungry,” we can easily understand its meaning. Similarly, given two sentences such as “I am hungry” and “I am sad,” we’re able to easily determine how similar they are. For machine learning (ML) models, such tasks are more difficult. The text needs to be processed in a way that enables the model to learn from it. And because language is complex, we need to think carefully about how this processing must be done. There has been a lot of research done on how to represent text, and we will look at some methods in the next chapter.

 

컴퓨터는 인간과 같은 방식으로 정보를 처리하지 않습니다. 예를 들어, 우리는 “나는 배고프다”라는 문장을 읽으면 그 의미를 쉽게 이해할 수 있습니다. 마찬가지로, “나는 배고프다”와 “나는 슬프다”라는 두 문장이 주어지면 우리는 그 두 문장이 얼마나 비슷한지 쉽게 판단할 수 있습니다. 기계 학습(ML) 모델의 경우 이러한 작업은 더 어렵습니다. 텍스트는 모델이 학습할 수 있는 방식으로 처리되어야 합니다. 그리고 언어는 복잡하기 때문에 이 처리가 어떻게 이루어져야 하는지 신중하게 생각해야 합니다. 텍스트를 표현하는 방법에 대해 많은 연구가 진행되어 왔으며 다음 장에서 몇 가지 방법을 살펴보겠습니다.