【Python】 자연어 처리 및 LLM 유용 함수 모음

자연어 처리 및 LLM 유용 함수 모음 

 

추천글 : 【Python】 파이썬 유용 함수 모음, 【알고리즘】 21강. NLP와 LLM 

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1. 자연어 처리 유용 함수 [본문]

2. Llama2 응용 [본문]

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a. ollama.ai : Llama3, Phi-3, Mistral, Gemma, etc

b. GroqChat : Mixtral, Llama3, Gemma

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1. 자연어 처리 유용 함수 [목차]

⑴ 주어진 문장을 자동으로 영어로 번역하는 함수

 

! pip install --upgrade googletrans httpx httpcore deep_translator

def to_english (sentence):
    from deep_translator import GoogleTranslator
    translated = GoogleTranslator(source='auto', target='en').translate(sentence)
    return translated

print( to_english("나는 소년입니다.") )
# I am a boy.

print( to_english("단핵구") )
# monocytes

 

⑵ 주어진 문장을 자동으로 한국어로 번역하는 함수

 

def to_korean (sentence):
    from deep_translator import GoogleTranslator
    translated = GoogleTranslator(source='auto', target='ko').translate(sentence)
    return translated

print( to_korean("I am a boy.") )
# 저는 남자입니다.

 

⑶ 주어진 문장을 자동으로 일본어로 번역하는 함수

 

def to_japanese (sentence):
    from deep_translator import GoogleTranslator
    translated = GoogleTranslator(source='auto', target='ja').translate(sentence)
    return translated

print( to_japanese("I am a boy.") )
# 私は男の子です。

 

⑷ 임의의 가변 길이 자연어 문장을 그 의미를 고려하여 384차원으로 만드는 함수 (cf. CELLama)

 

from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
import numpy as np
from scipy.sparse import csr_matrix
import pandas as pd
from sklearn.neighbors import NearestNeighbors
import torch
from torch.utils.data import DataLoader, TensorDataset
from xgboost import XGBClassifier

def sentences_to_embedding(sentences):
    embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    db = embedding_function.embed_documents(sentences)
    emb_res = np.asarray(db)
    return emb_res


sentences = []
sentences.append("What is the meaning of: obsolete")
sentences.append("What is the meaning of: old-fashioned")
sentences.append("What is the meaning of: demagogue")
emb_res = sentences_to_embedding(sentences)

 

 

2. Llama2 응용 [목차]

⑴ Llama2를 이용한 영한 번역

 

import ollama

def english_to_korean(sentence):
    content = 'Translate "' + sentence + '" to Korean. Output only the translated sentence.'
    response = ollama.chat(model='llama2', messages=[
      {
        'role': 'user',
        'content': content,
      },
    ])
    return response['message']['content']
    
sentence = "I am a boy."
english_to_korean(sentence)

 

⑵ 주어진 문장이 화학식을 포함하는지 판단하기

 

import ollama

def is_chemical_formula(sentence):
    content = 'Please determine if "' + sentence + '" contains a chemical formula or not. If it is correct, answer "sure"; otherwise, "no".'
    response = ollama.chat(model='llama2', messages=[
      {
        'role': 'user',
        'content': content,
      },
    ])
    return response['message']['content']
    
sentence = "NH2OH is an amine."
result = is_chemical_formula(sentence)
print(result)
print('sure' in result.lower())

sentence = "I am a boy."
result = is_chemical_formula(sentence)
print(result)
print('sure' in result.lower())

### Output ###
'''
The term "NH2OH" does contain a chemical formula, so the answer is "yes" or "sure".
True
The statement "I am a boy" does not contain any chemical formulas, so the answer is "no".
False
'''

 

⑶ 주어진 명사가 고유명사(proper noun)인지 보통명사(common noun)인지를 판단하기 

 

import ollama

def is_proper_noun(noun):
    content = 'Please determine if "' + noun + '" is a proper noun or common noun. If it is a proper noun, answer "proper"; otherwise, "common".'
    response = ollama.chat(model='llama2', messages=[
      {
        'role': 'user',
        'content': content,
      },
    ])
    return response['message']['content']
    
sentence = "Pencil"
result = is_proper_noun(sentence)
print(result)
print('proper' in result.lower())

sentence = "Feynman"
result = is_proper_noun(sentence)
print(result)
print('proper' in result.lower())

### Output ###
'''
"Pencil" is a common noun. Therefore, the answer is "common".
False

"Feynman" is a proper noun. Therefore, the answer is "proper".
True
'''

 

입력: 2024.02.10 13:34