models/train_classifier.py
def tokenize(text):
    """
    Tokenize and lemmatize each word in a given text
    Input: Text data
    Output: List of clean tokens
    
    """
    tokens=word_tokenize(text)
    lemmatizer = WordNetLemmatizer()
    
    clean_tokens = []
    for tok in tokens:
        clean_tok = lemmatizer.lemmatize(tok).lower().strip()
        clean_tokens.append(clean_tok)

    return clean_tokens

    Tokenize and lemmatize each word in a given text
    Input: Text data
    Output: List of clean tokens
    
    
Tokenize and lemmatize each word in a given text
Input: Text data
Output: List of clean tokens
Tokenize and lemmatize each word in a given text Input : Text data Output : List of clean tokens
def tokenize(text):
    tokens=word_tokenize(text)
    lemmatizer = WordNetLemmatizer()
    clean_tokens = []
    for tok in tokens:
        clean_tok = lemmatizer.lemmatize(tok).lower().strip()
        clean_tokens.append(clean_tok)
    return clean_tokens
def tokenize ( text ) : tokens = word_tokenize ( text ) lemmatizer = WordNetLemmatizer ( ) clean_tokens = [ ] for tok in tokens : clean_tok = lemmatizer . lemmatize ( tok ) . lower ( ) . strip ( ) clean_tokens . append ( clean_tok ) return clean_tokens
Tokenize and lemmatize each word in a given text
Input: Text data
Output: List of clean tokens
Tokenize and lemmatize each word in a given text Input : Text data Output : List of clean tokens
"""
    Tokenize and lemmatize each word in a given text
    Input: Text data
    Output: List of clean tokens
    
    """
text