texthero/nlp.py
def noun_chunks(s):
    """
    Return noun_chunks, flat phrases that have a noun as their head.
    
    """
    noun_chunks = []

    nlp = spacy.load('en_core_web_sm', disable=["ner"])
    # nlp.pipe is now "tagger", "parser"

    for doc in nlp.pipe(s.astype('unicode').values, batch_size=32):
        noun_chunks.append([(chunk.text, chunk.label_, chunk.start_char,
                             chunk.end_char) for chunk in doc.noun_chunks])

    return pd.Series(noun_chunks, index=s.index)

    Return noun_chunks, flat phrases that have a noun as their head.
    
    
Return noun_chunks, flat phrases that have a noun as their head.
Return noun_chunks flat phrases that have a noun as their head .
def noun_chunks(s):
    noun_chunks = []
    nlp = spacy.load('en_core_web_sm', disable=["ner"])
    for doc in nlp.pipe(s.astype('unicode').values, batch_size=32):
        noun_chunks.append([(chunk.text, chunk.label_, chunk.start_char,
                             chunk.end_char) for chunk in doc.noun_chunks])
    return pd.Series(noun_chunks, index=s.index)
def noun_chunks ( s ) : noun_chunks = [ ] nlp = spacy . load ( 'en_core_web_sm' , disable = [ "ner" ] ) for doc in nlp . pipe ( s . astype ( 'unicode' ) . values , batch_size = 32 ) : noun_chunks . append ( [ ( chunk . text , chunk . label_ , chunk . start_char , chunk . end_char ) for chunk in doc . noun_chunks ] ) return pd . Series ( noun_chunks , index = s . index )
Return noun_chunks, flat phrases that have a noun as their head.
Return noun_chunks flat phrases that have a noun as their head .
"""
    Return noun_chunks, flat phrases that have a noun as their head.
    
    """ # nlp.pipe is now "tagger", "parser"
s