quality_test/contains doesn't function about textdescriptives HOT 6 CLOSED

import textdescriptives as td

td.__version__
# 2.8.0
import spacy
nlp = spacy.blank("en")
nlp.add_pipe("sentencizer")
quality_pipe = nlp.add_pipe("textdescriptives/quality")
docs = nlp.pipe(["lorem ipsum"])
doc = next(docs)
doc._.passed_quality_check
# False
doc._.quality
# QualityOutput(
# 	passed=False, ...
#	contains={'lorem ipsum': ThresholdsOutput(value=1.0, passed=False, threshold=False)}, ...

import textdescriptives as td
import spacy
from spacy.cli import download

QUALITY_THRESHOLDS = td.QualityThresholds(
    n_stop_words=(None, None),
    alpha_ratio=(0.6, None),
    mean_word_length=(3, 10),
    doc_length=(1, 1000),
    symbol_to_word_ratio={"@": (None, 0.3)},
    proportion_ellipsis=(None, None),
    proportion_bullet_points=(None, 0.7),
    contains={"fake": False},
    duplicate_line_chr_fraction=(None, 0.2),
    duplicate_paragraph_chr_fraction=(None, 0.2),
    duplicate_ngram_chr_fraction={
        "5": (None, 0.15),
        "6": (None, 0.14),
        "7": (None, 0.13),
        "8": (None, 0.12),
        "9": (None, 0.11),
        "10": (None, 0.1),
    },
    top_ngram_chr_fraction={"2": (None, 0.2), "3": (None, 0.18), "4": (None, 0.16)},
    oov_ratio=(None, 0.3)
)

download("en_core_web_lg")
nlp = spacy.load("en_core_web_lg")
quality_pipe = nlp.add_pipe("textdescriptives/quality")
quality_pipe.set_quality_thresholds(QUALITY_THRESHOLDS)

text = "This is fake @@@@@"
doc = nlp(text)
print(doc._.quality)

passed=True 
	n_stop_words=ThresholdsOutput(value=2.0, passed=True, threshold=(None, None)) 
	alpha_ratio=ThresholdsOutput(value=0.75, passed=True, threshold=(0.6, None)) 
	mean_word_length=ThresholdsOutput(value=3.75, passed=True, threshold=(3.0, 10.0)) 
	doc_length=ThresholdsOutput(value=4.0, passed=True, threshold=(1.0, 1000.0)) 
	symbol_to_word_ratio={'#': ThresholdsOutput(value=0.0, passed=True, threshold=None)} 
	proportion_ellipsis=ThresholdsOutput(value=0.0, passed=True, threshold=(None, None)) 
	proportion_bullet_points=ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.7)) 
	contains={'lorem ipsum': ThresholdsOutput(value=0.0, passed=True, threshold=None)} 
	duplicate_line_chr_fraction=ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.2)) 
	duplicate_paragraph_chr_fraction=ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.2)) 
	duplicate_ngram_chr_fraction={'5': ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.15)), '6': ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.14)), '7': ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.13)), '8': ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.12)), '9': ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.11)), '10': ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.1))} 
	top_ngram_chr_fraction={'2': ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.2)), '3': ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.18)), '4': ThresholdsOutput(value=0.0, passed=True, threshold=(None, 0.16))} 
	oov_ratio=ThresholdsOutput(value=0.25, passed=True, threshold=(None, 0.3))