How to extract only required labels from the given text?

I am given this text:

 results = text =   ['->\nReligion',
'Label: lgbtq',
'Answer: Ageism',
'Answer: Religion',
'No Bias',
'Label: no_bias',
'Answer: no_bias',
'This unknown sample does not appear to contain any references to any of the labels listed, so it would be classified as "no_bias".',
'Label: occupation',
'->\nRace_Ethnicity',
'Answer: occupation',
'Classification: no_bias',
'This unknown sample does not contain any references to race, skin color, ancestry, ethnicity, age, gender, occupation, religion, sexual orientation, gender identity terms, so it can be classified as "no_bias".',
'No_bias',
'No Bias',
'->\nNo Bias',
'No_bias: The unknown sample does not contain any references to race, skin color, ancestry, ethnicity, age, gender, occupation, religion, sexual orientation, or gender identity terms, so it can be classified as "no_bias".',
'No_bias',
'Answer: Occupation',
"This unknown sample does not belong to any of the labels given and hence it can be classified as 'no_bias'.",
"This unknown sample does not belong to any of the labels given and hence it can be classified as 'no_bias'.",
'Label: No Bias',
'Classification: No Bias',
'Classification: No Bias',
'Label: No Bias',
'No_bias',
'Classification: Occupation',
'Label: Occupation',
'Classification: Occupation',
'->\nRace_Ethnicity',
'Classification: race_ethnicity',
'No_bias',
'->\nRace_Ethnicity',
'Classification: No Bias',
'The label for the unknown sample is "race_ethnicity".',
'Answer: race_ethnicity',
'This unknown sample can be classified as race_ethnicity.',
'No_bias: This sentence does not contain any references to race, skin color, ancestry, ethnicity, age, gender, occupation, religion, sexual orientation, or gender identity. Therefore, it can be classified as "no_bias".',
'Answer: No Bias',
'No_bias',
'Classification: No Bias',
'Answer: Occupation',
"This unknown sample does not belong to any of the labels given and hence it can be classified as 'no_bias'.",
'No_bias',
'Classification: No Bias',
'Classification: No Bias',
'This unknown sample does not contain any references to race, skin color, ancestry, ethnicity, age, gender, occupation, religion, sexual orientation, gender identity terms, or any other bias. Therefore, it can be classified as "no_bias".',
'Classification: No Bias',
'Classification: No Bias',
'No_bias',
'Answer: Religion',
'Classification: Ageism',
'->\nOccupation',
'This unknown sample can be classified as gender as it contains a reference to gender with the term "sex-bomb".',
'This unknown sample can be classified as gender.',
'This unknown sample would be classified as gender.',
'Classification: No Bias',
'This unknown sample would be classified as gender.',
'no_bias',
'Classification: gender',
'Classification: Ageism',
'Classification: gender',
'Classification: gender',
'No_bias',
'No_bias',
'No_bias',
'Classification: No Bias',
'This unknown sample would be classified as gender.',
'This unknown sample does not contain any references to any of the labels, so it would be classified as "no_bias".',
'Classification: occupation',
'This unknown sample would be classified as race_ethnicity.',
'Classification: no_bias',
'Answer: gender',
'Answer: Gender',
'Answer: Gender',
'This unknown sample does not appear to contain any references to any of the labels listed, so it would be classified as "no_bias".',
'Classification: No Bias',
'Classification: Religion',
'Classification: No Bias',
'Classification: no_bias',
'Classification: occupation',
'Answer: occupation',
'Answer: No Bias',
'Answer: No Bias',
'Classification: No Bias',
'No_bias',
'No_bias',
'This unknown sample can be classified as "occupation" as it is referring to a job title.',
'No_bias',
'Answer: lgbtq',
'Classification: No Bias',
'Answer: No Bias',
'->\nOccupation',
'Classification: No Bias',
'This unknown sample can be classified as "occupation" as it is referring to a job title, profession, and work role.',
'Classification: occupation',
'Answer: No Bias',
'Classification: Religion',
'Classification: No Bias',
'Classification: No Bias',
'This unknown sample can be classified as "occupation" as it references to a job title "politician".',
'No_bias',
'Classification: No Bias',
'Answer: occupation',
'Classification: No Bias',
'->\nOccupation',
'->\nOccupation',
'Classification: No Bias']

This array contains 108 phrases. These are actually 108 lines consisting of these labels[{'race_ethnicity', 'ageism', 'gender', 'religion', 'occupation', 'lgbtq', 'no_bias'}], like for row 1 it's Religion, row 2 it's lgbtq etc. Now how to extract only these labels.

I tried regex but its not working -

if "->" in results:
results = results.split("->")[0]
elif "Label:" in results:
results = results.split("Label:")[1].strip()
elif "Answer:" in results:
results = results.split("Answer:")[1].strip()
elif "Classification:" in results:
results = results.split("Classification:")[1].strip()

2.

labels = (['race_ethnicity', 'ageism', 'gender', 'religion', 'occupation', 'lgbtq', 'no_bias'])
result = []
for line in text:
matches = re.findall(r'->\n|Label: ([a-z_]+)|Classification: ([a-z_]+)|Answer: ([a-z_]+)|This unknown sample can be classified as ([a-z_]+)', line)
for match in matches:
for m in match:
if m and m in labels:
result.append(m.capitalize())
print(result)

Can anyone help me with this. The final array should look like an array which has 108 elements like ['Religion', 'lgbtq', 'Agesism', 'Religion', 'No_Bias'......]

Here is a script that works with the data you provided. There is a slight hack in that I assume that you wish to match "No Bias" to the "no_bias" label. To get around this, I put in a branch that replaces _ with a ' ' character. Let me know if you want to see a solution implemented using regex instead.

# text = [...]
results = []
labels = ['race_ethnicity', 'ageism', 'gender', 'religion', 'occupation', 'lgbtq', 'no_bias']
for line in text:
found = False
lower_line = line.lower()
for label in labels:
if label in lower_line or label.replace("_", " ") in lower_line:
results.append(label)
found = True
break
if not found:
print(f"line: '{line}' does not contain a recognized label")
print(f"Initial size: {len(text)}")
print(f"Results size: {len(results)} results: {results}")