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oobabooga 2023-07-12 11:33:25 -07:00
parent 9b55d3a9f9
commit e202190c4f
24 changed files with 146 additions and 125 deletions
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15
extensions/openai/moderations.py
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@ -4,10 +4,10 @@ from numpy.linalg import norm
from extensions.openai.embeddings import get_embeddings_model
moderations_disabled = False # return 0/false
moderations_disabled = False # return 0/false
category_embeddings = None
antonym_embeddings = None
categories = [ "sexual", "hate", "harassment", "self-harm", "sexual/minors", "hate/threatening", "violence/graphic", "self-harm/intent", "self-harm/instructions", "harassment/threatening", "violence" ]
categories = ["sexual", "hate", "harassment", "self-harm", "sexual/minors", "hate/threatening", "violence/graphic", "self-harm/intent", "self-harm/instructions", "harassment/threatening", "violence"]
flag_threshold = 0.5
@ -40,23 +40,22 @@ def moderations(input):
embeddings_model = get_embeddings_model()
if not embeddings_model or moderations_disabled:
results['results'] = [{
'categories': dict([ (C, False) for C in categories]),
'category_scores': dict([ (C, 0.0) for C in categories]),
'categories': dict([(C, False) for C in categories]),
'category_scores': dict([(C, 0.0) for C in categories]),
'flagged': False,
}]
return results
category_embeddings = get_category_embeddings()
# input, string or array
if isinstance(input, str):
input = [input]
for in_str in input:
for ine in embeddings_model.encode([in_str]).tolist():
category_scores = dict([ (C, mod_score(category_embeddings[C], ine)) for C in categories ])
category_flags = dict([ (C, bool(category_scores[C] > flag_threshold)) for C in categories ])
category_scores = dict([(C, mod_score(category_embeddings[C], ine)) for C in categories])
category_flags = dict([(C, bool(category_scores[C] > flag_threshold)) for C in categories])
flagged = any(category_flags.values())
results['results'].extend([{
@ -67,4 +66,4 @@ def moderations(input):
print(results)
return results
return results