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https://github.com/oobabooga/text-generation-webui.git
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3e7feb699c
* many openai updates * total reorg & cleanup. * fixups * missing import os for images * +moderations, custom_stopping_strings, more fixes * fix bugs in completion streaming * moderation fix (flagged) * updated moderation categories --------- Co-authored-by: Matthew Ashton <mashton-gitlab@zhero.org>
70 lines
No EOL
2.1 KiB
Python
70 lines
No EOL
2.1 KiB
Python
import time
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import numpy as np
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from numpy.linalg import norm
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from extensions.openai.embeddings import get_embeddings_model
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moderations_disabled = False # return 0/false
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category_embeddings = None
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antonym_embeddings = None
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categories = [ "sexual", "hate", "harassment", "self-harm", "sexual/minors", "hate/threatening", "violence/graphic", "self-harm/intent", "self-harm/instructions", "harassment/threatening", "violence" ]
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flag_threshold = 0.5
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def get_category_embeddings():
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global category_embeddings, categories
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if category_embeddings is None:
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embeddings = get_embeddings_model().encode(categories).tolist()
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category_embeddings = dict(zip(categories, embeddings))
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return category_embeddings
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def cosine_similarity(a, b):
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return np.dot(a, b) / (norm(a) * norm(b))
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# seems most openai like with all-mpnet-base-v2
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def mod_score(a, b):
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return 2.0 * np.dot(a, b)
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def moderations(input):
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global category_embeddings, categories, flag_threshold, moderations_disabled
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results = {
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"id": f"modr-{int(time.time()*1e9)}",
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"model": "text-moderation-001",
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"results": [],
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}
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embeddings_model = get_embeddings_model()
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if not embeddings_model or moderations_disabled:
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results['results'] = [{
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'categories': dict([ (C, False) for C in categories]),
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'category_scores': dict([ (C, 0.0) for C in categories]),
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'flagged': False,
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}]
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return results
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category_embeddings = get_category_embeddings()
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# input, string or array
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if isinstance(input, str):
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input = [input]
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for in_str in input:
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for ine in embeddings_model.encode([in_str]).tolist():
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category_scores = dict([ (C, mod_score(category_embeddings[C], ine)) for C in categories ])
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category_flags = dict([ (C, bool(category_scores[C] > flag_threshold)) for C in categories ])
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flagged = any(category_flags.values())
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results['results'].extend([{
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'flagged': flagged,
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'categories': category_flags,
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'category_scores': category_scores,
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}])
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print(results)
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return results |