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import json | |
import numpy as np | |
import httpx | |
import os | |
from constants import MUBERT_TAGS, MUBERT_MODE, MUBERT_LICENSE | |
def get_mubert_tags_embeddings(w2v_model): | |
return w2v_model.encode(MUBERT_TAGS) | |
def find_similar(em, embeddings, method='cosine'): | |
scores = [] | |
for ref in embeddings: | |
if method == 'cosine': | |
scores.append(1 - np.dot(ref, em) / (np.linalg.norm(ref) * np.linalg.norm(em))) | |
if method == 'norm': | |
scores.append(np.linalg.norm(ref - em)) | |
return np.array(scores), np.argsort(scores) | |
def get_tags_for_prompts(w2v_model, mubert_tags_embeddings, prompts, top_n=3, debug=False): | |
prompts_embeddings = w2v_model.encode(prompts) | |
ret = [] | |
for i, pe in enumerate(prompts_embeddings): | |
scores, idxs = find_similar(pe, mubert_tags_embeddings) | |
top_tags = MUBERT_TAGS[idxs[:top_n]] | |
top_prob = 1 - scores[idxs[:top_n]] | |
if debug: | |
print(f"Prompt: {prompts[i]}\nTags: {', '.join(top_tags)}\nScores: {top_prob}\n\n\n") | |
ret.append((prompts[i], list(top_tags))) | |
print("ret: " + ret) | |
return ret |