mirror of
https://github.com/stardrophere/InsightRadar.git
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stardrophere
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
# from dotenv import load_dotenv
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# import os
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# import time
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#
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# print("step 1: loading env")
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# load_dotenv()
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#
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# hf_token = os.getenv("HF_TOKEN")
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# print("step 2:", "HF_TOKEN loaded" if hf_token else "No token")
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#
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# print("step 3: importing sentence-transformers")
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# from sentence_transformers import SentenceTransformer
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#
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# print("step 4: start loading model")
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# t0 = time.time()
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# model = SentenceTransformer(r"E:\Models\bge-m3", local_files_only=True, device="cuda")
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# print(f"step 5: model loaded in {time.time() - t0:.2f}s")
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#
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# print("step 6: importing sklearn/numpy")
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# from sklearn.metrics.pairwise import cosine_similarity
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# import numpy as np
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#
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# titles = [
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# # A组：同品牌同产品，但含义不同
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# "苹果发布新款iPhone，影像系统再次升级",
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# "苹果推出全新iPhone，摄像头性能进一步增强",
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# "苹果回应新款iPhone发热问题：将通过系统更新修复",
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# "苹果下调部分旧款iPhone售价，新机型并未参与促销",
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#
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# # B组：看起来都像“苹果新闻”，但主题已变
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# "苹果公司股价上涨，市值再创新高",
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# "苹果供应链承压，部分零部件厂商下调全年预期",
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# "苹果被曝缩减Vision产品产量，市场需求不及预期",
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# "苹果发布新款MacBook，并未更新iPhone产品线",
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#
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# # C组：同样是“发布/推出”，但主体不同
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# "华为发布新款手机，影像能力进一步提升",
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# "小米推出全新手机，影像系统迎来升级",
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# "OPPO发布年度旗舰机型，主打夜景拍摄",
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# ]
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#
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# print("step 7: start encoding")
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# t1 = time.time()
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# embeddings = model.encode(
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# titles,
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# normalize_embeddings=True,
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# show_progress_bar=True,
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# batch_size=16
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# )
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# print(f"step 8: encode done in {time.time() - t1:.2f}s")
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#
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# sim = cosine_similarity(embeddings)
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# print(np.round(sim, 4))
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#
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import secrets
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print(secrets.token_urlsafe(64))
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