{"id":63666,"date":"2026-01-22T08:05:00","date_gmt":"2026-01-22T00:05:00","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/63666.html"},"modified":"2026-01-22T08:05:00","modified_gmt":"2026-01-22T00:05:00","slug":"bge-m3%e5%a4%9a%e8%af%ad%e8%a8%80%e5%a4%84%e7%90%86%e4%bd%93%e9%aa%8c%ef%bc%9a%e6%8c%89%e9%9c%80%e4%bb%98%e8%b4%b9%ef%bc%8c%e6%af%94%e8%87%aa%e5%bb%ba%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%be%bf%e5%ae%9c80","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/63666.html","title":{"rendered":"BGE-M3\u591a\u8bed\u8a00\u5904\u7406\u4f53\u9a8c\uff1a\u6309\u9700\u4ed8\u8d39\uff0c\u6bd4\u81ea\u5efa\u670d\u52a1\u5668\u4fbf\u5b9c80%"},"content":{"rendered":"<h2>BGE-M3\u591a\u8bed\u8a00\u5904\u7406\u4f53\u9a8c&#xff1a;\u6309\u9700\u4ed8\u8d39&#xff0c;\u6bd4\u81ea\u5efa\u670d\u52a1\u5668\u4fbf\u5b9c80%<\/h2>\n<p>\u4f60\u662f\u4e0d\u662f\u4e5f\u9047\u5230\u8fc7\u8fd9\u6837\u7684\u60c5\u51b5&#xff1f;\u8de8\u5883\u7535\u5546\u521a\u8d77\u6b65&#xff0c;\u7528\u6237\u8bc4\u8bba\u6765\u81ea\u5168\u7403\u5404\u5730\u2014\u2014\u82f1\u8bed\u3001\u6cd5\u8bed\u3001\u897f\u73ed\u7259\u8bed\u3001\u65e5\u8bed\u3001\u963f\u62c9\u4f2f\u8bed\u2026\u2026\u4e94\u82b1\u516b\u95e8\u7684\u8bed\u8a00\u5806\u6210\u5c71&#xff0c;\u4eba\u5de5\u4e00\u6761\u6761\u770b\u6839\u672c\u6765\u4e0d\u53ca\u3002\u4f60\u60f3\u7528AI\u6765\u5206\u6790\u8fd9\u4e9b\u8bc4\u8bba\u7684\u60c5\u611f\u503e\u5411\u3001\u63d0\u53d6\u5173\u952e\u8bcd\u3001\u505a\u805a\u7c7b\u5f52\u7c7b&#xff0c;\u4f46IT\u90e8\u95e8\u4e00\u5f00\u53e3\u5c31\u662f&#xff1a;\u201c\u5f97\u914d\u56fd\u9645\u7248GPU\u670d\u52a1\u5668&#xff0c;\u6bcf\u67081.2\u4e07\u8d77&#xff0c;\u8fd8\u5f97\u957f\u671f\u79df\u3002\u201d\u53ef\u4f60\u73b0\u5728\u6d41\u91cf\u4e0d\u7a33\u5b9a&#xff0c;\u9879\u76ee\u8fd8\u5728\u6d4b\u8bd5\u9636\u6bb5&#xff0c;\u771f\u6ca1\u5fc5\u8981\u7838\u8fd9\u4e48\u5927\u6210\u672c\u3002<\/p>\n<p>\u522b\u6025&#xff0c;\u4eca\u5929\u6211\u6765\u7ed9\u4f60\u652f\u4e2a\u62db&#xff1a;\u7528BGE-M3\u6a21\u578b 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\/>\n<h3>1. \u4e3a\u4ec0\u4e48\u8de8\u5883\u7535\u5546\u5fc5\u987b\u7528BGE-M3\u5904\u7406\u591a\u8bed\u8a00\u8bc4\u8bba&#xff1f;<\/h3>\n<h4>1.1 \u8de8\u5883\u7535\u5546\u7684\u771f\u5b9e\u75db\u70b9&#xff1a;\u8bed\u8a00\u592a\u591a&#xff0c;\u4fe1\u606f\u592a\u4e71<\/h4>\n<p>\u60f3\u8c61\u4e00\u4e0b&#xff0c;\u4f60\u7684\u5e97\u94fa\u521a\u4e0a\u7ebf\u4e09\u4e2a\u6708&#xff0c;\u4ea7\u54c1\u5356\u5230\u4e86\u6b27\u7f8e\u3001\u4e1c\u5357\u4e9a\u3001\u4e2d\u4e1c\u3002\u6bcf\u5929\u6536\u5230\u51e0\u767e\u6761\u7528\u6237\u53cd\u9988&#xff0c;\u6709\u7684\u8bf4\u201clove this product\u201d&#xff0c;\u6709\u7684\u5199\u201cproduit incroyable\u201d&#xff0c;\u8fd8\u6709\u7684\u7528\u963f\u62c9\u4f2f\u6587\u5938\u4f60\u5305\u88c5\u7cbe\u81f4\u3002\u8fd9\u4e9b\u597d\u8bc4\u5982\u679c\u80fd\u96c6\u4e2d\u5206\u6790&#xff0c;\u5c31\u80fd\u63d0\u70bc\u51fa\u5356\u70b9&#xff1b;\u5dee\u8bc4\u5982\u679c\u80fd\u53ca\u65f6\u53d1\u73b0&#xff0c;\u8fd8\u80fd\u907f\u514d\u5ba2\u8bc9\u5347\u7ea7\u3002<\/p>\n<p>\u4f46\u95ee\u9898\u6765\u4e86&#xff1a;\u6ca1\u4eba\u80fd\u7cbe\u901a\u6240\u6709\u8bed\u8a00&#xff0c;\u7ffb\u8bd1\u5de5\u5177\u53c8\u4e0d\u51c6&#xff0c;\u9760\u4eba\u529b\u6574\u7406\u6548\u7387\u6781\u4f4e\u3002\u66f4\u522b\u8bf4\u8fd8\u8981\u5224\u65ad\u60c5\u7eea\u662f\u6b63\u9762\u3001\u8d1f\u9762\u8fd8\u662f\u4e2d\u6027&#xff0c;\u63d0\u53d6\u5173\u952e\u8bcd\u6bd4\u5982\u201c\u7269\u6d41\u6162\u201d\u201c\u8d28\u91cf\u597d\u201d\u201c\u5c3a\u5bf8\u504f\u5c0f\u201d\u2026\u2026\u8fd9\u5de5\u4f5c\u91cf&#xff0c;\u4e00\u4e2a\u4eba\u5e72\u4e00\u5929\u90fd\u641e\u4e0d\u5b9a\u3002<\/p>\n<p>\u4f20\u7edf\u505a\u6cd5\u662f\u96c7\u591a\u8bed\u79cd\u5ba2\u670d\u6216\u5916\u5305\u7ed9\u7b2c\u4e09\u65b9&#xff0c;\u6210\u672c\u9ad8\u4e0d\u8bf4&#xff0c;\u54cd\u5e94\u8fd8\u6162\u3002\u7b49\u4f60\u53d1\u73b0\u95ee\u9898\u65f6&#xff0c;\u53ef\u80fd\u5df2\u7ecf\u4e22\u4e86\u5341\u51e0\u4e2a\u8ba2\u5355\u3002\u800cAI\u65f6\u4ee3&#xff0c;\u6211\u4eec\u6709\u66f4\u597d\u7684\u9009\u62e9\u2014\u2014\u8ba9\u6a21\u578b\u5148\u201c\u8bfb\u201d\u4e00\u904d&#xff0c;\u628a\u5173\u952e\u4fe1\u606f\u81ea\u52a8\u63d0\u70bc\u51fa\u6765&#xff0c;\u4eba\u53ea\u9700\u8981\u505a\u6700\u7ec8\u51b3\u7b56\u3002<\/p>\n<h4>1.2 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lente.\u201d<\/li>\n<li>\u4e2d\u6587&#xff1a;\u201c\u9001\u8d27\u592a\u6162\u4e86\u3002\u201d<\/li>\n<\/ul>\n<p>\u867d\u7136\u8bed\u8a00\u4e0d\u540c&#xff0c;\u4f46BGE-M3\u4f1a\u628a\u5b83\u4eec\u6620\u5c04\u5230\u51e0\u4e4e\u76f8\u540c\u7684\u4f4d\u7f6e\u3002\u8fd9\u6837\u4e00\u6765&#xff0c;\u4f60\u5c31\u53ef\u4ee5\u7528\u4e00\u4e2a\u7edf\u4e00\u7684\u65b9\u5f0f\u53bb\u641c\u7d22\u3001\u5206\u7c7b\u3001\u805a\u7c7b\u8fd9\u4e9b\u8bc4\u8bba&#xff0c;\u5b8c\u5168\u4e0d\u7528\u7ba1\u5b83\u662f\u54ea\u79cd\u8bed\u8a00\u5199\u7684\u3002<\/p>\n<p>&#x1f4a1; \u63d0\u793a&#xff1a;\u8fd9\u79cd\u80fd\u529b\u53eb\u201c\u8de8\u8bed\u8a00\u8bed\u4e49\u5bf9\u9f50\u201d&#xff0c;\u662fBGE-M3\u7684\u6838\u5fc3\u4f18\u52bf\u4e4b\u4e00\u3002\u5b83\u8bad\u7ec3\u65f6\u7528\u4e86\u5927\u91cf\u591a\u8bed\u8a00\u5e73\u884c\u8bed\u6599&#xff0c;\u6240\u4ee5\u80fd\u6253\u901a\u8bed\u8a00\u58c1\u5792\u3002<\/p>\n<h4>1.3 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\u591a\u7c92\u5ea6\u68c0\u7d22&#xff08;Multi-Granularity&#xff09;<\/h5>\n<p>BGE-M3\u4e0d\u4ec5\u80fd\u5904\u7406\u77ed\u53e5&#xff0c;\u8fd8\u80fd\u7406\u89e3\u957f\u8fbe8192\u4e2atoken\u7684\u957f\u6587\u672c&#xff08;\u7ea66000\u6c49\u5b57&#xff09;\u3002\u8fd9\u5bf9\u5546\u54c1\u8be6\u60c5\u9875\u3001\u7528\u6237\u957f\u8bc4\u3001\u552e\u540e\u6c9f\u901a\u8bb0\u5f55\u7279\u522b\u6709\u7528\u3002<\/p>\n<p>\u4f60\u53ef\u4ee5\u8ba9\u5b83&#xff1a;<\/p>\n<ul>\n<li>\u5206\u6790\u5355\u6761\u8bc4\u8bba\u7684\u60c5\u7eea<\/li>\n<li>\u805a\u5408\u591a\u4e2a\u7528\u6237\u7684\u5171\u540c\u62b1\u6028<\/li>\n<li>\u5339\u914dFAQ\u4e2d\u7684\u89e3\u51b3\u65b9\u6848<\/li>\n<\/ul>\n<h5>\u2705 \u591a\u6a21\u5f0f\u8f93\u51fa&#xff08;Multi-Mode&#xff09;<\/h5>\n<p>\u8fd9\u662fBGE-M3\u6700\u725b\u7684\u5730\u65b9\u2014\u2014\u5b83\u540c\u65f6\u652f\u6301\u4e09\u79cd\u68c0\u7d22\u65b9\u5f0f&#xff1a;<\/p>\n<ul>\n<li>\u7a20\u5bc6\u68c0\u7d22&#xff08;Dense&#xff09;&#xff1a;\u57fa\u4e8e\u8bed\u4e49\u76f8\u4f3c\u5ea6&#xff0c;\u9002\u5408\u627e\u201c\u610f\u601d\u76f8\u8fd1\u201d\u7684\u5185\u5bb9<\/li>\n<li>\u7a00\u758f\u68c0\u7d22&#xff08;Sparse&#xff09;&#xff1a;\u57fa\u4e8e\u5173\u952e\u8bcd\u6743\u91cd&#xff0c;\u7c7b\u4f3c\u4f20\u7edf\u641c\u7d22\u5f15\u64ce<\/li>\n<li>\u591a\u5411\u91cf&#xff08;Multi-Vector&#xff09;&#xff1a;\u7ed3\u5408\u4e24\u8005\u4f18\u70b9&#xff0c;\u7cbe\u5ea6\u66f4\u9ad8<\/li>\n<\/ul>\n<p>\u4f60\u53ef\u4ee5\u6839\u636e\u4efb\u52a1\u7075\u6d3b\u5207\u6362\u6a21\u5f0f\u3002\u6bd4\u5982\u505a\u60c5\u611f\u5206\u6790\u7528\u7a20\u5bc6\u6a21\u5f0f&#xff0c;\u505a\u5173\u952e\u8bcd\u63d0\u53d6\u7528\u7a00\u758f\u6a21\u5f0f&#xff0c;\u6548\u679c\u66f4\u597d\u3002<\/p>\n<h4>1.4 \u548c\u5176\u4ed6\u6a21\u578b\u6bd4&#xff0c;BGE-M3\u6709\u4ec0\u4e48\u4f18\u52bf&#xff1f;<\/h4>\n<p>\u7f51\u4e0a\u4e5f\u6709\u4e0d\u5c11\u5f00\u6e90embedding\u6a21\u578b&#xff0c;\u6bd4\u5982Sentence-BERT\u3001Piccolo\u7cfb\u5217\u7b49\u3002\u90a3\u4e3a\u4ec0\u4e48\u63a8\u8350BGE-M3&#xff1f;<\/p>\n<table>\n<tr>\u5bf9\u6bd4\u9879BGE-M3\u5176\u4ed6\u5e38\u89c1\u6a21\u578b<\/tr>\n<tbody>\n<tr>\n<td>\u591a\u8bed\u8a00\u652f\u6301<\/td>\n<td>\u2705 \u8d85100\u79cd&#xff0c;\u4f18\u5316\u826f\u597d<\/td>\n<td>\u274c \u591a\u6570\u4ec5\u652f\u6301\u82f1\/\u4e2d<\/td>\n<\/tr>\n<tr>\n<td>\u8f93\u5165\u957f\u5ea6<\/td>\n<td>\u2705 \u6700\u9ad88192 token<\/td>\n<td>\u26a0\ufe0f \u901a\u5e38512~2048<\/td>\n<\/tr>\n<tr>\n<td>\u663e\u5b58\u9700\u6c42<\/td>\n<td>\u2705 FP16\u7ea66.8GB&#xff0c;\u53ef\u91cf\u5316\u81f36GB\u4ee5\u4e0b<\/td>\n<td>\u26a0\ufe0f \u90e8\u5206\u6a21\u578b\u97008GB&#043;<\/td>\n<\/tr>\n<tr>\n<td>\u63a8\u7406\u901f\u5ea6<\/td>\n<td>\u2705 \u4f18\u5316\u826f\u597d&#xff0c;GPU\u4e0b\u5ef6\u8fdf\u4f4e<\/td>\n<td>\u26a0\ufe0f \u90e8\u5206\u6a21\u578b\u541e\u5410\u91cf\u4f4e<\/td>\n<\/tr>\n<tr>\n<td>\u662f\u5426\u514d\u8d39\u5546\u7528<\/td>\n<td>\u2705 \u53ef\u5546\u7528&#xff0c;\u65e0\u9650\u5236<\/td>\n<td>\u26a0\ufe0f \u90e8\u5206\u9700\u6388\u6743<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u7279\u522b\u662f\u5bf9\u4e8e\u521d\u521b\u56e2\u961f\u6765\u8bf4&#xff0c;\u514d\u8d39&#043;\u9ad8\u6027\u80fd&#043;\u6613\u90e8\u7f72\u8fd9\u4e09\u4e2a\u70b9&#xff0c;BGE-M3\u51e0\u4e4e\u662f\u76ee\u524d\u6700\u4f18\u89e3\u3002<\/p>\n<hr \/>\n<h3>2. \u5982\u4f55\u96f6\u57fa\u7840\u90e8\u7f72BGE-M3&#xff1f;\u4e00\u952e\u542f\u52a8\u5168\u6d41\u7a0b<\/h3>\n<h4>2.1 \u4e3a\u4ec0\u4e48\u4e0d\u8981\u81ea\u5efa\u670d\u52a1\u5668&#xff1f;\u7b97\u7b14\u8d26\u4f60\u5c31\u660e\u767d\u4e86<\/h4>\n<p>\u5148\u56de\u7b54\u90a3\u4e2a\u6700\u73b0\u5b9e\u7684\u95ee\u9898&#xff1a;\u4e3a\u4ec0\u4e48\u4e0d\u81ea\u5df1\u4e70GPU\u670d\u52a1\u5668&#xff1f;<\/p>\n<p>\u5047\u8bbe\u4f60\u8981\u90e8\u7f72BGE-M3&#xff0c;\u81f3\u5c11\u9700\u8981\u4e00\u5f20NVIDIA\u663e\u5361&#xff0c;\u663e\u5b58\u22658GB\u3002\u5982\u679c\u9009A10\u3001V100\u8fd9\u7c7b\u56fd\u9645\u4e91\u5382\u5546\u5e38\u7528\u7684\u5361&#xff0c;\u6708\u79df\u786e\u5b9e\u89811\u4e07\u4ee5\u4e0a\u3002\u5c31\u7b97\u9000\u800c\u6c42\u5176\u6b21\u7528RTX 3090\/4090&#xff0c;\u56fd\u5185\u5408\u89c4\u673a\u623f\u4e5f\u8981\u4e94\u516d\u5343\u4e00\u4e2a\u6708\u3002<\/p>\n<p>\u800c\u4e14\u4f60\u8fd8\u5f97\u8003\u8651&#xff1a;<\/p>\n<ul>\n<li>\u7cfb\u7edf\u5b89\u88c5\u3001CUDA\u9a71\u52a8\u3001PyTorch\u73af\u5883\u914d\u7f6e&#xff08;\u65b0\u624b\u81f3\u5c11\u6298\u817e\u4e09\u5929&#xff09;<\/li>\n<li>\u6a21\u578b\u4e0b\u8f7d&#xff08;BGE-M3\u5b8c\u6574\u7248\u7ea613GB&#xff0c;\u7f51\u7edc\u4e0d\u597d\u5bb9\u6613\u65ad&#xff09;<\/li>\n<li>\u5b89\u5168\u9632\u62a4\u3001API\u66b4\u9732\u3001\u5e76\u53d1\u8bbf\u95ee\u7b49\u95ee\u9898<\/li>\n<\/ul>\n<p>\u7ed3\u679c\u5462&#xff1f;\u4f60\u82b1\u4e86\u4e00\u4e24\u4e07&#xff0c;\u6700\u540e\u53ea\u7528\u6765\u8dd1\u4e00\u4e2a\u6a21\u578b&#xff0c;\u5229\u7528\u7387\u6781\u4f4e\u3002\u5c24\u5176\u5f53\u4f60\u53ea\u662f\u60f3\u505a\u4e2a\u6d4b\u8bd5\u9a8c\u8bc1\u60f3\u6cd5\u65f6&#xff0c;\u8fd9\u7b14\u6295\u5165\u592a\u91cd\u4e86\u3002<\/p>\n<p>\u66f4\u597d\u7684\u65b9\u5f0f\u662f&#xff1a;\u7528\u9884\u7f6e\u955c\u50cf &#043; \u6309\u9700\u4ed8\u8d39GPU\u8d44\u6e90&#xff0c;\u6309\u5c0f\u65f6\u8ba1\u8d39&#xff0c;\u7528\u5b8c\u5c31\u505c\u3002<\/p>\n<p>\u6bd4\u5982CSDN\u661f\u56fe\u5e73\u53f0\u63d0\u4f9b\u7684BGE-M3\u4e13\u7528\u955c\u50cf&#xff0c;\u5df2\u7ecf\u5e2e\u4f60\u88c5\u597d\u4e86&#xff1a;<\/p>\n<ul>\n<li>CUDA 11.8 &#043; PyTorch 2.0<\/li>\n<li>Transformers\u3001sentence-transformers\u5e93<\/li>\n<li>FastAPI\u670d\u52a1\u6846\u67b6<\/li>\n<li>\u793a\u4f8b\u4ee3\u7801\u548c\u6d4b\u8bd5\u811a\u672c<\/li>\n<\/ul>\n<p>\u4f60\u53ea\u9700\u8981\u70b9\u51fb\u201c\u4e00\u952e\u90e8\u7f72\u201d&#xff0c;\u9009\u4e2a\u5e26GPU\u7684\u5b9e\u4f8b&#xff08;\u5efa\u8bae\u663e\u5b58\u22658GB&#xff09;&#xff0c;\u51e0\u5206\u949f\u540e\u5c31\u80fd\u62ff\u5230\u4e00\u4e2a\u53ef\u8bbf\u95ee\u7684API\u63a5\u53e3&#xff0c;\u6bcf\u5c0f\u65f6\u51e0\u6bdb\u94b1&#xff0c;\u4e0d\u7528\u4e86\u968f\u65f6\u5173\u673a&#xff0c;\u771f\u6b63\u5b9e\u73b0\u201c\u7528\u591a\u5c11\u4ed8\u591a\u5c11\u201d\u3002<\/p>\n<h4>2.2 \u4e09\u6b65\u5b8c\u6210BGE-M3\u90e8\u7f72&#xff08;\u9644\u8be6\u7ec6\u622a\u56fe\u6307\u5f15&#xff09;<\/h4>\n<h5>\u7b2c\u4e00\u6b65&#xff1a;\u9009\u62e9\u9884\u7f6e\u955c\u50cf<\/h5>\n<p>\u767b\u5f55CSDN\u661f\u56fe\u5e73\u53f0\u540e&#xff0c;\u5728\u955c\u50cf\u5e7f\u573a\u641c\u7d22\u201cBGE-M3\u201d\u6216\u201c\u591a\u8bed\u8a00Embedding\u201d&#xff0c;\u627e\u5230\u5bf9\u5e94\u955c\u50cf\u3002\u786e\u8ba4\u63cf\u8ff0\u4e2d\u5305\u542b\u4ee5\u4e0b\u4fe1\u606f&#xff1a;<\/p>\n<ul>\n<li>\u6a21\u578b\u540d\u79f0&#xff1a;BAAI\/bge-m3<\/li>\n<li>\u652f\u6301\u6846\u67b6&#xff1a;Transformers \/ Sentence-Transformers<\/li>\n<li>\u9884\u88c5\u670d\u52a1&#xff1a;FastAPI or Gradio<\/li>\n<li>GPU\u652f\u6301&#xff1a;Yes<\/li>\n<\/ul>\n<p>\u70b9\u51fb\u201c\u7acb\u5373\u4f7f\u7528\u201d\u6216\u201c\u90e8\u7f72\u5b9e\u4f8b\u201d\u3002<\/p>\n<h5>\u7b2c\u4e8c\u6b65&#xff1a;\u914d\u7f6eGPU\u8d44\u6e90<\/h5>\n<p>\u5f39\u51fa\u914d\u7f6e\u7a97\u53e3\u65f6&#xff0c;\u91cd\u70b9\u770b\u4e09\u9879&#xff1a;<\/p>\n<li>GPU\u578b\u53f7&#xff1a;\u5efa\u8bae\u9009\u62e9NVIDIA T4\u3001A10\u6216H100&#xff08;\u5982\u6709&#xff09;\u3002T4\u6027\u4ef7\u6bd4\u6700\u9ad8&#xff0c;\u663e\u5b5816GB&#xff0c;\u8db3\u591f\u8fd0\u884cBGE-M3\u3002<\/li>\n<li>\u663e\u5b58\u5927\u5c0f&#xff1a;\u5fc5\u987b\u22658GB\u3002\u5b9e\u6d4bFP16\u7cbe\u5ea6\u4e0b&#xff0c;BGE-M3\u5360\u7528\u7ea66.8GB\u663e\u5b58&#xff0c;\u7559\u70b9\u4f59\u91cf\u66f4\u7a33\u3002<\/li>\n<li>\u5b58\u50a8\u7a7a\u95f4&#xff1a;\u5efa\u8bae\u900950GB\u4ee5\u4e0aSSD&#xff0c;\u7528\u4e8e\u7f13\u5b58\u6a21\u578b\u548c\u65e5\u5fd7\u3002<\/li>\n<p>\u5176\u4ed6\u914d\u7f6e\u4fdd\u6301\u9ed8\u8ba4\u5373\u53ef\u3002\u7136\u540e\u70b9\u51fb\u201c\u542f\u52a8\u5b9e\u4f8b\u201d\u3002<\/p>\n<p>\u26a0\ufe0f \u6ce8\u610f&#xff1a;\u9996\u6b21\u542f\u52a8\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u6a21\u578b&#xff08;\u7ea613GB&#xff09;&#xff0c;\u8017\u65f65~10\u5206\u949f&#xff0c;\u8bf7\u8010\u5fc3\u7b49\u5f85\u72b6\u6001\u53d8\u4e3a\u201c\u8fd0\u884c\u4e2d\u201d\u3002<\/p>\n<h5>\u7b2c\u4e09\u6b65&#xff1a;\u83b7\u53d6API\u5e76\u6d4b\u8bd5<\/h5>\n<p>\u5b9e\u4f8b\u542f\u52a8\u6210\u529f\u540e&#xff0c;\u4f60\u4f1a\u770b\u5230\u4e00\u4e2a\u516c\u7f51IP\u5730\u5740\u548c\u7aef\u53e3\u53f7&#xff08;\u5982 http:\/\/123.45.67.89:8080&#xff09;\u3002<\/p>\n<p>\u6253\u5f00\u6d4f\u89c8\u5668\u8bbf\u95ee\u8be5\u5730\u5740&#xff0c;\u901a\u5e38\u4f1a\u8fdb\u5165\u4e00\u4e2a\u6d4b\u8bd5\u9875\u9762&#xff08;Gradio\u754c\u9762&#xff09;&#xff0c;\u6216\u8005\u76f4\u63a5\u8fd4\u56deJSON\u8bf4\u660e\u6587\u6863\u3002<\/p>\n<p>\u4f60\u53ef\u4ee5\u76f4\u63a5\u5728\u7f51\u9875\u4e0a\u8f93\u5165\u4e00\u6bb5\u6587\u672c&#xff0c;\u6bd4\u5982&#xff1a;<\/p>\n<p>This product is amazing! Fast shipping and great quality.<\/p>\n<p>\u70b9\u51fb\u201cEmbedding\u201d\u6309\u94ae&#xff0c;\u51e0\u79d2\u540e\u5c31\u4f1a\u8fd4\u56de\u4e00\u4e32\u5411\u91cf\u6570\u636e&#xff08;\u5f88\u957f\u7684\u4e00\u7ec4\u6570\u5b57&#xff09;&#xff0c;\u8fd9\u5c31\u662fAI\u201c\u7406\u89e3\u201d\u8fd9\u53e5\u8bdd\u7684\u65b9\u5f0f\u3002<\/p>\n<p>\u5982\u679c\u4f60\u60f3\u901a\u8fc7\u7a0b\u5e8f\u8c03\u7528&#xff0c;\u53ef\u4ee5\u7528\u4e0b\u9762\u8fd9\u6bb5Python\u4ee3\u7801\u6d4b\u8bd5&#xff1a;<\/p>\n<p>import requests<\/p>\n<p>url &#061; &#034;http:\/\/123.45.67.89:8080\/embeddings&#034;<br \/>\ndata &#061; {<br \/>\n    &#034;input&#034;: [&#034;The delivery was too slow.&#034;, &#034;Je suis tr\u00e8s satisfait!&#034;]<br \/>\n}<\/p>\n<p>response &#061; requests.post(url, json&#061;data)<br \/>\nprint(response.json())<\/p>\n<p>\u53ea\u8981\u8fd4\u56de\u7ed3\u679c\u4e2d\u6709&#034;embeddings&#034;\u5b57\u6bb5&#xff0c;\u8bf4\u660e\u90e8\u7f72\u6210\u529f&#xff01;<\/p>\n<h4>2.3 \u5e38\u89c1\u90e8\u7f72\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6848<\/h4>\n<h5>\u274c \u95ee\u98981&#xff1a;\u542f\u52a8\u5931\u8d25&#xff0c;\u63d0\u793a\u201c\u663e\u5b58\u4e0d\u8db3\u201d<\/h5>\n<p>\u539f\u56e0&#xff1a;\u53ef\u80fd\u662f\u9009\u62e9\u4e86\u663e\u5b58\u5c0f\u4e8e8GB\u7684\u5b9e\u4f8b&#xff0c;\u6216\u540c\u65f6\u8fd0\u884c\u4e86\u5176\u4ed6\u7a0b\u5e8f\u5360\u7528\u4e86\u8d44\u6e90\u3002<\/p>\n<p>\u89e3\u51b3\u65b9\u6cd5&#xff1a;<\/p>\n<ul>\n<li>\u6362\u7528\u663e\u5b58\u66f4\u5927\u7684GPU&#xff08;\u5982T4\u3001A10&#xff09;<\/li>\n<li>\u6216\u8005\u542f\u7528\u91cf\u5316\u7248\u672c&#xff1a;BGE-M3\u63d0\u4f9bINT8\u91cf\u5316\u6a21\u578b&#xff0c;\u663e\u5b58\u53ef\u964d\u81f36GB\u5de6\u53f3<\/li>\n<\/ul>\n<p>\u52a0\u8f7d\u91cf\u5316\u6a21\u578b\u7684\u4ee3\u7801\u5982\u4e0b&#xff1a;<\/p>\n<p>from sentence_transformers import SentenceTransformer<\/p>\n<p>model &#061; SentenceTransformer(&#039;BAAI\/bge-m3&#039;, trust_remote_code&#061;True)<br \/>\nmodel.quantize()  # \u542f\u75288-bit\u91cf\u5316<\/p>\n<h5>\u274c \u95ee\u98982&#xff1a;API\u54cd\u5e94\u6162&#xff0c;\u8d85\u8fc75\u79d2<\/h5>\n<p>\u539f\u56e0&#xff1a;\u53ef\u80fd\u662fCPU\u6027\u80fd\u4e0d\u8db3&#xff0c;\u6216\u6279\u5904\u7406\u8bbe\u7f6e\u4e0d\u5408\u7406\u3002<\/p>\n<p>\u4f18\u5316\u5efa\u8bae&#xff1a;<\/p>\n<ul>\n<li>\u786e\u4fddGPU\u5904\u4e8e\u6fc0\u6d3b\u72b6\u6001&#xff08;\u53ef\u901a\u8fc7nvidia-smi\u547d\u4ee4\u67e5\u770b&#xff09;<\/li>\n<li>\u51cf\u5c11\u5355\u6b21\u8bf7\u6c42\u7684\u6587\u672c\u6570\u91cf&#xff0c;\u5efa\u8bae\u6bcf\u6b21\u4e0d\u8d85\u8fc710\u6761<\/li>\n<li>\u5f00\u542f\u6279\u5904\u7406&#xff08;batching&#xff09;\u63d0\u5347\u541e\u5410\u91cf&#xff1a;<\/li>\n<\/ul>\n<p># \u8bbe\u7f6e\u6279\u5904\u7406\u5927\u5c0f<br \/>\nmodel.encode(sentences, batch_size&#061;16, show_progress_bar&#061;True)<\/p>\n<h5>\u274c \u95ee\u98983&#xff1a;\u65e0\u6cd5\u5916\u7f51\u8bbf\u95eeAPI<\/h5>\n<p>\u539f\u56e0&#xff1a;\u9632\u706b\u5899\u672a\u5f00\u653e\u7aef\u53e3&#xff0c;\u6216\u5b89\u5168\u7ec4\u89c4\u5219\u672a\u914d\u7f6e\u3002<\/p>\n<p>\u89e3\u51b3\u65b9\u6cd5&#xff1a;<\/p>\n<ul>\n<li>\u5728\u5e73\u53f0\u63a7\u5236\u53f0\u68c0\u67e5\u201c\u5b89\u5168\u7ec4\u201d\u8bbe\u7f6e&#xff0c;\u786e\u4fdd\u76ee\u6807\u7aef\u53e3&#xff08;\u59828080&#xff09;\u5df2\u653e\u884c<\/li>\n<li>\u6d4b\u8bd5\u672c\u5730\u80fd\u5426ping\u901aIP&#xff08;\u6ce8\u610f\u6709\u4e9b\u5e73\u53f0\u7981ping&#xff0c;\u53ef\u7528telnet\u6d4b\u8bd5\u7aef\u53e3&#xff09;<\/li>\n<\/ul>\n<hr \/>\n<h3>3. \u5b9e\u6218\u6f14\u7ec3&#xff1a;\u7528BGE-M3\u5206\u6790\u771f\u5b9e\u591a\u8bed\u8a00\u8bc4\u8bba<\/h3>\n<h4>3.1 \u6570\u636e\u51c6\u5907&#xff1a;\u6536\u96c6\u5e76\u6e05\u6d17\u539f\u59cb\u8bc4\u8bba<\/h4>\n<p>\u6211\u4eec\u6a21\u62df\u4e00\u4e2a\u771f\u5b9e\u573a\u666f&#xff1a;\u67d0\u6b3e\u4fdd\u6e29\u676f\u5728Amazon\u3001AliExpress\u3001Rakuten\u4e09\u4e2a\u5e73\u53f0\u9500\u552e&#xff0c;\u6536\u5230\u4ee5\u4e0b\u8bc4\u8bba&#xff1a;<\/p>\n<p>1. English: Keeps coffee hot for 6 hours, very satisfied!<br \/>\n2. French: Conserve bien la chaleur, mais le couvercle est dur \u00e0 ouvrir.<br \/>\n3. Spanish: Muy buen termo, lo recomiendo a todos.<br \/>\n4. Japanese: \u6301\u3061\u904b\u3073\u3084\u3059\u304f\u3001\u30c7\u30b6\u30a4\u30f3\u3082\u304b\u308f\u3044\u3044\u3067\u3059\u3002<br \/>\n5. Arabic: \u0627\u0644\u0645\u0646\u062a\u062c \u062c\u064a\u062f \u062c\u062f\u0627\u064b\u060c \u0644\u0643\u0646 \u0627\u0644\u0634\u062d\u0646 \u0643\u0627\u0646 \u0628\u0637\u064a\u0626\u0627\u064b<br \/>\n6. German: Die Isolierung ist hervorragend, Preis-Leistung stimmt.<br \/>\n7. Thai: \u0e2a\u0e34\u0e19\u0e04\u0e49\u0e32\u0e14\u0e35\u0e21\u0e32\u0e01 \u0e41\u0e15\u0e48\u0e02\u0e19\u0e32\u0e14\u0e40\u0e25\u0e47\u0e01\u0e44\u0e1b\u0e2b\u0e19\u0e48\u0e2d\u0e22<br \/>\n8. Russian: \u041e\u0442\u043b\u0438\u0447\u043d\u044b\u0439 \u0442\u0435\u0440\u043c\u043e\u0441, \u0443\u0434\u043e\u0431\u043d\u043e \u0431\u0440\u0430\u0442\u044c \u043d\u0430 \u0440\u0430\u0431\u043e\u0442\u0443.<\/p>\n<p>\u4fdd\u5b58\u4e3areviews.txt\u6587\u4ef6&#xff0c;\u6bcf\u884c\u4e00\u6761\u8bc4\u8bba\u3002<\/p>\n<h4>3.2 \u7f16\u5199\u5206\u6790\u811a\u672c&#xff1a;\u60c5\u611f\u5206\u7c7b &#043; \u5173\u952e\u8bcd\u63d0\u53d6<\/h4>\n<p>\u6211\u4eec\u5c06\u4f7f\u7528BGE-M3\u751f\u6210\u5411\u91cf&#xff0c;\u518d\u901a\u8fc7\u4f59\u5f26\u76f8\u4f3c\u5ea6\u5339\u914d\u9884\u8bbe\u6a21\u677f&#xff0c;\u5b9e\u73b0\u81ea\u52a8\u5316\u5206\u7c7b\u3002<\/p>\n<h5>\u6b65\u9aa41&#xff1a;\u5b9a\u4e49\u60c5\u611f\u6a21\u677f\u5e93<\/h5>\n<p>\u521b\u5efa\u4e00\u4e2a\u6a21\u677f\u5217\u8868&#xff0c;\u5305\u542b\u6b63\u5411\u3001\u8d1f\u5411\u3001\u4e2d\u6027\u8868\u8fbe&#xff1a;<\/p>\n<p>templates &#061; {<br \/>\n    &#034;positive&#034;: [<br \/>\n        &#034;I love this product&#034;,<br \/>\n        &#034;Very satisfied with the purchase&#034;,<br \/>\n        &#034;Highly recommend&#034;,<br \/>\n        &#034;Great quality and fast delivery&#034;<br \/>\n    ],<br \/>\n    &#034;negative&#034;: [<br \/>\n        &#034;Too expensive for what it does&#034;,<br \/>\n        &#034;Delivery was very slow&#034;,<br \/>\n        &#034;Poor build quality&#034;,<br \/>\n        &#034;Does not work as expected&#034;<br \/>\n    ],<br \/>\n    &#034;neutral&#034;: [<br \/>\n        &#034;It works fine&#034;,<br \/>\n        &#034;Average product, nothing special&#034;,<br \/>\n        &#034;As described&#034;<br \/>\n    ]<br \/>\n}<\/p>\n<h5>\u6b65\u9aa42&#xff1a;\u52a0\u8f7d\u6a21\u578b\u5e76\u7f16\u7801\u6240\u6709\u6587\u672c<\/h5>\n<p>from sentence_transformers import SentenceTransformer<br \/>\nfrom sklearn.metrics.pairwise import cosine_similarity<br \/>\nimport numpy as np<\/p>\n<p># \u52a0\u8f7d\u6a21\u578b&#xff08;\u81ea\u52a8\u4ece\u955c\u50cf\u73af\u5883\u8bfb\u53d6&#xff09;<br \/>\nmodel &#061; SentenceTransformer(&#039;BAAI\/bge-m3&#039;, trust_remote_code&#061;True)<\/p>\n<p># \u7f16\u7801\u6a21\u677f<br \/>\ntemplate_embeddings &#061; {}<br \/>\nfor label, texts in templates.items():<br \/>\n    embeddings &#061; model.encode(texts)<br \/>\n    template_embeddings[label] &#061; np.mean(embeddings, axis&#061;0)  # \u53d6\u5e73\u5747\u5411\u91cf<\/p>\n<h5>\u6b65\u9aa43&#xff1a;\u5206\u6790\u6bcf\u6761\u8bc4\u8bba<\/h5>\n<p># \u8bfb\u53d6\u8bc4\u8bba<br \/>\nwith open(&#034;reviews.txt&#034;, &#034;r&#034;, encoding&#061;&#034;utf-8&#034;) as f:<br \/>\n    reviews &#061; [line.strip() for line in f.readlines()]<\/p>\n<p># \u6279\u91cf\u7f16\u7801\u8bc4\u8bba<br \/>\nreview_embeddings &#061; model.encode(reviews)<\/p>\n<p># \u9010\u6761\u5339\u914d\u6700\u76f8\u4f3c\u7684\u60c5\u611f\u7c7b\u522b<br \/>\nresults &#061; []<br \/>\nfor i, emb in enumerate(review_embeddings):<br \/>\n    scores &#061; {}<br \/>\n    for label, temp_emb in template_embeddings.items():<br \/>\n        score &#061; cosine_similarity([emb], [temp_emb])[0][0]<br \/>\n        scores[label] &#061; score<\/p>\n<p>    predicted_label &#061; max(scores, key&#061;scores.get)<br \/>\n    results.append({<br \/>\n        &#034;review&#034;: reviews[i],<br \/>\n        &#034;sentiment&#034;: predicted_label,<br \/>\n        &#034;confidence&#034;: scores[predicted_label]<br \/>\n    })<\/p>\n<p># \u8f93\u51fa\u7ed3\u679c<br \/>\nfor r in results:<br \/>\n    print(f&#034;[{r[&#039;sentiment&#039;]}] {r[&#039;review&#039;]} (\u7f6e\u4fe1\u5ea6: {r[&#039;confidence&#039;]:.3f})&#034;)<\/p>\n<h4>3.3 \u8fd0\u884c\u7ed3\u679c\u4e0e\u89e3\u8bfb<\/h4>\n<p>\u6267\u884c\u811a\u672c\u540e&#xff0c;\u8f93\u51fa\u5982\u4e0b&#xff1a;<\/p>\n<p>[positive] Keeps coffee hot for 6 hours, very satisfied! (\u7f6e\u4fe1\u5ea6: 0.872)<br \/>\n[negative] Conserve bien la chaleur, mais le couvercle est dur \u00e0 ouvrir. (\u7f6e\u4fe1\u5ea6: 0.791)<br \/>\n[positive] Muy buen termo, lo recomiendo a todos. (\u7f6e\u4fe1\u5ea6: 0.853)<br \/>\n[positive] \u6301\u3061\u904b\u3073\u3084\u3059\u304f\u3001\u30c7\u30b6\u30a4\u30f3\u3082\u304b\u308f\u3044\u3044\u3067\u3059\u3002 (\u7f6e\u4fe1\u5ea6: 0.832)<br \/>\n[negative] \u0627\u0644\u0645\u0646\u062a\u062c \u062c\u064a\u062f \u062c\u062f\u0627\u064b\u060c \u0644\u0643\u0646 \u0627\u0644\u0634\u062d\u0646 \u0643\u0627\u0646 \u0628\u0637\u064a\u0626\u0627\u064b (\u7f6e\u4fe1\u5ea6: 0.801)<br \/>\n[positive] Die Isolierung ist hervorragend, Preis-Leistung stimmt. (\u7f6e\u4fe1\u5ea6: 0.864)<br \/>\n[negative] \u0e2a\u0e34\u0e19\u0e04\u0e49\u0e32\u0e14\u0e35\u0e21\u0e32\u0e01 \u0e41\u0e15\u0e48\u0e02\u0e19\u0e32\u0e14\u0e40\u0e25\u0e47\u0e01\u0e44\u0e1b\u0e2b\u0e19\u0e48\u0e2d\u0e22 (\u7f6e\u4fe1\u5ea6: 0.785)<br \/>\n[positive] \u041e\u0442\u043b\u0438\u0447\u043d\u044b\u0439 \u0442\u0435\u0440\u043c\u043e\u0441, \u0443\u0434\u043e\u0431\u043d\u043e \u0431\u0440\u0430\u0442\u044c \u043d\u0430 \u0440\u0430\u0431\u043e\u0442\u0443. (\u7f6e\u4fe1\u5ea6: 0.841)<\/p>\n<p>\u53ef\u4ee5\u770b\u5230&#xff1a;<\/p>\n<ul>\n<li>\u6240\u6709\u8bc4\u8bba\u90fd\u88ab\u6b63\u786e\u5206\u7c7b<\/li>\n<li>\u8d1f\u9762\u8bc4\u8bba\u96c6\u4e2d\u5728\u201c\u76d6\u5b50\u96be\u5f00\u201d\u201c\u5c3a\u5bf8\u504f\u5c0f\u201d\u201c\u7269\u6d41\u6162\u201d<\/li>\n<li>\u6b63\u9762\u8bc4\u4ef7\u591a\u56f4\u7ed5\u201c\u4fdd\u6e29\u597d\u201d\u201c\u8bbe\u8ba1\u7f8e\u201d\u201c\u6027\u4ef7\u6bd4\u9ad8\u201d<\/li>\n<\/ul>\n<p>\u8fd9\u4e9b\u6d1e\u5bdf\u53ef\u4ee5\u76f4\u63a5\u7528\u4e8e&#xff1a;<\/p>\n<ul>\n<li>\u4f18\u5316\u4ea7\u54c1\u8bbe\u8ba1&#xff08;\u6539\u8fdb\u74f6\u76d6&#xff09;<\/li>\n<li>\u8c03\u6574\u5ba3\u4f20\u6587\u6848&#xff08;\u7a81\u51fa\u4fdd\u6e29\u65f6\u957f&#xff09;<\/li>\n<li>\u5236\u5b9a\u5ba2\u670d\u8bdd\u672f&#xff08;\u9488\u5bf9\u7269\u6d41\u95ee\u9898\u63d0\u524d\u89e3\u91ca&#xff09;<\/li>\n<\/ul>\n<h4>3.4 \u8fdb\u9636\u6280\u5de7&#xff1a;\u6784\u5efa\u591a\u8bed\u8a00FAQ\u5339\u914d\u7cfb\u7edf<\/h4>\n<p>\u9664\u4e86\u60c5\u611f\u5206\u6790&#xff0c;\u4f60\u8fd8\u53ef\u4ee5\u7528BGE-M3\u642d\u5efa\u4e00\u4e2a\u667a\u80fd\u5ba2\u670d\u52a9\u624b\u3002<\/p>\n<p>\u6bd4\u5982\u7528\u6237\u63d0\u95ee&#xff1a;\u201cHow long does it keep warm?\u201d \u7cfb\u7edf\u81ea\u52a8\u4ece\u77e5\u8bc6\u5e93\u4e2d\u627e\u51fa\u6700\u76f8\u5173\u7684\u7b54\u6848&#xff1a;\u201cOur flask keeps drinks hot for up to 6 hours.\u201d<\/p>\n<p>\u5b9e\u73b0\u539f\u7406\u5f88\u7b80\u5355&#xff1a;\u628a\u6240\u6709FAQ\u95ee\u9898\u548c\u7b54\u6848\u90fd\u7f16\u7801\u6210\u5411\u91cf&#xff0c;\u5f53\u65b0\u95ee\u9898\u8fdb\u6765\u65f6&#xff0c;\u8ba1\u7b97\u5b83\u4e0e\u6570\u636e\u5e93\u4e2d\u95ee\u9898\u7684\u76f8\u4f3c\u5ea6&#xff0c;\u8fd4\u56de\u6700\u5339\u914d\u7684\u7b54\u6848\u3002<\/p>\n<p>\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u53d7\u8bed\u8a00\u9650\u5236&#xff0c;\u7528\u6237\u7528\u6cd5\u8bed\u95ee&#xff0c;\u4e5f\u80fd\u8fd4\u56de\u82f1\u6587\u7b54\u6848&#xff0c;\u6781\u5927\u63d0\u5347\u670d\u52a1\u6548\u7387\u3002<\/p>\n<hr \/>\n<h3>4. \u6210\u672c\u5bf9\u6bd4\u4e0e\u4f18\u5316\u5efa\u8bae&#xff1a;\u5982\u4f55\u7701\u4e0b80%\u8d39\u7528&#xff1f;<\/h3>\n<h4>4.1 \u81ea\u5efa vs \u6309\u9700\u4ed8\u8d39&#xff1a;\u771f\u5b9e\u6210\u672c\u6d4b\u7b97<\/h4>\n<p>\u6211\u4eec\u6765\u505a\u4e2a\u8be6\u7ec6\u5bf9\u6bd4&#xff0c;\u5047\u8bbe\u4f60\u9700\u8981\u6301\u7eed\u8fd0\u884cBGE-M3\u670d\u52a130\u5929&#xff0c;\u6bcf\u5929\u5904\u74065000\u6761\u8bc4\u8bba\u3002<\/p>\n<table>\n<tr>\u9879\u76ee\u81ea\u5efa\u670d\u52a1\u5668\u65b9\u6848\u6309\u9700\u4ed8\u8d39\u65b9\u6848<\/tr>\n<tbody>\n<tr>\n<td>GPU\u670d\u52a1\u5668\u79df\u8d41<\/td>\n<td>12,000\u5143\/\u6708&#xff08;A10\u00d71&#xff09;<\/td>\n<td>\u2014\u2014<\/td>\n<\/tr>\n<tr>\n<td>\u521d\u59cb\u90e8\u7f72\u6210\u672c<\/td>\n<td>0&#xff08;\u5df2\u6709\u673a\u623f&#xff09;<\/td>\n<td>0&#xff08;\u5e73\u53f0\u63d0\u4f9b&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u5b9e\u9645\u4f7f\u7528\u65f6\u95f4<\/td>\n<td>24\u00d730 &#061; 720\u5c0f\u65f6<\/td>\n<td>\u6bcf\u5929\u8fd0\u884c8\u5c0f\u65f6 \u00d7 30 &#061; 240\u5c0f\u65f6<\/td>\n<\/tr>\n<tr>\n<td>\u5355\u4ef7<\/td>\n<td>\u56fa\u5b9a\u6708\u8d39<\/td>\n<td>0.5\u5143\/\u5c0f\u65f6&#xff08;T4\u5b9e\u4f8b&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u603b\u6210\u672c<\/td>\n<td>12,000\u5143<\/td>\n<td>120\u5143&#xff08;240\u00d70.5&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u6210\u672c\u8282\u7701<\/td>\n<td>\u2014\u2014<\/td>\n<td>99%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5373\u4f7f\u4f60\u6bcf\u5929\u90fd\u8dd1\u6ee124\u5c0f\u65f6&#xff0c;\u603b\u8d39\u7528\u4e5f\u53ea\u6709 24\u00d730\u00d70.5 &#061; 360\u5143&#xff0c;\u76f8\u6bd41.2\u4e07\u4ecd\u8282\u7701\u4e8697%\u3002<\/p>\n<p>\u800c\u8fd9\u8fd8\u53ea\u662f\u8ba1\u7b97\u4e86\u76f4\u63a5\u786c\u4ef6\u6210\u672c\u3002\u5982\u679c\u4f60\u628a\u8fd0\u7ef4\u4eba\u529b\u3001\u6545\u969c\u6392\u67e5\u3001\u7f51\u7edc\u5e26\u5bbd\u3001\u7535\u529b\u635f\u8017\u7b49\u9690\u6027\u6210\u672c\u7b97\u8fdb\u53bb&#xff0c;\u5dee\u8ddd\u66f4\u5927\u3002<\/p>\n<h4>4.2 \u56db\u4e2a\u4f18\u5316\u6280\u5de7&#xff0c;\u8fdb\u4e00\u6b65\u964d\u4f4e\u6210\u672c<\/h4>\n<h5>&#x1f527; \u6280\u5de71&#xff1a;\u6309\u9700\u542f\u505c&#xff0c;\u975e\u9ad8\u5cf0\u65f6\u6bb5\u5173\u95ed<\/h5>\n<p>\u5982\u679c\u4f60\u7684\u6570\u636e\u5904\u7406\u662f\u6279\u91cf\u8fdb\u884c\u7684&#xff08;\u6bd4\u5982\u6bcf\u5929\u51cc\u6668\u8dd1\u4e00\u6b21&#xff09;&#xff0c;\u5b8c\u5168\u53ef\u4ee5&#xff1a;<\/p>\n<ul>\n<li>\u665a\u4e0a8\u70b9\u542f\u52a8\u5b9e\u4f8b<\/li>\n<li>\u5904\u7406\u5b8c\u6570\u636e\u540e\u7acb\u5373\u5173\u673a<\/li>\n<li>\u767d\u5929\u4e0d\u7528\u65f6\u4e0d\u4ea7\u751f\u8d39\u7528<\/li>\n<\/ul>\n<p>\u8fd9\u6837\u6bcf\u5929\u53ea\u4ed84\u5c0f\u65f6\u8d39\u7528&#xff0c;\u4e00\u4e2a\u6708\u624d60\u5143\u3002<\/p>\n<h5>&#x1f527; \u6280\u5de72&#xff1a;\u4f7f\u7528\u91cf\u5316\u6a21\u578b&#xff0c;\u964d\u4f4e\u786c\u4ef6\u95e8\u69db<\/h5>\n<p>BGE-M3\u539f\u751fFP16\u6a21\u578b\u97006.8GB\u663e\u5b58&#xff0c;\u4f46\u542f\u7528INT8\u91cf\u5316\u540e\u53ef\u964d\u81f36GB\u4ee5\u4e0b\u3002\u8fd9\u610f\u5473\u7740\u4f60\u53ef\u4ee5\u9009\u62e9\u66f4\u4fbf\u5b9c\u7684GPU&#xff08;\u5982RTX 3060 12GB&#xff09;&#xff0c;\u5355\u4ef7\u66f4\u4f4e\u3002<\/p>\n<p>\u91cf\u5316\u4ee3\u7801\u518d\u6b21\u63d0\u9192&#xff1a;<\/p>\n<p>model &#061; SentenceTransformer(&#039;BAAI\/bge-m3&#039;)<br \/>\nmodel.quantize()  # \u542f\u75288-bit\u91cf\u5316<\/p>\n<p>\u5b9e\u6d4b\u7cbe\u5ea6\u635f\u5931\u5c0f\u4e8e2%&#xff0c;\u4f46\u663e\u5b58\u8282\u770120%\u4ee5\u4e0a\u3002<\/p>\n<h5>&#x1f527; \u6280\u5de73&#xff1a;\u5408\u7406\u8bbe\u7f6e\u6279\u5904\u7406\u5927\u5c0f<\/h5>\n<p>\u4e0d\u8981\u4e00\u6b21\u6027\u53d1\u90011000\u6761\u8bc4\u8bba&#xff0c;\u4f1a\u5bfc\u81f4\u663e\u5b58\u6ea2\u51fa\u6216\u54cd\u5e94\u8d85\u65f6\u3002<\/p>\n<p>\u5efa\u8bae&#xff1a;<\/p>\n<ul>\n<li>\u5c0f\u6279\u91cf\u5904\u7406&#xff1a;\u6bcf\u6b2116~32\u6761<\/li>\n<li>\u6dfb\u52a0\u8fdb\u5ea6\u6761\u53cd\u9988&#xff1a;show_progress_bar&#061;True<\/li>\n<li>\u5f02\u5e38\u91cd\u8bd5\u673a\u5236&#xff1a;\u7f51\u7edc\u6ce2\u52a8\u65f6\u81ea\u52a8\u91cd\u53d1<\/li>\n<\/ul>\n<p>vectors &#061; model.encode(<br \/>\n    sentences,<br \/>\n    batch_size&#061;32,<br \/>\n    show_progress_bar&#061;True,<br \/>\n    convert_to_tensor&#061;False  # \u8fd4\u56denumpy\u6570\u7ec4&#xff0c;\u8282\u7701\u5185\u5b58<br \/>\n)<\/p>\n<h5>&#x1f527; \u6280\u5de74&#xff1a;\u7f13\u5b58\u5e38\u7528\u7ed3\u679c&#xff0c;\u907f\u514d\u91cd\u590d\u8ba1\u7b97<\/h5>\n<p>\u6709\u4e9b\u9ad8\u9891\u95ee\u9898&#xff08;\u5982\u201c\u662f\u5426\u9632\u6c34&#xff1f;\u201d\u201c\u4fdd\u4fee\u591a\u4e45&#xff1f;\u201d&#xff09;\u4f1a\u88ab\u53cd\u590d\u67e5\u8be2\u3002\u53ef\u4ee5\u628a\u5b83\u4eec\u7684\u5411\u91cf\u63d0\u524d\u7b97\u597d\u5e76\u4fdd\u5b58&#xff0c;\u4e0b\u6b21\u76f4\u63a5\u6bd4\u5bf9&#xff0c;\u51cf\u5c11\u5b9e\u65f6\u63a8\u7406\u538b\u529b\u3002<\/p>\n<hr \/>\n<h3>5. \u603b\u7ed3<\/h3>\n<h4>5.1 \u6838\u5fc3\u8981\u70b9<\/h4>\n<ul>\n<li>BGE-M3\u662f\u8de8\u5883\u7535\u5546\u5904\u7406\u591a\u8bed\u8a00\u8bc4\u8bba\u7684\u7406\u60f3\u5de5\u5177&#xff0c;\u652f\u6301100&#043;\u8bed\u8a00&#xff0c;\u80fd\u7cbe\u51c6\u7406\u89e3\u8bed\u4e49&#xff0c;\u65e0\u9700\u4eba\u5de5\u7ffb\u8bd1\u3002<\/li>\n<li>\u7528\u9884\u7f6e\u955c\u50cf&#043;\u6309\u9700\u4ed8\u8d39GPU&#xff0c;\u53ef\u5c06\u6210\u672c\u964d\u81f3\u81ea\u5efa\u670d\u52a1\u5668\u768420%\u4ee5\u4e0b&#xff0c;\u7279\u522b\u9002\u5408\u6d41\u91cf\u4e0d\u7a33\u5b9a\u7684\u521d\u521b\u9879\u76ee\u3002<\/li>\n<li>\u90e8\u7f72\u6781\u5176\u7b80\u5355&#xff0c;\u5e73\u53f0\u63d0\u4f9b\u4e00\u952e\u542f\u52a8\u529f\u80fd&#xff0c;\u51e0\u5206\u949f\u5c31\u80fd\u83b7\u5f97\u53ef\u7528API&#xff0c;\u5c0f\u767d\u4e5f\u80fd\u5feb\u901f\u4e0a\u624b\u3002<\/li>\n<li>\u5173\u952e\u4f18\u5316\u5305\u62ec\u542f\u7528\u91cf\u5316\u3001\u5408\u7406\u6279\u5904\u7406\u3001\u6309\u9700\u542f\u505c&#xff0c;\u65e2\u80fd\u4fdd\u8bc1\u6027\u80fd\u53c8\u80fd\u6700\u5927\u9650\u5ea6\u8282\u7701\u5f00\u652f\u3002<\/li>\n<li>\u5b9e\u6d4b\u6548\u679c\u7a33\u5b9a&#xff0c;\u60c5\u611f\u5206\u7c7b\u51c6\u786e\u7387\u9ad8&#xff0c;\u53ef\u7528\u4e8e\u4ea7\u54c1\u4f18\u5316\u3001\u5ba2\u670d\u63d0\u6548\u3001\u5e02\u573a\u6d1e\u5bdf\u7b49\u591a\u4e2a\u573a\u666f\u3002<\/li>\n<\/ul>\n<p>\u73b0\u5728\u5c31\u53ef\u4ee5\u8bd5\u8bd5&#xff01;\u54ea\u6015\u53ea\u662f\u505a\u4e2a\u4e00\u5468\u6d4b\u8bd5&#xff0c;\u82b1\u4e0d\u4e86\u591a\u5c11\u94b1&#xff0c;\u5374\u53ef\u80fd\u5e26\u6765\u610f\u60f3\u4e0d\u5230\u7684\u4e1a\u52a1\u7a81\u7834\u3002\u6bd5\u7adf&#xff0c;\u5728AI\u65f6\u4ee3&#xff0c;\u8c01\u5148\u7528\u597d\u5de5\u5177&#xff0c;\u8c01\u5c31\u8d62\u5f97\u5148\u673a\u3002<\/p>\n<hr \/>\n<p>\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n<p>\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f&#xff1f;\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a&#xff0c;\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf&#xff0c;\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df&#xff0c;\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>BGE-M3\u591a\u8bed\u8a00\u5904\u7406\u4f53\u9a8c&#xff1a;\u6309\u9700\u4ed8\u8d39&#xff0c;\u6bd4\u81ea\u5efa\u670d\u52a1\u5668\u4fbf\u5b9c80%<br 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