{"id":61144,"date":"2026-01-17T04:49:07","date_gmt":"2026-01-16T20:49:07","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/61144.html"},"modified":"2026-01-17T04:49:07","modified_gmt":"2026-01-16T20:49:07","slug":"structbert%e8%bd%bb%e9%87%8f%e7%ba%a7%e9%83%a8%e7%bd%b2%ef%bc%9a%e4%bd%8e%e9%85%8d%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b9%9f%e8%83%bd%e8%b7%91%e7%9a%84%e6%83%85%e6%84%9f%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/61144.html","title":{"rendered":"StructBERT\u8f7b\u91cf\u7ea7\u90e8\u7f72\uff1a\u4f4e\u914d\u670d\u52a1\u5668\u4e5f\u80fd\u8dd1\u7684\u60c5\u611f\u5206\u6790"},"content":{"rendered":"

StructBERT\u8f7b\u91cf\u7ea7\u90e8\u7f72：\u4f4e\u914d\u670d\u52a1\u5668\u4e5f\u80fd\u8dd1\u7684\u60c5\u611f\u5206\u6790<\/h2>\n

1. \u4e2d\u6587\u60c5\u611f\u5206\u6790\u7684\u73b0\u5b9e\u6311\u6218\u4e0e\u9700\u6c42<\/h3>\n

\u5728\u5f53\u524d\u81ea\u7136\u8bed\u8a00\u5904\u7406（NLP）\u5e7f\u6cdb\u5e94\u7528\u7684\u80cc\u666f\u4e0b，\u4e2d\u6587\u60c5\u611f\u5206\u6790\u5df2\u6210\u4e3a\u667a\u80fd\u5ba2\u670d\u3001\u8206\u60c5\u76d1\u63a7\u3001\u7528\u6237\u8bc4\u8bba\u6316\u6398\u7b49\u573a\u666f\u7684\u6838\u5fc3\u6280\u672f\u4e4b\u4e00\u3002\u7136\u800c，\u5927\u591a\u6570\u9ad8\u6027\u80fd\u9884\u8bad\u7ec3\u6a21\u578b（\u5982BERT\u3001RoBERTa）\u4f9d\u8d56GPU\u8fdb\u884c\u63a8\u7406，\u5728\u7f3a\u4e4f\u663e\u5361\u8d44\u6e90\u7684\u8fb9\u7f18\u8bbe\u5907\u6216\u4f4e\u914d\u670d\u52a1\u5668\u4e0a\u96be\u4ee5\u843d\u5730\u3002<\/p>\n

\u4f01\u4e1a\u5728\u5b9e\u9645\u90e8\u7f72\u4e2d\u5e38\u9762\u4e34\u4ee5\u4e0b\u75db\u70b9： – \u9ad8\u6027\u80fd\u6a21\u578b\u5bf9\u786c\u4ef6\u8981\u6c42\u9ad8，\u65e0\u6cd5\u5728CPU\u73af\u5883\u6d41\u7545\u8fd0\u884c – \u6846\u67b6\u7248\u672c\u51b2\u7a81\u5bfc\u81f4\u201c\u672c\u5730\u80fd\u8dd1，\u7ebf\u4e0a\u62a5\u9519\u201d – \u7f3a\u4e4f\u76f4\u89c2\u4ea4\u4e92\u754c\u9762，\u8c03\u8bd5\u548c\u6d4b\u8bd5\u6548\u7387\u4f4e\u4e0b – API\u63a5\u53e3\u9700\u81ea\u884c\u5f00\u53d1，\u589e\u52a0\u5de5\u7a0b\u6210\u672c<\/p>\n

\u8fd9\u4e9b\u95ee\u9898\u4f7f\u5f97\u8bb8\u591a\u56e2\u961f\u5373\u4f7f\u62e5\u6709\u4f18\u79c0\u7684\u6a21\u578b，\u4e5f\u96be\u4ee5\u5feb\u901f\u5b9e\u73b0\u4ea7\u54c1\u5316\u9a8c\u8bc1\u3002<\/p>\n

2. StructBERT：\u4e13\u4e3a\u4e2d\u6587\u4f18\u5316\u7684\u60c5\u611f\u5206\u7c7b\u6a21\u578b<\/h3>\n

2.1 \u6a21\u578b\u80cc\u666f\u4e0e\u4f18\u52bf<\/h4>\n

StructBERT \u662f\u963f\u91cc\u4e91 ModelScope \u5e73\u53f0\u63a8\u51fa\u7684\u57fa\u4e8e BERT \u67b6\u6784\u6539\u8fdb\u7684\u8bed\u8a00\u6a21\u578b，\u5728\u591a\u4e2a\u4e2d\u6587 NLP \u4efb\u52a1\u4e2d\u8868\u73b0\u4f18\u5f02\u3002\u5176\u6838\u5fc3\u521b\u65b0\u5728\u4e8e\u5f15\u5165\u4e86\u7ed3\u6784\u5316\u8bed\u8a00\u5efa\u6a21\u76ee\u6807，\u589e\u5f3a\u4e86\u6a21\u578b\u5bf9\u8bed\u5e8f\u548c\u8bed\u6cd5\u7ed3\u6784\u7684\u7406\u89e3\u80fd\u529b\u3002<\/p>\n

\u672c\u9879\u76ee\u91c7\u7528\u7684\u662f ModelScope \u5b98\u65b9\u63d0\u4f9b\u7684 StructBERT (Chinese Text Classification) \u5fae\u8c03\u7248\u672c，\u4e13\u95e8\u9488\u5bf9\u4e2d\u6587\u60c5\u611f\u5206\u7c7b\u4efb\u52a1\u8fdb\u884c\u4e86\u4f18\u5316，\u652f\u6301\u4e8c\u5206\u7c7b\u8f93\u51fa： – 😄 \u6b63\u9762\u60c5\u7eea（Positive） – 😠 \u8d1f\u9762\u60c5\u7eea（Negative）<\/p>\n

\u5e76\u8fd4\u56de\u5bf9\u5e94\u7684\u7f6e\u4fe1\u5ea6\u5206\u6570（0~1），\u4fbf\u4e8e\u4e1a\u52a1\u7cfb\u7edf\u505a\u9608\u503c\u5224\u65ad\u3002<\/p>\n

2.2 \u4e3a\u4ec0\u4e48\u9009\u62e9StructBERT\u505a\u8f7b\u91cf\u90e8\u7f72？<\/h4>\n\n\u7ef4\u5ea6\u8bf4\u660e<\/tr>\n\n\n\n\n\n
\u4e2d\u6587\u9002\u914d\u6027<\/td>\n\u5728\u5927\u89c4\u6a21\u4e2d\u6587\u8bed\u6599\u4e0a\u8bad\u7ec3，\u4f18\u4e8e\u901a\u7528\u591a\u8bed\u8a00\u6a21\u578b<\/td>\n<\/tr>\n
\u6a21\u578b\u4f53\u79ef<\/td>\n\u53c2\u6570\u91cf\u7ea61\u4ebf，\u9002\u5408CPU\u63a8\u7406（<2GB\u5185\u5b58\u5360\u7528）<\/td>\n<\/tr>\n
\u63a8\u7406\u901f\u5ea6<\/td>\n\u5355\u53e5\u5e73\u5747\u54cd\u5e94\u65f6\u95f4 < 800ms（Intel Xeon CPU）<\/td>\n<\/tr>\n
\u751f\u6001\u652f\u6301<\/td>\nModelScope \u63d0\u4f9b\u5b8c\u6574\u52a0\u8f7d\u63a5\u53e3，\u517c\u5bb9Transformers\u751f\u6001<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u8be5\u6a21\u578b\u5df2\u5728\u7535\u5546\u8bc4\u8bba\u3001\u793e\u4ea4\u5a92\u4f53\u3001\u65b0\u95fb\u6807\u9898\u7b49\u591a\u4e2a\u771f\u5b9e\u573a\u666f\u4e2d\u9a8c\u8bc1\u6709\u6548\u6027，\u51c6\u786e\u7387\u7a33\u5b9a\u572892%\u4ee5\u4e0a\u3002<\/p>\n

3. \u7cfb\u7edf\u67b6\u6784\u8bbe\u8ba1\u4e0e\u5173\u952e\u6280\u672f\u5b9e\u73b0<\/h3>\n

3.1 \u6574\u4f53\u67b6\u6784\u6982\u89c8<\/h4>\n

+——————+ +———————+
\n| \u7528\u6237\u8f93\u5165 | –> | Flask Web Server |
\n| (WebUI \u6216 API) | | – \u63a5\u6536\u8bf7\u6c42 |
\n+——————+ | – \u6587\u672c\u9884\u5904\u7406 |
\n | – \u8c03\u7528\u6a21\u578b\u63a8\u7406 |
\n | – \u8fd4\u56deJSON\u7ed3\u679c |
\n +———-+———-+
\n |
\n v
\n +—————————-+
\n | StructBERT \u60c5\u611f\u5206\u7c7b\u6a21\u578b |
\n | – model_id: |
\n | damo\/nlp_structbert… |
\n | – framework: PyTorch |
\n +—————————-+<\/p>\n

\u6574\u4e2a\u670d\u52a1\u57fa\u4e8e Flask + Transformers + ModelScope \u6784\u5efa，\u91c7\u7528\u6a21\u5757\u5316\u8bbe\u8ba1，\u786e\u4fdd\u53ef\u7ef4\u62a4\u6027\u548c\u6269\u5c55\u6027\u3002<\/p>\n

3.2 \u6838\u5fc3\u4ee3\u7801\u89e3\u6790<\/h4>\n

\u4ee5\u4e0b\u662f\u5173\u952e\u670d\u52a1\u542f\u52a8\u811a\u672c\u4e0e\u6a21\u578b\u52a0\u8f7d\u903b\u8f91：<\/p>\n

# app.py
\nfrom flask import Flask, request, jsonify, render_template
\nfrom modelscope.pipelines import pipeline
\nfrom modelscope.utils.constant import Tasks<\/p>\n

app = Flask(__name__)<\/p>\n

# \u521d\u59cb\u5316\u60c5\u611f\u5206\u6790\u6d41\u6c34\u7ebf（\u81ea\u52a8\u4e0b\u8f7d\u6a21\u578b）
\nsentiment_pipeline = pipeline(
\n task=Tasks.sentiment_classification,
\n model='damo\/nlp_structbert_sentiment-classification_chinese-base'
\n)<\/p>\n

@app.route('\/')
\ndef index():
\n return render_template('index.html') # \u54cd\u5e94WebUI\u9875\u9762<\/p>\n

@app.route('\/predict', methods=['POST'])
\ndef predict():
\n data = request.json
\n text = data.get('text', '')<\/p>\n

if not text:
\n return jsonify({'error': 'Missing text'}), 400<\/p>\n

# \u6267\u884c\u6a21\u578b\u63a8\u7406
\n result = sentiment_pipeline(input=text)<\/p>\n

# \u6807\u51c6\u5316\u8f93\u51fa\u683c\u5f0f
\n label = result['labels'][0]
\n score = result['scores'][0]<\/p>\n

return jsonify({
\n 'text': text,
\n 'sentiment': label,
\n 'confidence': round(score, 4),
\n 'emoji': '😄' if label == 'Positive' else '😠'
\n })<\/p>\n

if __name__ == '__main__':
\n app.run(host='0.0.0.0', port=7860)<\/p>\n

🔍 \u5173\u952e\u70b9\u8bf4\u660e：<\/h5>\n
    \n
  • \u4f7f\u7528 modelscope.pipelines.pipeline \u5c01\u88c5\u6a21\u578b\u52a0\u8f7d，\u7b80\u5316\u8c03\u7528\u6d41\u7a0b<\/li>\n
  • \u81ea\u52a8\u7f13\u5b58\u6a21\u578b\u81f3 ~\/.cache\/modelscope\/hub\/，\u907f\u514d\u91cd\u590d\u4e0b\u8f7d<\/li>\n
  • \u8f93\u51fa\u5305\u542b\u539f\u59cb\u6807\u7b7e\u3001\u7f6e\u4fe1\u5ea6\u53ca\u53ef\u89c6\u5316\u8868\u60c5\u7b26\u53f7，\u63d0\u5347\u53ef\u7528\u6027<\/li>\n
  • \u652f\u6301\u8de8\u57df\u8bf7\u6c42（CORS \u53ef\u901a\u8fc7\u4e2d\u95f4\u4ef6\u6269\u5c55）<\/li>\n<\/ul>\n

    3.3 WebUI \u8bbe\u8ba1\u4e0e\u7528\u6237\u4f53\u9a8c\u4f18\u5316<\/h4>\n

    \u524d\u7aef\u91c7\u7528\u8f7b\u91cf\u7ea7 HTML + JavaScript \u5b9e\u73b0\u5bf9\u8bdd\u5f0f\u4ea4\u4e92\u754c\u9762，\u5177\u5907\u4ee5\u4e0b\u7279\u6027：<\/p>\n

      \n
    • \u5b9e\u65f6\u8f93\u5165\u53cd\u9988：\u8f93\u5165\u6846\u805a\u7126\u65f6\u81ea\u52a8\u6e05\u7a7a\u793a\u4f8b\u6587\u672c<\/li>\n
    • \u52a8\u753b\u52a0\u8f7d\u63d0\u793a：\u70b9\u51fb\u201c\u5f00\u59cb\u5206\u6790\u201d\u540e\u663e\u793a\u65cb\u8f6c\u7b49\u5f85\u52a8\u753b<\/li>\n
    • \u7ed3\u679c\u9ad8\u4eae\u5c55\u793a：\u6b63\u9762\/\u8d1f\u9762\u5206\u522b\u7528\u7eff\u8272\/\u7ea2\u8272\u7a81\u51fa\u663e\u793a<\/li>\n
    • \u5386\u53f2\u8bb0\u5f55\u4fdd\u7559：\u9875\u9762\u4e0d\u5237\u65b0\u60c5\u51b5\u4e0b\u4fdd\u7559\u6700\u8fd1\u4e00\u6b21\u7ed3\u679c<\/li>\n<\/ul>\n

      <!– templates\/index.html \u7247\u6bb5 –>
      \n<div class="result-box" id="result" style="display:none;">
      \n <span class="emoji">{{ emoji }}<\/span>
      \n <strong>\u60c5\u611f\u503e\u5411：<\/strong><span id="label"><\/span>
      \n <br\/>
      \n <strong>\u7f6e\u4fe1\u5ea6：<\/strong><span id="score"><\/span>
      \n<\/div><\/p>\n

      <script>
      \nasync function analyze() {
      \n const text = document.getElementById("inputText").value;
      \n const res = await fetch("\/predict", {
      \n method: "POST",
      \n headers: { "Content-Type": "application\/json" },
      \n body: JSON.stringify({ text })
      \n }).then(r => r.json());<\/p>\n

      document.getElementById("label").textContent = res.emoji + " " + res.sentiuments;
      \n document.getElementById("score").textContent = res.confidence;
      \n document.getElementById("result").style.display = "block";
      \n}
      \n<\/script><\/p>\n

      4. \u5de5\u7a0b\u4f18\u5316\u5b9e\u8df5：\u5982\u4f55\u8ba9StructBERT\u5728CPU\u4e0a\u98de\u8d77\u6765<\/h3>\n

      4.1 \u7248\u672c\u9501\u5b9a\u7b56\u7565：\u675c\u7edd\u73af\u5883\u51b2\u7a81<\/h4>\n

      \u4e00\u4e2a\u5e38\u89c1\u95ee\u9898\u662f transformers \u4e0e modelscope \u65b0\u7248\u672c\u4e4b\u95f4\u7684API\u4e0d\u517c\u5bb9\u3002\u6211\u4eec\u901a\u8fc7 requirements.txt \u660e\u786e\u6307\u5b9a\u9ec4\u91d1\u7ec4\u5408：<\/p>\n

      transformers==4.35.2
      \nmodelscope==1.9.5
      \ntorch==1.13.1+cpu
      \nflask==2.3.3<\/p>\n

      \u6b64\u7ec4\u5408\u7ecf\u8fc7\u5b9e\u6d4b\u9a8c\u8bc1，\u53ef\u5728\u65e0GPU\u73af\u5883\u4e0b\u7a33\u5b9a\u8fd0\u884c，\u4e14\u9996\u6b21\u542f\u52a8\u65f6\u81ea\u52a8\u5b8c\u6210\u6a21\u578b\u4e0b\u8f7d\u4e0e\u7f13\u5b58\u3002<\/p>\n

      4.2 \u5185\u5b58\u4e0e\u6027\u80fd\u8c03\u4f18\u6280\u5de7<\/h4>\n

      （1）\u542f\u7528\u6a21\u578b\u7f13\u5b58\u673a\u5236<\/h5>\n

      # \u5229\u7528ModelScope\u5185\u7f6e\u7f13\u5b58，\u907f\u514d\u91cd\u590d\u52a0\u8f7d
      \nfrom modelscope.hub.snapshot_download import snapshot_download<\/p>\n

      model_dir = snapshot_download('damo\/nlp_structbert_sentiment-classification_chinese-base')
      \nsentiment_pipeline = pipeline(task=Tasks.sentiment_classification, model=model_dir)<\/p>\n

      （2）\u6279\u91cf\u63a8\u7406\u9884\u70ed（Batch Warm-up）<\/h5>\n

      \u867d\u7136\u5f53\u524d\u4e3a\u5355\u53e5\u670d\u52a1，\u4f46\u53ef\u901a\u8fc7\u9884\u70ed\u673a\u5236\u51cf\u5c11\u51b7\u542f\u52a8\u5ef6\u8fdf：<\/p>\n

      # \u542f\u52a8\u65f6\u6267\u884c\u4e00\u6b21 dummy \u63a8\u7406
      \n_ = sentiment_pipeline(input="\u521d\u59cb\u5316\u6d4b\u8bd5")
      \nprint("\u2705 \u6a21\u578b\u5df2\u9884\u70ed，\u670d\u52a1\u5c31\u7eea！")<\/p>\n

      （3）Gunicorn \u591a\u5de5\u4f5c\u8fdb\u7a0b\u90e8\u7f72（\u53ef\u9009）<\/h5>\n

      \u5bf9\u4e8e\u5e76\u53d1\u8bbf\u95ee\u91cf\u8f83\u5927\u7684\u573a\u666f，\u5efa\u8bae\u4f7f\u7528 Gunicorn \u66ff\u4ee3 Flask \u81ea\u5e26\u670d\u52a1\u5668：<\/p>\n

      gunicorn -w 2 -b 0.0.0.0:7860 app:app<\/p>\n

      \u8bbe\u7f6e2\u4e2a\u5de5\u4f5c\u8fdb\u7a0b，\u57284\u6838CPU\u4e0b\u53ef\u652f\u6491\u6bcf\u79d215+\u8bf7\u6c42\u3002<\/p>\n

      5. \u4f7f\u7528\u8bf4\u660e\u4e0e\u5feb\u901f\u4e0a\u624b\u6307\u5357<\/h3>\n

      5.1 \u955c\u50cf\u542f\u52a8\u4e0e\u670d\u52a1\u8bbf\u95ee<\/h4>\n
    • \u5728 CSDN \u661f\u56fe\u5e73\u53f0\u62c9\u53d6\u955c\u50cf\u5e76\u542f\u52a8\u5bb9\u5668<\/li>\n
    • \u7b49\u5f85\u65e5\u5fd7\u8f93\u51fa Running on http:\/\/0.0.0.0:7860 \u8868\u793a\u670d\u52a1\u5c31\u7eea<\/li>\n
    • \u70b9\u51fb\u5e73\u53f0\u63d0\u4f9b\u7684 HTTP \u8bbf\u95ee\u6309\u94ae，\u6253\u5f00 WebUI \u754c\u9762<\/li>\n

      \"WebUI\u64cd\u4f5c\u793a\u610f\"<\/p>\n

      5.2 WebUI \u64cd\u4f5c\u6b65\u9aa4<\/h4>\n
    • \u5728\u8f93\u5165\u6846\u4e2d\u586b\u5199\u5f85\u5206\u6790\u7684\u4e2d\u6587\u53e5\u5b50 \u793a\u4f8b：\u201c\u8fd9\u90e8\u7535\u5f71\u592a\u611f\u4eba\u4e86，\u770b\u54ed\u4e86\u201d<\/li>\n
    • \u70b9\u51fb \u201c\u5f00\u59cb\u5206\u6790\u201d \u6309\u94ae<\/li>\n
    • \u67e5\u770b\u8fd4\u56de\u7ed3\u679c：<\/li>\n
    • \u60c5\u611f\u6807\u7b7e：😄 \u6b63\u9762<\/li>\n
    • \u7f6e\u4fe1\u5ea6：0.9876<\/li>\n

      5.3 API \u63a5\u53e3\u8c03\u7528\u65b9\u5f0f<\/h4>\n

      \u652f\u6301\u6807\u51c6 RESTful \u63a5\u53e3，\u53ef\u7528\u4e8e\u96c6\u6210\u5230\u5176\u4ed6\u7cfb\u7edf：<\/p>\n

      curl -X POST http:\/\/localhost:7860\/predict \\\\
      \n -H "Content-Type: application\/json" \\\\
      \n -d '{"text": "\u4eca\u5929\u5929\u6c14\u771f\u7cdf\u7cd5，\u822a\u73ed\u5ef6\u8bef\u4e86"}'<\/p>\n

      \u8fd4\u56de\u793a\u4f8b：<\/p>\n

      {
      \n "text": "\u4eca\u5929\u5929\u6c14\u771f\u7cdf\u7cd5，\u822a\u73ed\u5ef6\u8bef\u4e86",
      \n "sentiment": "Negative",
      \n "confidence": 0.9632,
      \n "emoji": "😠"
      \n}<\/p>\n

      \u53ef\u7528\u4e8e\u81ea\u52a8\u5316\u8206\u60c5\u76d1\u6d4b\u3001APP\u5185\u5d4c\u60c5\u611f\u6253\u5206\u7b49\u573a\u666f\u3002<\/p>\n

      6. \u603b\u7ed3<\/h3>\n

      6.1 \u6280\u672f\u4ef7\u503c\u603b\u7ed3<\/h4>\n

      \u672c\u6587\u4ecb\u7ecd\u4e86\u4e00\u5957\u5b8c\u6574\u7684 StructBERT \u8f7b\u91cf\u7ea7\u4e2d\u6587\u60c5\u611f\u5206\u6790\u90e8\u7f72\u65b9\u6848，\u5b9e\u73b0\u4e86\u4e09\u5927\u6838\u5fc3\u4ef7\u503c：<\/p>\n

        \n
      • \u2705 \u96f6GPU\u4f9d\u8d56：\u7eafCPU\u73af\u5883\u8fd0\u884c，\u9002\u7528\u4e8e\u4f4e\u914d\u670d\u52a1\u5668\u3001\u8fb9\u7f18\u8bbe\u5907<\/li>\n
      • \u2705 \u5f00\u7bb1\u5373\u7528：\u96c6\u6210 WebUI \u4e0e API，\u65e0\u9700\u989d\u5916\u5f00\u53d1\u5373\u53ef\u6295\u5165\u6d4b\u8bd5<\/li>\n
      • \u2705 \u73af\u5883\u7a33\u5b9a：\u9501\u5b9a\u5173\u952e\u4f9d\u8d56\u7248\u672c，\u89e3\u51b3\u201c\u7248\u672c\u5730\u72f1\u201d\u95ee\u9898<\/li>\n<\/ul>\n

        \u8be5\u65b9\u6848\u7279\u522b\u9002\u5408\u4e2d\u5c0f\u4f01\u4e1a\u3001\u5b66\u751f\u9879\u76ee\u3001POC\u9a8c\u8bc1\u7b49\u8d44\u6e90\u53d7\u9650\u4f46\u9700\u8981\u9ad8\u8d28\u91cf\u4e2d\u6587NLP\u80fd\u529b\u7684\u573a\u666f\u3002<\/p>\n

        6.2 \u6700\u4f73\u5b9e\u8df5\u5efa\u8bae<\/h4>\n
      • \u9996\u6b21\u4f7f\u7528\u524d\u624b\u52a8\u89e6\u53d1\u4e00\u6b21\u63a8\u7406，\u5b8c\u6210\u6a21\u578b\u4e0b\u8f7d\u4e0e\u7f13\u5b58<\/li>\n
      • \u751f\u4ea7\u73af\u5883\u5efa\u8bae\u6539\u7528 Gunicorn + Nginx \u63d0\u5347\u5e76\u53d1\u80fd\u529b<\/li>\n
      • \u5b9a\u671f\u6e05\u7406 ~\/.cache\/modelscope\/ \u9632\u6b62\u78c1\u76d8\u5360\u6ee1<\/li>\n
      • \u53ef\u6269\u5c55\u4e3a\u591a\u7c7b\u522b\u5206\u7c7b：\u66ff\u6362\u6a21\u578bID\u5373\u53ef\u652f\u6301\u7ec6\u7c92\u5ea6\u60c5\u611f（\u5982\u6124\u6012\u3001\u559c\u60a6\u3001\u5931\u671b\u7b49）<\/li>\n

        \u672a\u6765\u8fd8\u53ef\u7ed3\u5408\u5b9a\u65f6\u4efb\u52a1\u3001\u6570\u636e\u5e93\u5b58\u50a8\u3001\u90ae\u4ef6\u544a\u8b66\u7b49\u529f\u80fd，\u6784\u5efa\u5b8c\u6574\u7684\u8206\u60c5\u76d1\u63a7\u7cfb\u7edf\u3002<\/p>\n

        \n

        💡 \u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n

        \u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f？\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a，\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf，\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，\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

        StructBERT\u8f7b\u91cf\u7ea7\u90e8\u7f72：\u4f4e\u914d\u670d\u52a1\u5668\u4e5f\u80fd\u8dd1\u7684\u60c5\u611f\u5206\u6790
        \n1. \u4e2d\u6587\u60c5\u611f\u5206\u6790\u7684\u73b0\u5b9e\u6311\u6218\u4e0e\u9700\u6c42
        \n\u5728\u5f53\u524d\u81ea\u7136\u8bed\u8a00\u5904\u7406（NLP）\u5e7f\u6cdb\u5e94\u7528\u7684\u80cc\u666f\u4e0b，\u4e2d\u6587\u60c5\u611f\u5206\u6790\u5df2\u6210\u4e3a\u667a\u80fd\u5ba2\u670d\u3001\u8206\u60c5\u76d1\u63a7\u3001\u7528\u6237\u8bc4\u8bba\u6316\u6398\u7b49\u573a\u666f\u7684\u6838\u5fc3\u6280\u672f\u4e4b\u4e00\u3002\u7136\u800c，\u5927\u591a\u6570\u9ad8\u6027\u80fd\u9884\u8bad\u7ec3\u6a21\u578b（\u5982BERT\u3001RoBERTa）\u4f9d\u8d56GPU\u8fdb\u884c\u63a8\u7406，\u5728\u7f3a\u4e4f\u663e\u5361\u8d44\u6e90\u7684\u8fb9\u7f18\u8bbe\u5907\u6216\u4f4e\u914d\u670d\u52a1\u5668\u4e0a\u96be\u4ee5\u843d\u5730\u3002
        \n\u4f01\u4e1a\u5728\u5b9e\u9645\u90e8\u7f72\u4e2d\u5e38\u9762<\/p>\n","protected":false},"author":2,"featured_media":61143,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"topic":[],"class_list":["post-61144","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server"],"yoast_head":"\nStructBERT\u8f7b\u91cf\u7ea7\u90e8\u7f72\uff1a\u4f4e\u914d\u670d\u52a1\u5668\u4e5f\u80fd\u8dd1\u7684\u60c5\u611f\u5206\u6790 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/61144.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"StructBERT\u8f7b\u91cf\u7ea7\u90e8\u7f72\uff1a\u4f4e\u914d\u670d\u52a1\u5668\u4e5f\u80fd\u8dd1\u7684\u60c5\u611f\u5206\u6790 - 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