{"id":10006642,"date":"2022-09-30T21:52:19","date_gmt":"2022-09-30T13:52:19","guid":{"rendered":"https:\/\/ityet.com\/?p=10006642"},"modified":"2022-09-30T21:52:19","modified_gmt":"2022-09-30T13:52:19","slug":"easynlp%e5%b8%a6%e4%bd%a0%e5%ae%9e%e7%8e%b0%e4%b8%ad%e8%8b%b1%e6%96%87%e6%9c%ba%e5%99%a8%e9%98%85%e8%af%bb%e7%90%86%e8%a7%a3","status":"publish","type":"post","link":"https:\/\/itmax.cn\/?p=10006642","title":{"rendered":"EasyNLP\u5e26\u4f60\u5b9e\u73b0\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3"},"content":{"rendered":"<p>\u7b80\u4ecb\uff1a\u00a0\u672c\u2f42\u5c06\u63d0\u4f9b\u5bf9MacBERT\u6a21\u578b\u7684\u6280\u672f\u89e3\u8bfb\uff0c\u4ee5\u53ca\u5982\u4f55\u5728EasyNLP\u6846\u67b6\u4e2d\u4f7f\u2f64MacBERT\u53ca\u5176\u4ed6\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\uff0c\u8fdb\u884c\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u7684\u8bad\u7ec3\u4e0e\u9884\u6d4b\u3002\u4f5c\u8005\uff1a\u65bd\u6668\u3001\u9ec4\u4fca\u5bfc\u8bfb\u673a\u5668\u9605\u8bfb\u7406\u89e3\u662f\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\uff0c\u7279\u522b\u662f\u81ea\u7136\u8bed\u8a00\u7406\u89e3\uff08NLU\uff09\u9886\u57df\u6700\u91cd\u8981\u7684\u7814\u7a76\u65b9\u5411\u4e4b\u4e00\u3002\u81ea1977\u5e74\u9996\u6b21\u88ab\u63d0\u51fa\u4ee5\u6765\uff0c\u673a\u5668\u9605\u8bfb\u7406\u89e3\u5df2\u6709\u8fd150\u5e74\u7684\u53d1\u5c55\u53f2\uff0c\u5386\u7ecf\u201c\u4eba\u5de5\u89c4\u5219\u201d\u3001\u201c\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u201d\u3001\u201c\u6df1\u5ea6\u5b66\u4e60\u201d\u3001\u201c\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u6a21\u578b\u201d\u7b49\u591a\u4e2a\u53d1\u5c55\u9636\u6bb5\u3002\u00a0\u673a\u5668\u9605\u8bfb\u7406\u89e3\u65e8\u5728\u5e2e\u52a9\u4eba\u7c7b\u4ece\u5927\u91cf\u6587\u672c\u4e2d\uff0c\u5feb\u901f\u805a\u7126\u76f8\u5173\u4fe1\u606f\uff0c\u964d\u4f4e\u4eba\u5de5\u4fe1\u606f\u83b7\u53d6\u6210\u672c\uff0c\u589e\u52a0\u4fe1\u606f\u68c0\u7d22\u6709\u6548\u6027\u3002\u4f5c\u4e3a\u4eba\u5de5\u667a\u80fd\u5728\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u65b9\u5411\u4e0a\u7684\u201c\u96c6\u5927\u6210\u8005\u201d\uff0c\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u8003\u5bdf\u4e86\u4ece \u201c\u8bcd\u201d \u5230 \u201c\u53e5\u201d \u4e43\u81f3 \u201c\u7bc7\u7ae0\u201d\uff0c\u6bcf\u4e00\u4e2a\u8bed\u8a00\u7c92\u5ea6\u7684\u7406\u89e3\u80fd\u529b\uff0c\u8fd9\u4f7f\u5f97\u5728\u4eba\u5de5\u667a\u80fd\u53d1\u5c55\u7684\u6bcf\u4e2a\u9636\u6bb5\uff0c\u5b83\u90fd\u662f\u975e\u5e38\u56f0\u96be\u3001\u4e5f\u9887\u4e3a\u91cd\u8981\u7684\u201c\u5175\u5bb6\u5fc5\u4e89\u4e4b\u5730\u201d\u3002\u4ee5SQuAD\u4e3a\u4ee3\u8868\u7684\u9605\u8bfb\u7406\u89e3\u6570\u636e\u96c6\uff0c\u89c1\u8bc1\u4e86\u6df1\u5ea6\u5b66\u4e60\u5927\u53d1\u5c55\u65f6\u4ee3\u91cc\uff0c\u5404\u5927\u516c\u53f8\u5237\u699c\u521b\u65b0\u65b9\u6848\u7684\u4f60\u65b9\u5531\u7f62\u6211\u767b\u573a\uff0c\u968f\u540e\u53c8\u5728\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u65f6\u4ee3\uff0c\u6210\u4e3aBERT\u7b49\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u8bc4\u6d4b\u57fa\u51c6\u3002\u53ef\u4ee5\u8bf4\u8fd1\u5341\u5e74\u91cc\uff0c\u673a\u5668\u9605\u8bfb\u7406\u89e3\u6781\u5927\u4fc3\u8fdb\u4e0e\u89c1\u8bc1\u4e86\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u7e41\u8363\u4e0e\u53d1\u5c55\u3002\u5f62\u5f0f\u5316\u6765\u8bb2\uff0c\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u7684\u8f93\u5165\u4e3a\u4e00\u6bb5\u7bc7\u7ae0\u6587\u672c\uff08context\uff09\uff0c\u4e0e\u4e00\u4e2a\u95ee\u53e5\uff08question\uff09\uff0c\u901a\u8fc7\u5b66\u4e60\u6a21\u578b\uff0c\u8f93\u51fa\u9884\u6d4b\u7684\u7b54\u6848\u6587\u672c\uff08answer\uff09\u3002\u6839\u636e\u83b7\u53d6\u7b54\u6848\u65b9\u5f0f\u7684\u4e0d\u540c\uff0c\u5f53\u524d\u4e1a\u754c\u4e3b\u6d41\u5c06\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u5206\u4e3a\u56db\u5927\u7c7b\uff1a\u5b8c\u578b\u586b\u7a7a\u5f0f\uff08Cloze tests\uff09\u3001\u591a\u9879\u9009\u62e9\u5f0f\uff08Multi-choice\uff09\u3001\u7247\u6bb5\u62bd\u53d6\u5f0f\uff08Span extraction\uff09\u53ca\u81ea\u7531\u751f\u6210\u5f0f\uff08Free answering\uff09\u3002\u5176\u4e2d\u7247\u6bb5\u62bd\u53d6\u5f0f\u6839\u636e\u95ee\u53e5\uff08question\uff09\uff0c\u76f4\u63a5\u4ece\u7bc7\u7ae0\u6587\u672c\uff08context\uff09\u4e2d\u9884\u6d4b\u7b54\u6848\u6587\u672c\uff08answer\uff09\u7684\u8d77\u6b62\u4f4d\u7f6e\uff08start\/end positions\uff09\uff0c\u4ece\u800c\u62bd\u53d6\u51fa\u7b54\u6848\u3002\u7531\u4e8e\u5176\u4e0e\u771f\u5b9e\u573a\u666f\u63a5\u8fd1\uff0c\u96be\u5ea6\u9002\u4e2d\uff0c\u6613\u4e8e\u8bc4\u6d4b\uff0c\u4e14\u6709SQuAD\u7b49\u9ad8\u8d28\u91cf\u6570\u636e\u96c6\u652f\u6491\uff0c\u56e0\u6b64\u6210\u4e3a\u5f53\u524d\u7684\u4e3b\u6d41\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u3002\u968f\u7740\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u7684\u53d1\u5c55\uff0c\u8fd1\u5e74\u6765\u7247\u6bb5\u62bd\u53d6\u5f0f\u9605\u8bfb\u7406\u89e3\u7684\u6548\u679c\u5c61\u521b\u65b0\u9ad8\u3002\u5728\u82f1\u6587\u573a\u666f\u4e0b\uff0c\u4f20\u7edf\u7684BERT\u3001RoBERTa\u3001ALBERT\u7b49\u6a21\u578b\u90fd\u80fd\u53d6\u5f97\u8d85\u8d8a\u4eba\u7c7b\u7684\u6548\u679c\uff1b\u800c\u5728\u4e2d\u6587\u573a\u666f\u4e0b\uff0cMacBERT\u7b49\u6a21\u578b\uff08Pre-Training with Whole Word Masking for Chinese BERT\uff09\u901a\u8fc7\u5f15\u5165\u4e00\u79cd\u7ea0\u9519\u578b\u63a9\u7801\u8bed\u8a00\u6a21\u578b\uff08Mac\uff09\u9884\u8bad\u7ec3\u4efb\u52a1\uff0c\u7f13\u89e3\u4e86\u201c\u9884\u8bad\u7ec3-\u4e0b\u6e38\u4efb\u52a1\u201d\u4e0d\u4e00\u81f4\u7684\u95ee\u9898\uff0c\u4e5f\u66f4\u597d\u5730\u9002\u914d\u4e86\u4e2d\u6587\u573a\u666f\uff0c\u5728\u5305\u62ec\u673a\u5668\u9605\u8bfb\u7406\u89e3\u5728\u5185\u7684\u591a\u79cdNLP\u4efb\u52a1\u4e0a\u53d6\u5f97\u4e86\u663e\u8457\u7684\u6027\u80fd\u63d0\u5347\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u5728EasyNLP\u6846\u67b6\u4e2d\u96c6\u6210\u4e86MacBERT\u7b97\u6cd5\u548c\u6a21\u578b\uff0c\u914d\u5408EasyNLP\u4e2d\u539f\u6709\u7684BERT\u3001RoBERTa\u7b49\u6a21\u578b\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u65b9\u4fbf\u5730\u4f7f\u7528\u8fd9\u4e9b\u6a21\u578b\u8fdb\u884c\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u7684\u8bad\u7ec3\u548c\u9884\u6d4b\u3002\u00a0EasyNLP\uff08<a href=\"https:\/\/link.segmentfault.com\/?enc=Pnjzo6zGTJU25UBX4bmIBA%3D%3D.QuU58p3KoF1iCJdWARcL3nTQm0fyYkeh9YRoivOtIWzPKZchZo6KSQcU6xggyOS0\" rel=\"nofollow\">https:\/\/github.com\/alibaba\/Ea&#8230;<\/a>\uff09\u662f\u963f\u2fa5\u4e91\u673a\u5668\u5b66\u4e60PAI \u56e2\u961f\u57fa\u4e8e PyTorch \u5f00\u53d1\u7684\u6613\u2f64\u4e14\u4e30\u5bcc\u7684\u4e2d\u2f42NLP\u7b97\u6cd5\u6846\u67b6\uff0c\u2f40\u6301\u5e38\u2f64\u7684\u4e2d\u2f42\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u2f24\u6a21\u578b\u843d\u5730\u6280\u672f\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e86\u4ece\u8bad\u7ec3\u5230\u90e8\u7f72\u7684\u2f00\u7ad9\u5f0f NLP \u5f00\u53d1\u4f53\u9a8c\u3002EasyNLP \u63d0\u4f9b\u4e86\u7b80\u6d01\u7684\u63a5\u2f1d\u4f9b\u2f64\u6237\u5f00\u53d1 NLP \u6a21\u578b\uff0c\u5305\u62ecNLP\u5e94\u2f64 AppZoo \u548c\u9884\u8bad\u7ec3 ModelZoo\uff0c\u540c\u65f6\u63d0\u4f9b\u6280\u672f\u5e2e\u52a9\u2f64\u6237\u2fbc\u6548\u7684\u843d\u5730\u8d85\u2f24\u9884\u8bad\u7ec3\u6a21\u578b\u5230\u4e1a\u52a1\u3002\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4f5c\u4e3a\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u7684\u96c6\u5927\u6210\u8005\uff0c\u540c\u65f6\u4e5f\u662f\u6587\u672c\u95ee\u7b54\u3001\u4fe1\u606f\u62bd\u53d6\u7b49\u9886\u57df\u7684\u57fa\u7840\u4efb\u52a1\uff0c\u5177\u6709\u8f83\u9ad8\u7684\u7814\u7a76\u4ef7\u503c\u3002\u56e0\u6b64\uff0cEasyNLP\u589e\u52a0\u4e86\u5bf9\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u7684\u652f\u6301\uff0c\u5e0c\u671b\u80fd\u670d\u52a1\u66f4\u591a\u7684NLP\/NLU\u7b97\u6cd5\u5f00\u53d1\u8005\u548c\u7814\u7a76\u8005\uff0c\u4e5f\u5e0c\u671b\u548c\u793e\u533a\u4e00\u8d77\u63a8\u52a8NLU\u76f8\u5173\u6280\u672f\u7684\u53d1\u5c55\u548c\u843d\u5730\u3002\u00a0\u672c\u2f42\u5c06\u63d0\u4f9b\u5bf9MacBERT\u6a21\u578b\u7684\u6280\u672f\u89e3\u8bfb\uff0c\u4ee5\u53ca\u5982\u4f55\u5728EasyNLP\u6846\u67b6\u4e2d\u4f7f\u2f64MacBERT\u53ca\u5176\u4ed6\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\uff0c\u8fdb\u884c\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u7684\u8bad\u7ec3\u4e0e\u9884\u6d4b\u3002MacBERT\u6a21\u578b\u89e3\u8bfb\u4e3b\u6d41\u7684\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\uff08\u5982BERT\u3001RoBERTa\u7b49\uff09\u4e3b\u8981\u9488\u5bf9\u82f1\u8bed\u8bed\u8a00\u8bbe\u8ba1\uff0c\u5c06\u5176\u76f4\u63a5\u8fc1\u79fb\u5230\u4e2d\u6587\u573a\u666f\u65f6\uff0c\u4f1a\u9762\u4e34\u4e2d\u82f1\u6587\u8bed\u8a00\u672c\u8eab\u7684\u5dee\u5f02\uff0c\u5982\uff1a\u4e2d\u6587\u8bcd\u95f4\u65e0\u7a7a\u683c\u3001\u65e0\u9700\u5207\u5206\u5b50\u8bcd\u3001\u591a\u4e2a\u5355\u5b57\u7ec4\u6210\u5177\u6709\u5b8c\u6574\u610f\u4e49\u7684\u8bcd\u8bed\u7b49\u3002\u4f8b\u5982\u4e0b\u56fe\u4e2d\u539f\u59cb\u8bed\u53e5\u201c\u4f7f\u7528\u8bed\u8a00\u6a21\u578b\u6765\u9884\u6d4b\u4e0b\u4e00\u4e2a\u8bcd\u7684\u6982\u7387\u201d\uff0c\u6309\u5355\u5b57\u5207\u5206\u540e\uff0c\u67d0\u4e9b\u5355\u5b57\u7531\u4e8e\u4f1a\u7ec4\u6210\u5b8c\u6574\u7684\u8bcd\u8bed\uff08\u5982\uff1a\u8bed\u8a00\u6a21\u578b\u3001\u9884\u6d4b\u3001\u6982\u7387\uff09\uff0c\u6b64\u65f6\u82e5\u53eamask\u6389\u5176\u4e2d\u4e00\u4e2a\u5b57\uff0c\u4f1a\u5f71\u54cd\u63a9\u7801\u4e0e\u8bed\u8a00\u6a21\u578b\u9884\u8bad\u7ec3\u7684\u6548\u679c\u3002\u6b64\u5916\uff0c\u4f20\u7edf\u8bed\u8a00\u6a21\u578b\u5728\u9884\u8bad\u7ec3\u65f6\u4f1a\u91c7\u7528 [MASK] \u5b57\u7b26\u8fdb\u884cmask\uff0c\u800c\u5728\u4e0b\u6e38\u4efb\u52a1\u6587\u672c\u4e2d\u5374\u5e76\u4e0d\u5b58\u5728 [MASK] \u6807\u8bb0\uff0c\u8fd9\u5728\u4e24\u9636\u6bb5\u4e2d\u5929\u7136\u5f15\u5165\u4e86gap\u3002\u4e3a\u7f13\u89e3\u4e0a\u8ff0\u95ee\u9898\uff0cMacBERT\u7b49\u6a21\u578b\u4fee\u6539\u4f20\u7edfMLM\u4efb\u52a1\uff0c\u5f15\u5165\u4e86\u4e00\u79cd\u7ea0\u9519\u578b\u63a9\u7801\u8bed\u8a00\u6a21\u578b\uff08Mac\uff09\u9884\u8bad\u7ec3\u4efb\u52a1\uff0c\u5305\u62ecwwm (whole word masking)\u3001NM\uff08n-gram masking\uff09\u3001\u76f8\u4f3c\u8bcd\u66ff\u6362\u7b49mask\u65b9\u6848\uff0c\u66f4\u597d\u5730\u9002\u914d\u4e86\u4e2d\u6587\u7684\u8bed\u8a00\u573a\u666f\uff0c\u51cf\u5c11\u4e86\u201c\u9884\u8bad\u7ec3-\u4e0b\u6e38\u4efb\u52a1\u201d\u4e0d\u4e00\u81f4\u7684\u95ee\u9898\uff0c\u63d0\u5347\u4e86\u9884\u8bad\u7ec3\u6a21\u578b\u5728\u591a\u79cdNLP\u4efb\u52a1\u4e0a\u7684\u6548\u679c\u3002\u53e6\u5916\uff0c\u7531\u4e8eMacBERT\u7684\u4e3b\u8981\u6846\u67b6\u4e0eBERT\u5b8c\u5168\u4e00\u81f4\uff0c\u53ef\u5728\u4e0d\u4fee\u6539\u73b0\u6709\u4ee3\u7801\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u65e0\u7f1d\u8fc7\u6e21\uff0c\u8fd9\u4e3a\u5f00\u53d1\u8005\u7684\u4ee3\u7801\u8fc1\u79fb\u5e26\u6765\u4e86\u5f88\u5927\u7684\u4fbf\u5229\u3002<br \/><img decoding=\"async\" referrerpolicy=\"no-referrer\" src=\"https:\/\/segmentfault.com\/img\/remote\/1460000042566815\" alt=\"\u56fe\u7247\" title=\"\u56fe\u7247\" loading=\"lazy\"><br \/>\u5177\u4f53\u6765\u8bb2\uff0cMacBERT\u7b49\u6a21\u578b\u5728MLM\u65f6\uff0c\u5bf9\u4e2d\u6587\u6574\u8bcd\u4e2d\u6240\u6709\u5355\u5b57\u540c\u65f6\u8fdb\u884cmask\uff0c\u5e76\u91c7\u7528n-gram mask\u7b56\u7565\uff0c\u4eceunigram\u52304-gram\u5bf9\u5e94\u7684mask\u6982\u7387\u5206\u522b\u4e3a40%-10%\u3002\u5728mask\u65f6\u4e0d\u518d\u91c7\u7528 [MASK] token\uff0c\u800c\u6539\u4e3a\u4f7f\u7528\u8bcd\u8bed\u7684\u8fd1\u4e49\u8bcd\uff0c\u8fd1\u4e49\u8bcd\u91c7\u7528\u57fa\u4e8eword2vec\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u7684Synonyms toolkit\u83b7\u53d6\uff0c\u5728\u6781\u5c11\u6570\u60c5\u51b5\u6ca1\u6709\u8fd1\u4e49\u8bcd\u65f6\uff0c\u4f7f\u7528\u968f\u673a\u8bcd\u8fdb\u884c\u66ff\u6362\u3002\u6a21\u578b\u603b\u4f53\u5bf9\u8f93\u516515%\u7684\u8bcd\u8fdb\u884cmask\uff0cmask\u65f680%\u7684\u6982\u7387\u66ff\u6362\u4e3a\u8fd1\u4e49\u8bcd\u300110%\u7684\u6982\u7387\u66ff\u6362\u4e3a\u968f\u673a\u8bcd\u300110%\u7684\u6982\u7387\u4fdd\u7559\u4e3a\u539f\u59cb\u8bcd\u3002\u6b64\u5916\uff0cBERT\u539f\u7248\u7684NSP\u6a21\u578b\u957f\u671f\u4e3a\u7814\u7a76\u8005\u4eec\u6240\u8bdf\u75c5\uff0cMacBERT\u6a21\u578b\u4e2d\u5c06NSP\u4fee\u6539\u4e3aSOP (Sentence Order Prediction)\uff0c\u5176\u6b63\u4f8b\u4e3a\u8fde\u7eed\u6587\u672c\uff0c\u8d1f\u4f8b\u4e3a\u4ea4\u6362\u539f\u59cb\u6587\u672c\u987a\u5e8f\uff0c\u8fd9\u6837\u5f88\u597d\u63d0\u5347\u4e86\u6a21\u578b\u5728\u591a\u53e5\u7bc7\u7ae0\u578b\u4efb\u52a1\u4e0a\u7684\u6548\u679c\u3002\u6a21\u578b\u7684\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u53bb\u9664\u4e0a\u8ff0MLM\u4e2d\u4efb\u4e00\u6539\u8fdb\u90fd\u4f1a\u5bfc\u81f4\u5e73\u5747\u6027\u80fd\u4e0b\u964d\uff0c\u8fd9\u8868\u660e\u51e0\u5904\u63a9\u7801\u4fee\u6539\u90fd\u6709\u52a9\u4e8e\u8bed\u8a00\u6a21\u578b\u5b66\u4e60\uff1b\u540c\u65f6\u53bb\u9664SOP\u4efb\u52a1\u540e\uff0c\u5728\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u4e0a\u7684\u6548\u679c\u4f1a\u660e\u663e\u4e0b\u964d\uff0c\u8fd9\u4e5f\u8868\u660e\u53e5\u5b50\u7ea7\u9884\u8bad\u7ec3\u4efb\u52a1\u5728\u7bc7\u7ae0\u5b66\u4e60\u4e2d\u7684\u5fc5\u8981\u6027\u3002<br \/><img decoding=\"async\" referrerpolicy=\"no-referrer\" src=\"https:\/\/segmentfault.com\/img\/remote\/1460000042566816\" alt=\"\u56fe\u7247\" title=\"\u56fe\u7247\" loading=\"lazy\"><br \/>\u673a\u5668\u9605\u8bfb\u7406\u89e3\u6a21\u578b\u4f7f\u7528\u6559\u7a0b\u4ee5\u4e0b\u6211\u4eec\u7b80\u8981\u4ecb\u7ecd\uff0c\u5982\u4f55\u5728EasyNLP\u6846\u67b6\u4e2d\u4f7f\u7528MacBERT\u53ca\u5176\u4ed6\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\uff0c\u8fdb\u884c\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u7684\u8bad\u7ec3\u53ca\u9884\u6d4b\u3002\u5b89\u88c5EasyNLP\u7528\u6237\u53ef\u4ee5\u76f4\u63a5\u53c2\u8003GitHub\uff08<a href=\"https:\/\/link.segmentfault.com\/?enc=zm%2BJy8CQmOICyNwD6Qvh4Q%3D%3D.2lyk3j7Bc5VlmVa91xfiTjrQYrDDMtSLdd9dREUtidcEmt2jca8afN8FyznK4FU%2F\" rel=\"nofollow\">https:\/\/github.com\/alibaba\/Ea&#8230;<\/a>\uff09\u4e0a\u7684\u8bf4\u660e\u5b89\u88c5EasyNLP\u7b97\u6cd5\u6846\u67b6\u3002pipeline\u63a5\u53e3\u5feb\u901f\u201c\u5c1d\u9c9c\u201d\u4f53\u9a8c\u6548\u679c\u4e3a\u4e86\u65b9\u4fbf\u5f00\u53d1\u8005\u4f7f\u7528\uff0c\u6211\u4eec\u5728EasyNLP\u6846\u67b6\u5185\u5b9e\u73b0\u4e86Inference Pipeline\u529f\u80fd\u3002\u7528\u6237\u53ef\u4ee5\u5728\u4e0d\u9700\u8981\u81ea\u5df1\u8bad\u7ec3\u6216\u5fae\u8c03\u6a21\u578b\u7684\u60c5\u51b5\u4e0b\uff0c\u76f4\u63a5\u201c\u5c1d\u9c9c\u201d\uff0c\u4f7f\u7528pipeline\u63a5\u53e3\u4e00\u6b65\u8c03\u7528finetune\u597d\u7684\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u6a21\u578b\u3002\u53ea\u9700\u6267\u884c\u5982\u4e0b\u547d\u4ee4\u5373\u53ef\uff1afrom easynlp.pipelines import pipeline<\/p>\n<h2>\u8f93\u5165\u6570\u636e<\/h2>\n<p>data = [{<\/p>\n<pre><code>        &quot;query&quot;: &quot;\u676d\u5dde\u4ec0\u4e48\u65f6\u5019\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff1f&quot;,\n        &quot;answer_text&quot;: &quot;2020\u5e744\u6708&quot;,\n        &quot;context&quot;: &quot;\u65b0\u534e\u793e\u676d\u5dde9\u670822\u65e5\u7535\uff08\u8bb0\u8005\u5546\u610f\u76c8 \u590f\u4eae\uff09\u7ade\u8d5b\u573a\u9986\u5168\u90e8\u7ae3\u5de5\u5e76\u5b8c\u6210\u8d5b\u4e8b\u529f\u80fd\u9a8c\u6536\uff0c\u201c\u8fce\u4e9a\u8fd0\u201d\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u5efa\u8bbe\u8fc8\u5165\u5feb\u8f66\u9053\uff0c\u4e9a\u8fd0\u573a\u9986\u63d0\u524d\u5f00\u653e\u6380\u8d77\u5168\u6c11\u5065\u8eab\u70ed\u6f6e\u2026\u202623\u65e5\uff0c\u5ef6\u671f\u540e\u7684\u676d\u5dde\u4e9a\u8fd0\u4f1a\u5c06\u8fce\u6765\u5012\u8ba1\u65f6\u4e00\u5468\u5e74\uff0c\u5404\u9879\u7b79\u5907\u5de5\u4f5c\u4e5f\u7ed3\u51fa\u7d2f\u7d2f\u7855\u679c\uff0c\u201c\u5929\u5802\u4e4b\u57ce\u201d\u518d\u6b21\u84c4\u52bf\u5f85\u53d1\u3002\u529e\u597d\u4e00\u4e2a\u4f1a\uff0c\u63d0\u5347\u4e00\u5ea7\u57ce\u30022020\u5e744\u6708\uff0c\u676d\u5dde\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff0c\u5176\u4e2d\u5305\u62ec\u57fa\u7840\u8bbe\u65bd\u63d0\u5347\u3001\u7eff\u6c34\u9752\u5c71\u5b88\u62a4\u3001\u6570\u5b57\u6cbb\u7406\u8d4b\u80fd\u7b49\u516b\u9879\u5177\u4f53\u884c\u52a8\u3002\u968f\u7740\u4e9a\u8fd0\u7684\u811a\u6b65\u65e5\u76ca\u4e34\u8fd1\uff0c\u676d\u5dde\u897f\u7ad9\u3001\u8427\u5c71\u56fd\u9645\u673a\u573a\u4e09\u671f\u3001\u5408\u676d\u9ad8\u94c1\u6e56\u676d\u6bb5\u3001\u673a\u573a\u8f68\u9053\u5feb\u7ebf\uff0819\u53f7\u7ebf\uff09\u7b49\u201c\u4e24\u70b9\u4e24\u7ebf\u201d\u91cd\u5927\u9879\u76ee\u6b63\u5f0f\u6295\u8fd0\u3002\u6839\u636e\u676d\u5dde\u5e02\u57ce\u4e61\u5efa\u8bbe\u59d4\u5458\u4f1a\u53d1\u5e03\u7684\u4fe1\u606f\uff0c\u9884\u8ba1\u5230\u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230480\u516c\u91cc\u3002\u4f4f\u5728\u8fd9\u91cc\u7684\u4eba\u4eec\u6b63\u5207\u8eab\u4f53\u4f1a\u5230\u6084\u7136\u53d1\u751f\u7684\u53d8\u5316\u2014\u2014\u4ea4\u901a\u65b9\u4fbf\u4e86\uff0c\u9053\u8def\u53d8\u7f8e\u4e86\uff0c\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u4e5f\u6108\u53d1\u5b8c\u5584\u3002&quot;,\n        &quot;qas_id&quot;: &quot;CN_01&quot;\n    },\n    {\n        &quot;query&quot;: &quot;\u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230\u591a\u5c11\uff1f&quot;,\n        &quot;answer_text&quot;: &quot;480\u516c\u91cc&quot;,\n        &quot;context&quot;: &quot;\u65b0\u534e\u793e\u676d\u5dde9\u670822\u65e5\u7535\uff08\u8bb0\u8005\u5546\u610f\u76c8 \u590f\u4eae\uff09\u7ade\u8d5b\u573a\u9986\u5168\u90e8\u7ae3\u5de5\u5e76\u5b8c\u6210\u8d5b\u4e8b\u529f\u80fd\u9a8c\u6536\uff0c\u201c\u8fce\u4e9a\u8fd0\u201d\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u5efa\u8bbe\u8fc8\u5165\u5feb\u8f66\u9053\uff0c\u4e9a\u8fd0\u573a\u9986\u63d0\u524d\u5f00\u653e\u6380\u8d77\u5168\u6c11\u5065\u8eab\u70ed\u6f6e\u2026\u202623\u65e5\uff0c\u5ef6\u671f\u540e\u7684\u676d\u5dde\u4e9a\u8fd0\u4f1a\u5c06\u8fce\u6765\u5012\u8ba1\u65f6\u4e00\u5468\u5e74\uff0c\u5404\u9879\u7b79\u5907\u5de5\u4f5c\u4e5f\u7ed3\u51fa\u7d2f\u7d2f\u7855\u679c\uff0c\u201c\u5929\u5802\u4e4b\u57ce\u201d\u518d\u6b21\u84c4\u52bf\u5f85\u53d1\u3002\u529e\u597d\u4e00\u4e2a\u4f1a\uff0c\u63d0\u5347\u4e00\u5ea7\u57ce\u30022020\u5e744\u6708\uff0c\u676d\u5dde\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff0c\u5176\u4e2d\u5305\u62ec\u57fa\u7840\u8bbe\u65bd\u63d0\u5347\u3001\u7eff\u6c34\u9752\u5c71\u5b88\u62a4\u3001\u6570\u5b57\u6cbb\u7406\u8d4b\u80fd\u7b49\u516b\u9879\u5177\u4f53\u884c\u52a8\u3002\u968f\u7740\u4e9a\u8fd0\u7684\u811a\u6b65\u65e5\u76ca\u4e34\u8fd1\uff0c\u676d\u5dde\u897f\u7ad9\u3001\u8427\u5c71\u56fd\u9645\u673a\u573a\u4e09\u671f\u3001\u5408\u676d\u9ad8\u94c1\u6e56\u676d\u6bb5\u3001\u673a\u573a\u8f68\u9053\u5feb\u7ebf\uff0819\u53f7\u7ebf\uff09\u7b49\u201c\u4e24\u70b9\u4e24\u7ebf\u201d\u91cd\u5927\u9879\u76ee\u6b63\u5f0f\u6295\u8fd0\u3002\u6839\u636e\u676d\u5dde\u5e02\u57ce\u4e61\u5efa\u8bbe\u59d4\u5458\u4f1a\u53d1\u5e03\u7684\u4fe1\u606f\uff0c\u9884\u8ba1\u5230\u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230480\u516c\u91cc\u3002\u4f4f\u5728\u8fd9\u91cc\u7684\u4eba\u4eec\u6b63\u5207\u8eab\u4f53\u4f1a\u5230\u6084\u7136\u53d1\u751f\u7684\u53d8\u5316\u2014\u2014\u4ea4\u901a\u65b9\u4fbf\u4e86\uff0c\u9053\u8def\u53d8\u7f8e\u4e86\uff0c\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u4e5f\u6108\u53d1\u5b8c\u5584\u3002&quot;,\n        &quot;qas_id&quot;: &quot;CN_02&quot;\n    }]\n<\/code><\/pre>\n<h2>pipeline\u7684\u53c2\u6570\u4e3afinetune\u597d\u7684\u6a21\u578b<\/h2>\n<h2>\u5f53\u524dEasyNLP\u652f\u6301\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u7684pipeline\u5feb\u901f\u4f53\u9a8c\uff0c\u5206\u522b\u96c6\u6210\u4e86finetune\u597d\u7684\u4e2d\u6587macbert\u6a21\u578b\u548c\u82f1\u6587bert\u6a21\u578b<\/h2>\n<h2>\u5982\u60f3\u4f53\u9a8c\u82f1\u6587\u9605\u8bfb\u7406\u89e3\uff0c\u53ea\u9700\u5c06\u6a21\u578b\u540d\u6539\u4e3a &#8216;bert-base-rcen&#8217; \u5373\u53ef<\/h2>\n<p>generator = pipeline(&#8216;macbert-base-rczh&#8217;)<\/p>\n<p>results = generator(data)<\/p>\n<p>for input_dict, result in zip(data, results):<\/p>\n<pre><code>context = result[&quot;context&quot;]\nquery = result[&quot;query&quot;]\nanswer_gold = result[&quot;gold_answer&quot;]\nanswer_pred = result[&quot;best_answer&quot;]\nprint('\/n context: {} \/n query: {} \/n gold_answer: {} \/n pred_answer: {} \/n'.format(context, query, answer_gold, answer_pred))\u5982\u4ee3\u7801\u4e2d\u6240\u793a\uff0c\u8f93\u5165\u7684data\u4e3a\u4e00\u4e2alist\uff0c\u5176\u4e2d\u6bcf\u6761instance\u4e3a\u4e00\u4e2adict\uff0c\u5305\u542b\u5176query\u3001answer\u3001context\u3001id\u4fe1\u606f\u3002pipeline\u7684\u53c2\u6570\u4e3afinetune\u597d\u7684\u6a21\u578b\uff0c\u5f53\u524dEasyNLP\u652f\u6301\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u7684pipeline\u5feb\u901f\u4f53\u9a8c\uff0c\u5206\u522b\u96c6\u6210\u4e86finetune\u597d\u7684\u4e2d\u6587macbert\u6a21\u578b\u548c\u82f1\u6587bert\u6a21\u578b\uff0c\u4e2d\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u7684\u6a21\u578b\u4e3a\u00a0'macbert-base-rczh'\uff0c\u5982\u60f3\u4f53\u9a8c\u82f1\u6587\u9605\u8bfb\u7406\u89e3\uff0c\u53ea\u9700\u5c06\u4e0a\u8ff0\u4ee3\u7801pipeline\u53c2\u6570\u4e2d\u7684\u6a21\u578b\u540d\u6539\u4e3a\u00a0'bert-base-rcen'\u00a0\u5373\u53ef\u3002\u00a0\u4ee5\u4e0a\u4ee3\u7801\u7684\u6267\u884c\u7ed3\u679c\u5982\u4e0b\u6240\u793a\uff0c\u53ef\u4ee5\u770b\u51fa\uff0c\u673a\u5668\u51c6\u786e\u5730\u7406\u89e3\u4e86\u6587\u672c\u4e0e\u95ee\u53e5\u7684\u542b\u4e49\uff0c\u5e76\u7ed9\u51fa\u4e86\u6b63\u786e\u7684\u7ed3\u679c\u3002 context: \u65b0\u534e\u793e\u676d\u5dde9\u670822\u65e5\u7535\uff08\u8bb0\u8005\u5546\u610f\u76c8\u590f\u4eae\uff09\u7ade\u8d5b\u573a\u9986\u5168\u90e8\u7ae3\u5de5\u5e76\u5b8c\u6210\u8d5b\u4e8b\u529f\u80fd\u9a8c\u6536\uff0c\u201c\u8fce\u4e9a\u8fd0\u201d\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u5efa\u8bbe\u8fc8\u5165\u5feb\u8f66\u9053\uff0c\u4e9a\u8fd0\u573a\u9986\u63d0\u524d\u5f00\u653e\u6380\u8d77\u5168\u6c11\u5065\u8eab\u70ed\u6f6e\u2026\u202623\u65e5\uff0c\u5ef6\u671f\u540e\u7684\u676d\u5dde\u4e9a\u8fd0\u4f1a\u5c06\u8fce\u6765\u5012\u8ba1\u65f6\u4e00\u5468\u5e74\uff0c\u5404\u9879\u7b79\u5907\u5de5\u4f5c\u4e5f\u7ed3\u51fa\u7d2f\u7d2f\u7855\u679c\uff0c\u201c\u5929\u5802\u4e4b\u57ce\u201d\u518d\u6b21\u84c4\u52bf\u5f85\u53d1\u3002\u529e\u597d\u4e00\u4e2a\u4f1a\uff0c\u63d0\u5347\u4e00\u5ea7\u57ce\u30022020\u5e744\u6708\uff0c\u676d\u5dde\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff0c\u5176\u4e2d\u5305\u62ec\u57fa\u7840\u8bbe\u65bd\u63d0\u5347\u3001\u7eff\u6c34\u9752\u5c71\u5b88\u62a4\u3001\u6570\u5b57\u6cbb\u7406\u8d4b\u80fd\u7b49\u516b\u9879\u5177\u4f53\u884c\u52a8\u3002\u968f\u7740\u4e9a\u8fd0\u7684\u811a\u6b65\u65e5\u76ca\u4e34\u8fd1\uff0c\u676d\u5dde\u897f\u7ad9\u3001\u8427\u5c71\u56fd\u9645\u673a\u573a\u4e09\u671f\u3001\u5408\u676d\u9ad8\u94c1\u6e56\u676d\u6bb5\u3001\u673a\u573a\u8f68\u9053\u5feb\u7ebf\uff0819\u53f7\u7ebf\uff09\u7b49\u201c\u4e24\u70b9\u4e24\u7ebf\u201d\u91cd\u5927\u9879\u76ee\u6b63\u5f0f\u6295\u8fd0\u3002\u6839\u636e\u676d\u5dde\u5e02\u57ce\u4e61\u5efa\u8bbe\u59d4\u5458\u4f1a\u53d1\u5e03\u7684\u4fe1\u606f\uff0c\u9884\u8ba1\u5230\u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230480\u516c\u91cc\u3002\u4f4f\u5728\u8fd9\u91cc\u7684\u4eba\u4eec\u6b63\u5207\u8eab\u4f53\u4f1a\u5230\u6084\u7136\u53d1\u751f\u7684\u53d8\u5316\u2014\u2014\u4ea4\u901a\u65b9\u4fbf\u4e86\uff0c\u9053\u8def\u53d8\u7f8e\u4e86\uff0c\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u4e5f\u6108\u53d1\u5b8c\u5584\u3002 <\/code><\/pre>\n<p>query: \u676d\u5dde\u4ec0\u4e48\u65f6\u5019\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff1f <br \/> gold_answer: 2020\u5e744\u6708 <br \/> pred_answer: 2020\u5e744\u6708 <\/p>\n<p>context: \u65b0\u534e\u793e\u676d\u5dde9\u670822\u65e5\u7535\uff08\u8bb0\u8005\u5546\u610f\u76c8\u590f\u4eae\uff09\u7ade\u8d5b\u573a\u9986\u5168\u90e8\u7ae3\u5de5\u5e76\u5b8c\u6210\u8d5b\u4e8b\u529f\u80fd\u9a8c\u6536\uff0c\u201c\u8fce\u4e9a\u8fd0\u201d\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u5efa\u8bbe\u8fc8\u5165\u5feb\u8f66\u9053\uff0c\u4e9a\u8fd0\u573a\u9986\u63d0\u524d\u5f00\u653e\u6380\u8d77\u5168\u6c11\u5065\u8eab\u70ed\u6f6e\u2026\u202623\u65e5\uff0c\u5ef6\u671f\u540e\u7684\u676d\u5dde\u4e9a\u8fd0\u4f1a\u5c06\u8fce\u6765\u5012\u8ba1\u65f6\u4e00\u5468\u5e74\uff0c\u5404\u9879\u7b79\u5907\u5de5\u4f5c\u4e5f\u7ed3\u51fa\u7d2f\u7d2f\u7855\u679c\uff0c\u201c\u5929\u5802\u4e4b\u57ce\u201d\u518d\u6b21\u84c4\u52bf\u5f85\u53d1\u3002\u529e\u597d\u4e00\u4e2a\u4f1a\uff0c\u63d0\u5347\u4e00\u5ea7\u57ce\u30022020\u5e744\u6708\uff0c\u676d\u5dde\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff0c\u5176\u4e2d\u5305\u62ec\u57fa\u7840\u8bbe\u65bd\u63d0\u5347\u3001\u7eff\u6c34\u9752\u5c71\u5b88\u62a4\u3001\u6570\u5b57\u6cbb\u7406\u8d4b\u80fd\u7b49\u516b\u9879\u5177\u4f53\u884c\u52a8\u3002\u968f\u7740\u4e9a\u8fd0\u7684\u811a\u6b65\u65e5\u76ca\u4e34\u8fd1\uff0c\u676d\u5dde\u897f\u7ad9\u3001\u8427\u5c71\u56fd\u9645\u673a\u573a\u4e09\u671f\u3001\u5408\u676d\u9ad8\u94c1\u6e56\u676d\u6bb5\u3001\u673a\u573a\u8f68\u9053\u5feb\u7ebf\uff0819\u53f7\u7ebf\uff09\u7b49\u201c\u4e24\u70b9\u4e24\u7ebf\u201d\u91cd\u5927\u9879\u76ee\u6b63\u5f0f\u6295\u8fd0\u3002\u6839\u636e\u676d\u5dde\u5e02\u57ce\u4e61\u5efa\u8bbe\u59d4\u5458\u4f1a\u53d1\u5e03\u7684\u4fe1\u606f\uff0c\u9884\u8ba1\u5230\u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230480\u516c\u91cc\u3002\u4f4f\u5728\u8fd9\u91cc\u7684\u4eba\u4eec\u6b63\u5207\u8eab\u4f53\u4f1a\u5230\u6084\u7136\u53d1\u751f\u7684\u53d8\u5316\u2014\u2014\u4ea4\u901a\u65b9\u4fbf\u4e86\uff0c\u9053\u8def\u53d8\u7f8e\u4e86\uff0c\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u4e5f\u6108\u53d1\u5b8c\u5584\u3002 <br \/> query: \u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230\u591a\u5c11\uff1f <br \/> gold_answer: 480\u516c\u91cc <br \/> pred_answer: 480\u516c\u91cc\u4e0b\u9762\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u6a21\u578b\u7684\u8be6\u7ec6\u5b9e\u73b0\u8fc7\u7a0b\uff0c\u4ece\u6570\u636e\u51c6\u5907\uff0c\u5230\u6a21\u578b\u7684\u8bad\u7ec3\u3001\u8bc4\u4f30\u3001\u9884\u6d4b\uff0c\u5e76\u7ed9\u51fa\u57fa\u4e8eEasyNLP\u7684\u51e0\u79cd\u65b9\u4fbf\u5feb\u6377\u7684\u4e00\u6b65\u6267\u884c\u65b9\u6cd5\u3002\u6570\u636e\u51c6\u5907\u91c7\u7528\u9884\u8bad\u7ec3\u597d\u7684\u8bed\u8a00\u6a21\u578b\uff0c\u8fdb\u884c\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u7684finetune\u65f6\uff0c\u9700\u8981\u7528\u6237\u63d0\u4f9b\u4efb\u52a1\u76f8\u5173\u7684\u8bad\u7ec3\u4e0e\u9a8c\u8bc1\u6570\u636e\uff0c\u5747\u4e3atsv\u683c\u5f0f\u6587\u4ef6\u3002\u6587\u4ef6\u4e2d\u6bcf\u884c\u5305\u542b\u4ee5\u5236\u8868\u7b26\/t\u5206\u9694\u7684\u591a\u5217\uff0c\u5305\u542b\u9605\u8bfb\u7406\u89e3\u8bad\u7ec3\u6240\u9700\u7684\u6240\u6709\u4fe1\u606f\uff0c\u4ece\u5de6\u81f3\u53f3\u4f9d\u6b21\u4e3a\uff1a\u6837\u4f8bID\u3001\u7bc7\u7ae0\u6587\u672c\uff08context\uff09\u3001\u95ee\u53e5\uff08question\uff09\u3001\u7b54\u6848\u6587\u672c\uff08answer\uff09\u3001\u7b54\u6848\u6587\u672c\u5728\u7bc7\u7ae0\u6587\u672c\u4e2d\u7684\u8d77\u59cb\u4f4d\u7f6e\u3001\u7bc7\u7ae0\u9898\u76ee\u3002\u6837\u4f8b\u5982\u4e0b\uff1aDEV_125_QUERY_3   \u4e59\u4e8c\u919b\u662f\u4e00\u4e2a\u6709\u673a\u5316\u5408\u7269\uff0c\u5316\u5b66\u5f0f\u4e3aOCHCHO\uff0c\u7531\u4e24\u4e2a\u919b\u57fa-C\u76f8\u8fde\u3002\u5b83\u662f\u6700\u7b80\u5355\u7684\u4e8c\u919b\uff0c\u5ba4\u6e29\u4e0b\u4e3a\u9ec4\u8272\u6db2\u4f53\u3002\u5de5\u4e1a\u4e0a\uff0c\u4e59\u4e8c\u919b\u53ef\u7531\u4e59\u4e8c\u9187\u5728\u94f6\u6216\u94dc\u50ac\u5316\u4e0b\u7684\u6c14\u76f8\u6c27\u5316\uff0c\u6216\u7528\u785d\u9178\u6eb6\u6db2\u6c27\u5316\u4e59\u919b\u5236\u5f97\u3002\u5b9e\u9a8c\u5ba4\u4e2d\uff0c\u4e59\u4e8c\u919b\u662f\u901a\u8fc7\u7528\u4e9a\u7852\u9178\u6c27\u5316\u4e59\u919b\u5236\u53d6\u3002\u65e0\u6c34\u4e59\u4e8c\u919b\u53ef\u7531\u56fa\u6001\u6c34\u5408\u7269\u4e0e\u4e94\u6c27\u5316\u4e8c\u78f7\u5171\u70ed\u5236\u5f97\u3002\u4e59\u4e8c\u919b\u7684\u5e94\u7528\u6709\uff1aof\u901a\u5e38\u4e59\u4e8c\u919b\u4ee540%\u6eb6\u6db2\u7684\u5f62\u5f0f\u51fa\u552e\u3002\u5b83\u4e0e\u5176\u4ed6\u5c0f\u5206\u5b50\u919b\u7c7b\u4f3c\uff0c\u53ef\u4ee5\u5f62\u6210\u6c34\u5408\u7269\uff0c\u800c\u4e14\u6c34\u5408\u7269\u7f29\u5408\u751f\u6210\u4e00\u7cfb\u5217\u7684\u201c\u5be1\u805a\u4f53\u201d\uff0c\u7ed3\u6784\u5c1a\u4e0d\u6e05\u695a\u3002\u76ee\u524d\u51fa\u552e\u7684\u81f3\u5c11\u6709\u4ee5\u4e0b\u4e24\u79cd\u6c34\u5408\u7269\uff1a\u6839\u636e\u4f30\u8ba1\uff0c\u4e59\u4e8c\u919b\u6c34\u6eb6\u6db2\u6d53\u5ea6\u4f4e\u4e8e1M\u65f6\uff0c\u5b83\u4e3b\u8981\u4ee5\u5355\u4f53\u6216\u6c34\u5408\u7269\u7684\u5f62\u5f0f\u5b58\u5728\uff0c\u5373OCHCHO\u3001OCHCH(OH)\u6216(HO)CHCH(OH)\u3002\u6d53\u5ea6\u5927\u4e8e1M\u65f6\uff0c\u4e3b\u8981\u4e3a\u4e8c\u805a\u4f53\u578b\uff0c\u53ef\u80fd\u4e3a\u7f29\u919b\uff0f\u916e\u7ed3\u6784\uff0c\u5206\u5b50\u5f0f\u4e3a[(HO)CH]OCHCHO\u3002   \u5de5\u4e1a\u4e0a\u4e59\u4e8c\u919b\u662f\u600e\u4e48\u5236\u5f97\u7684\uff1f   \u7531\u4e59\u4e8c\u9187\u5728\u94f6\u6216\u94dc\u50ac\u5316\u4e0b\u7684\u6c14\u76f8\u6c27\u5316\uff0c\u6216\u7528\u785d\u9178\u6eb6\u6db2\u6c27\u5316\u4e59\u919b\u5236\u5f97\u3002    59    \u4e59\u4e8c\u919b\u4e0b\u5217\u6587\u4ef6\u4e3a\u5df2\u7ecf\u5b8c\u6210\u9884\u5904\u7406\u7684\u4e2d\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u7684\u8bad\u7ec3\u548c\u9a8c\u8bc1\u6570\u636e\uff0c\u53ef\u7528\u4e8e\u6a21\u578b\u8bad\u7ec3\u4e0e\u6d4b\u8bd5\uff1a# \u4e2d\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u6570\u636e<br \/><a href=\"https:\/\/link.segmentfault.com\/?enc=%2BXhBIinDy%2FQRiaNOodMbCA%3D%3D.%2BiD6KIPZ%2Fu%2FzkOwFNOJcqxHiP1r4f4jEhg8YWqCksvLHFCJc163FNB37hPfFCg02LMuHB%2FQPdS8hl9XqqeVDEawDqS%2FvjNt%2B3ScC50Rth9kJPSK4yVuf9K1xvWxL99WWDHUlZaI%2BC%2BbrBbGIJnp3dwiiGeTtH3jh2knjoHmUROw%3D\" rel=\"nofollow\">http:\/\/atp-modelzoo-sh.oss-cn&#8230;<\/a><br \/><a href=\"https:\/\/link.segmentfault.com\/?enc=VixXLwc0Q0BmChwJMvAxyQ%3D%3D.6%2Bv2EkroXYHz0u2bP2Hl77hB8BPJABUCSeBdk0QhCobUQLP0bP83oDUjUXoyi2HMNdFshsKsDvL3OLHWcvbwxQojIzcTl8uf6VVhe2frSZc5vowPemSpqXRELwNHn1HpqMu4TuyIDJweX9jeeFTFmLBXzbRBAAaHiNxzVg%2BeakA%3D\" rel=\"nofollow\">http:\/\/atp-modelzoo-sh.oss-cn&#8230;<\/a><\/p>\n<h2>\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u6570\u636e<\/h2>\n<p><a href=\"https:\/\/link.segmentfault.com\/?enc=R9Vxt9VhQgXP4sm6G4W7FQ%3D%3D.FgHHhQ9GUEXX4%2BrZr7aDgAAJAug%2BcrZ1h9TOWnBDxQ4OnMekG%2FDOnpSxCUYYGMXRGv2XyzewLdCLWfVvRWXaG8BgcBTT4qIh8PK4Fecinl3929Cy1lZ7vhMhkbfsrY%2Bs879vtCNeLcWJhupwuFdiaZmMM3HvzNtOgommlAbKEYY%3D\" rel=\"nofollow\">http:\/\/atp-modelzoo-sh.oss-cn&#8230;<\/a><br \/><a href=\"https:\/\/link.segmentfault.com\/?enc=ad2EdAZ9PqW4bT%2FwilL3Vw%3D%3D.ouu29QRPcsPQ2v1R4lZZYwmNN9M7XE9zKnZkIXcrkkUgAZgvB5fllrRv6KGlljrKIfW9jUFmKTsxmg6ovffH71%2F5QrzSvfmgHUvhSEvSByPBk5UIIfP5LNpLJDvFpNUmnx6DhDz5Jeiy6WbJANY4pN3RQ0BOq5tD4FjJqe8BpwA%3D\" rel=\"nofollow\">http:\/\/atp-modelzoo-sh.oss-cn&#8230;<\/a>\u4e2d\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u5728Python 3.6\u73af\u5883\u4e0b\uff0c\u6211\u4eec\u9996\u5148\u4ece\u521a\u521a\u5b89\u88c5\u597d\u7684EasyNLP\u4e2d\u5f15\u5165\u6a21\u578b\u8fd0\u884c\u9700\u8981\u7684\u5404\u79cd\u5e93\uff0c\u5e76\u505a\u521d\u59cb\u5316\u3002\u6211\u4eec\u53ea\u9700\u6307\u5b9a pretrain_model_name_or_path=hfl\/macbert-base-zh\uff0c\u5373\u53ef\u4f7f\u7528\u96c6\u6210\u597d\u7684MacBERT\u6a21\u578b\u3002EasyNLP\u4e2d\u96c6\u6210\u4e86\u4e30\u5bcc\u7684\u9884\u8bad\u7ec3\u6a21\u578b\u5e93\uff0c\u5982\u679c\u60f3\u5c1d\u8bd5\u5176\u4ed6\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u5982BERT\u3001RoBERTa\u7b49\uff0c\u4e5f\u53ef\u4ee5\u5728user_defined_parameters\u4e2d\u8fdb\u884c\u76f8\u5e94\u4fee\u6539\uff0c\u5177\u4f53\u7684\u6a21\u578b\u540d\u79f0\u53ef\u89c1\u6a21\u578b\u5217\u8868\u3002EasyNLP\u5f53\u524d\u540c\u65f6\u652f\u6301\u4e2d\u82f1\u6587\u7684\u9605\u8bfb\u7406\u89e3\uff0c\u53ea\u9700\u8981\u5728user_defined_parameters\u4e2d\u6307\u5b9alanguage=zh\uff0c\u5373\u53ef\u6307\u5b9a\u4e2d\u6587\u6587\u672c\u9884\u5904\u7406\u7684\u76f8\u5e94\u914d\u7f6e\u3002import torch.cuda<br \/>from easynlp.appzoo import MachineReadingComprehensionDataset<br \/>from easynlp.appzoo import get_application_predictor, get_application_model, get_application_evaluator, get_application_model_for_evaluation<br \/>from easynlp.core import Trainer, PredictorManager<br \/>from easynlp.utils import initialize_easynlp, get_args, get_pretrain_model_path<br \/>from easynlp.utils.global_vars import parse_user_defined_parameters<\/p>\n<p>initialize_easynlp()<br \/>args = get_args()<br \/>user_defined_parameters = parse_user_defined_parameters(&#8216;pretrain_model_name_or_path=bert-base-uncased language=en qas_id=qas_id answer_name=answer_text start_position_name=start_position_character max_query_length=64 max_answer_length=30 doc_stride=128 n_best_size=10 output_answer_file=dev.ans.csv&#8217;)\u8f7d\u5165\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528EasyNLP\u5185\u7f6e\u7684MachineReadingComprehensionDataset\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u7531\u4e8e\u63cf\u8ff0\u6587\u672c\u53ef\u80fd\u8fc7\u957f\uff0c\u4ece\u800c\u53ef\u80fd\u5bfc\u81f4\u7b54\u6848\u7247\u6bb5\u5728\u8d85\u51famax_seq_length\u540e\u88ab\u622a\u65ad\u7684\u90e8\u5206\u4e2d\u3002\u56e0\u6b64\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4e2d\u901a\u5e38\u91c7\u7528\u201c\u6ed1\u7a97\u6cd5\u201d\uff0c\u5f53\u63cf\u8ff0\u6587\u672c\u8fc7\u957f\u65f6\uff0c\u4f7f\u7528\u6ed1\u52a8\u7a97\u53e3\u5c06\u5b8c\u6574\u7684\u63cf\u8ff0\u6587\u672c\u62c6\u6210\u51e0\u6bb5\uff0c\u5c06\u4e00\u4e2a\u8f93\u5165case\u62c6\u6210\u591a\u4e2acase\u5206\u522b\u5904\u7406\u3002\u4f7f\u7528user_defined_parameters\u4e2d\u7684doc_stride\u53c2\u6570\u5373\u53ef\u63a7\u5236\u6ed1\u52a8\u7a97\u53e3\u5927\u5c0f\u3002train_dataset = MachineReadingComprehensionDataset(pretrained_model_name_or_path=get_pretrain_model_path(&#8220;bert-base-uncased&#8221;),<\/p>\n<pre><code>                                               data_file=&quot;train_squad.tsv&quot;,\n                                               sequence_length=384,\n                                               input_schema=&quot;qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1&quot;,\n                                               first_sequence=&quot;question_text&quot;,\n                                               second_sequence=&quot;context_text&quot;,\n                                               user_defined_parameters=user_defined_parameters,\n                                               is_training=True\n                                               )\u6a21\u578b\u8bad\u7ec3\u65f6\uff0c\u4f7f\u7528EasyNLP\u4e2d\u5c01\u88c5\u597d\u7684get_application_model\u51fd\u6570\uff0c\u5e76\u6307\u5b9aapp_name=&quot;machine_reading_comprehension&quot;\uff0c\u4fbf\u53ef\u8fdb\u884c\u8bad\u7ec3\u65f6\u7684\u6a21\u578b\u6784\u5efa\u3002\u7531\u4e8e\u6ed1\u52a8\u7a97\u53e3\u673a\u5236\u7684\u5b58\u5728\uff0c\u8bad\u7ec3\u65f6batch_size\u8bbe\u7f6e\u4e0d\u5b9c\u8fc7\u5927\uff0c\u5426\u5219\u5bb9\u6613\u5f15\u8d77OOM\u3002model = get_application_model(app_name=&quot;machine_reading_comprehension&quot;,\n                          pretrained_model_name_or_path=get_pretrain_model_path(&quot;bert-base-uncased&quot;),\n                          user_defined_parameters=user_defined_parameters\n                          )\u6a21\u578b\u8bc4\u4f30\u4e0e\u9884\u6d4b\u65f6\uff0c\u6211\u4eec\u540c\u6837\u53ef\u4ee5\u4f7f\u7528EasyNLP\u4e2d\u5c01\u88c5\u597d\u7684get_application_model_for_evaluation\u548cget_application_predictor\u51fd\u6570\uff0c\u5e76\u5206\u522b\u6307\u5b9aapp_name=&quot;machine_reading_comprehension&quot;\uff0c\u5373\u53ef\u8fdb\u884c\u6a21\u578b\u7684\u8bc4\u4f30\u4e0e\u9884\u6d4b\u3002evaluator = get_application_evaluator(app_name=&quot;machine_reading_comprehension&quot;, \n                                  valid_dataset=valid_dataset,\n                                  eval_batch_size=32, \n                                  user_defined_parameters=user_defined_parameters\n                                  )\u6a21\u578b\u9884\u6d4b\u65f6\uff0c\u7ed3\u679c\u5c06\u8f93\u51fa\u81f3\u6307\u5b9a\u6587\u4ef6\u4e2d\uff0c\u6587\u4ef6\u683c\u5f0fschema\u53ef\u4ee5\u5b9a\u5236\uff0c\u4e00\u822c\u5206\u4e3a\/t\u5206\u9694\u76844\u5217\uff0c\u4f9d\u6b21\u4e3a\uff1a\u6837\u4f8bID\u3001\u7b54\u6848\u6587\u672c\u3001\u95ee\u53e5\u3001\u7bc7\u7ae0\u6587\u672c\u3002\u9664\u4e86\u8f93\u51fa\u7684\u6700\u4f73\u7b54\u6848\u4e4b\u5916\uff0c\u6a21\u578b\u8fd8\u4f1a\u5c06beam_search\u540e\u6392\u5728top_n\u7684\u7b54\u6848\u7ed3\u679c\uff0c\u4e5f\u8f93\u51fa\u5230\u6307\u5b9a\u6587\u4ef6\u4e2d\uff0c\u4f9b\u6a21\u578b\u4f18\u5316\u65f6\u5206\u6790\u6bd4\u5bf9\u3002\u901a\u8fc7user_defined_parameters\u4e2d\u7684n_best_size\u53c2\u6570\uff0c\u5373\u53ef\u5b9a\u5236top_n\u503c\u3002predictor = get_application_predictor(app_name=&quot;machine_reading_comprehension&quot;,\n                                  model_dir=&quot;.\/squad_model_dir\/&quot;, \n                                  first_sequence=&quot;question_text&quot;,\n                                  second_sequence=&quot;context_text&quot;,\n                                  sequence_length=384,\n                                  output_file=&quot;dev.pred.csv&quot;,\n                                  user_defined_parameters=user_defined_parameters\n                                  )\n<\/code><\/pre>\n<p>predictor_manager = PredictorManager(predictor=predictor,<\/p>\n<pre><code>                                 input_file=&quot;dev_squad.tsv&quot;,\n                                 input_schema=&quot;qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1&quot;,\n                                 output_file=&quot;dev.pred.csv&quot;,\n                                 output_schema=&quot;unique_id,best_answer,query,context&quot;,\n                                 append_cols=args.append_cols,\n                                 batch_size=1024\n                                 )\u5f53\u524dEasyNLP\u5df2\u7ecf\u5bf9MacBERT-base\/large\u4e24\u79cd\u6a21\u578b\u8fdb\u884c\u4e86\u96c6\u6210\uff0c\u5728modelzoo\u4e2d\u7684\u6a21\u578b\u8c03\u7528\u540d\u79f0\u5206\u522b\u4e3a\uff1ahfl\/macbert-base-zh \u548c hfl\/macbert-large-zh\uff0c\u4e24\u79cd\u6a21\u578b\u7684\u7ed3\u6784\u53c2\u6570\u5982\u4e0b\u8868\u6240\u793a\u3002\u7528\u6237\u53ef\u76f4\u63a5\u5728pretrain_model_name_or_path\u53c2\u6570\u4e2d\u6307\u5b9a\u540d\u79f0\u8fdb\u884c\u8c03\u7528\u3002\u4e2d\u6587\u7ed3\u6784\u53c2\u6570hfl\/macbert-base-zh12-layer, 768-hidden, 12-heads, 102M parametershfl\/macbert-large-zh24-layer, 1024-hidden, 16-heads, 324M parameters\u6211\u4eec\u4e5f\u4f7f\u7528\u8fd9\u4e24\u79cd\u6a21\u578b\u5206\u522b\u5728\u4e2d\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u6570\u636e\u4e0a\u8fdb\u884c\u4e86 finetune\uff0c\u6548\u679c\u5982\u4e0b\uff1a\u4e2d\u6587EMF1hfl\/macbert-base-zh61.3578.62hfl\/macbert-large-zh59.1579.33\u00a0\u4e0a\u8ff0\u6240\u6709\u8bad\u7ec3\/\u8bc4\u4f30\/\u9884\u6d4b\u4ee3\u7801\uff0c\u90fd\u5df2\u7ecf\u88ab\u96c6\u6210\u5728EasyNLP\/examples\/appzoo_tutorials\/machine_reading_comprehension\/main.py\u00a0\u4e2d\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u5e26\u53c2\u6570\u8fd0\u884cmain.py\uff0c\u4e00\u6b65\u6267\u884c\u4e0a\u8ff0\u6240\u6709\u8bad\u7ec3\/\u8bc4\u4f30\/\u9884\u6d4b\u64cd\u4f5c\u3002# \u6a21\u578b\u8bad\u7ec3<\/code><\/pre>\n<p>python main.py \/<\/p>\n<pre><code>--mode train \/\n--app_name=machine_reading_comprehension \/\n--worker_gpu=1 \/\n--tables=train.tsv,dev.tsv \/\n--input_schema=qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1 \/\n--first_sequence=question_text \/\n--second_sequence=context_text \/\n--sequence_length=384 \/\n--checkpoint_dir=.\/model_dir \/\n--learning_rate=3.5e-5 \/\n--epoch_num=3 \/\n--random_seed=42 \/\n--save_checkpoint_steps=500 \/\n--train_batch_size=16 \/\n--user_defined_parameters='\n    pretrain_model_name_or_path=hfl\/macbert-base-zh\n    language=zh\n    answer_name=answer_text\n    qas_id=qas_id\n    start_position_name=start_position_character\n    doc_stride=128\n    max_query_length=64\n'# \u6a21\u578b\u9884\u6d4b<\/code><\/pre>\n<p>python main.py \/<\/p>\n<pre><code>--mode predict \/\n--app_name=machine_reading_comprehension \/\n--worker_gpu=1 \/\n--tables=dev.tsv \/\n--outputs=dev.pred.csv \/\n--input_schema=qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1 \/\n--output_schema=unique_id,best_answer,query,context \/\n--first_sequence=question_text \/\n--second_sequence=context_text \/\n--sequence_length=384 \/\n--checkpoint_dir=.\/model_dir \/\n--micro_batch_size=256 \/\n--user_defined_parameters='\n    pretrain_model_name_or_path=hfl\/macbert-base-zh\n    language=zh\n    qas_id=qas_id\n    answer_name=answer_text\n    start_position_name=start_position_character\n    max_query_length=64\n    max_answer_length=30\n    doc_stride=128\n    n_best_size=10\n    output_answer_file=dev.ans.csv\n'\u00a0\u9664\u4e86main.py\u4e00\u6b65\u6267\u884c\u4e4b\u5916\uff0c\u6211\u4eec\u540c\u6837\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4\u884c\u6267\u884c\u7684\u65b9\u5f0f\u8fdb\u884c\u5feb\u901f\u8bad\u7ec3\/\u9884\u6d4b\uff0c\u547d\u4ee4\u5982\u4e0b\uff1a# \u6a21\u578b\u8bad\u7ec3<\/code><\/pre>\n<p>easynlp \/<\/p>\n<pre><code>--mode train \/\n--app_name=machine_reading_comprehension \/\n--worker_gpu=1 \/\n--tables=train.tsv,dev.tsv \/\n--input_schema=qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1 \/\n--first_sequence=question_text \/\n--second_sequence=context_text \/\n--sequence_length=384 \/\n--checkpoint_dir=.\/model_dir \/\n--learning_rate=3.5e-5 \/\n--epoch_num=5 \/\n--random_seed=42 \/\n--save_checkpoint_steps=600 \/\n--train_batch_size=16 \/\n--user_defined_parameters='\n    pretrain_model_name_or_path=hfl\/macbert-base-zh \n    language=zh\n    answer_name=answer_text\n    qas_id=qas_id\n    start_position_name=start_position_character\n    doc_stride=128\n    max_query_length=64\n'# \u6a21\u578b\u9884\u6d4b<\/code><\/pre>\n<p>easynlp \/<\/p>\n<pre><code>--mode predict \/\n--app_name=machine_reading_comprehension \/\n--worker_gpu=1 \/\n--tables=dev.tsv \/\n--outputs=dev.pred.csv \/\n--input_schema=qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1 \/\n--output_schema=unique_id,best_answer,query,context \/\n--first_sequence=question_text \/\n--second_sequence=context_text \/\n--sequence_length=384 \/\n--checkpoint_dir=.\/model_dir \/\n--micro_batch_size=256 \/\n--user_defined_parameters='\n    pretrain_model_name_or_path=hfl\/macbert-base-zh \n    language=zh\n    qas_id=qas_id\n    answer_name=answer_text\n    start_position_name=start_position_character\n    max_query_length=64\n    max_answer_length=30\n    doc_stride=128\n    n_best_size=10\n    output_answer_file=dev.ans.csv\n'\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u5728EasyNLP\/examples\/appzoo_tutorials\/machine_reading_comprehension\/\u6587\u4ef6\u5939\u4e0b\u5c01\u88c5\u597d\u4e86\u591a\u79cd\u53ef\u76f4\u63a5\u6267\u884c\u7684bash\u811a\u672c\uff0c\u7528\u6237\u540c\u6837\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528bash\u6587\u4ef6\u547d\u4ee4\u884c\u6267\u884c\u7684\u65b9\u5f0f\u6765\u4e00\u6b65\u5b8c\u6210\u6a21\u578b\u7684\u8bad\u7ec3\/\u8bc4\u4f30\/\u9884\u6d4b\u3002bash\u6587\u4ef6\u9700\u8981\u4f20\u5165\u4e24\u4e2a\u53c2\u6570\uff0c\u7b2c\u4e00\u4e2a\u53c2\u6570\u4e3a\u8fd0\u884c\u7a0b\u5e8f\u7684GPU\u7f16\u53f7\uff0c\u4e00\u822c\u4e3a0\uff1b\u7b2c\u4e8c\u4e2a\u53c2\u6570\u4ee3\u8868\u6a21\u578b\u7684\u8bad\u7ec3\/\u8bc4\u4f30\/\u9884\u6d4b\u3002# \u8bad\u7ec3<\/code><\/pre>\n<p>! bash run_train_eval_predict_user_defined_local_zh.sh 0 train<\/p>\n<h2>\u9884\u6d4b<\/h2>\n<p>! bash run_train_eval_predict_user_defined_local_zh.sh 0 predict\u00a0\u6a21\u578b\u8bad\u7ec3\u597d\u4e4b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u4efb\u610f\u4e2d\u6587\u6587\u672c\u8fdb\u884c\u9605\u8bfb\u7406\u89e3\uff0c\u53ea\u8981\u5c06\u6587\u672c\u8f6c\u6210\u7b26\u5408\u4e0a\u8ff0\u6a21\u578b\u8f93\u5165\u7684\u683c\u5f0f\uff0c\u6dfb\u52a0\u76f8\u5e94\u7684\u95ee\u9898\uff0c\u4fbf\u53ef\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u5e76\u5f97\u5230\u7b54\u6848\u3002\u4ee5\u4e0b\u4e3a\u65b0\u534e\u793e\u6700\u65b0\u5173\u4e8e\u201c\u676d\u5dde\u4e9a\u8fd0\u4f1a\u5012\u8ba1\u65f6\u4e00\u5468\u5e74\u201d\u7684\u65b0\u95fb\u7a3f\u4ef6\uff0c\u6211\u4eec\u4ee5\u8fd9\u4e00\u7a3f\u4ef6\u4f5c\u4e3a\u7bc7\u7ae0\u6587\u672c\uff0c\u4eba\u5de5\u6dfb\u52a0\u4e86\u4e24\u4e2a\u95ee\u9898\uff1a\u201c\u676d\u5dde\u4ec0\u4e48\u65f6\u5019\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff1f\u201d &#038; \u201c\u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230\u591a\u5c11\uff1f\u201d\uff0c\u901a\u8fc7\u6a21\u578b\u9884\u6d4b\uff0c\u4fbf\u53ef\u5f97\u5230\u6b63\u786e\u7684\u7ed3\u679c\uff1a\u201c2020\u5e744\u6708\u201d &#038; \u201c480\u516c\u91cc\u201d\u3002CN_01   2020\u5e744\u6708   \u676d\u5dde\u4ec0\u4e48\u65f6\u5019\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff1f   \u65b0\u534e\u793e\u676d\u5dde9\u670822\u65e5\u7535\uff08\u8bb0\u8005\u5546\u610f\u76c8\u590f\u4eae\uff09\u7ade\u8d5b\u573a\u9986\u5168\u90e8\u7ae3\u5de5\u5e76\u5b8c\u6210\u8d5b\u4e8b\u529f\u80fd\u9a8c\u6536\uff0c\u201c\u8fce\u4e9a\u8fd0\u201d\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u5efa\u8bbe\u8fc8\u5165\u5feb\u8f66\u9053\uff0c\u4e9a\u8fd0\u573a\u9986\u63d0\u524d\u5f00\u653e\u6380\u8d77\u5168\u6c11\u5065\u8eab\u70ed\u6f6e\u2026\u202623\u65e5\uff0c\u5ef6\u671f\u540e\u7684\u676d\u5dde\u4e9a\u8fd0\u4f1a\u5c06\u8fce\u6765\u5012\u8ba1\u65f6\u4e00\u5468\u5e74\uff0c\u5404\u9879\u7b79\u5907\u5de5\u4f5c\u4e5f\u7ed3\u51fa\u7d2f\u7d2f\u7855\u679c\uff0c\u201c\u5929\u5802\u4e4b\u57ce\u201d\u518d\u6b21\u84c4\u52bf\u5f85\u53d1\u3002\u529e\u597d\u4e00\u4e2a\u4f1a\uff0c\u63d0\u5347\u4e00\u5ea7\u57ce\u30022020\u5e744\u6708\uff0c\u676d\u5dde\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff0c\u5176\u4e2d\u5305\u62ec\u57fa\u7840\u8bbe\u65bd\u63d0\u5347\u3001\u7eff\u6c34\u9752\u5c71\u5b88\u62a4\u3001\u6570\u5b57\u6cbb\u7406\u8d4b\u80fd\u7b49\u516b\u9879\u5177\u4f53\u884c\u52a8\u3002\u968f\u7740\u4e9a\u8fd0\u7684\u811a\u6b65\u65e5\u76ca\u4e34\u8fd1\uff0c\u676d\u5dde\u897f\u7ad9\u3001\u8427\u5c71\u56fd\u9645\u673a\u573a\u4e09\u671f\u3001\u5408\u676d\u9ad8\u94c1\u6e56\u676d\u6bb5\u3001\u673a\u573a\u8f68\u9053\u5feb\u7ebf\uff0819\u53f7\u7ebf\uff09\u7b49\u201c\u4e24\u70b9\u4e24\u7ebf\u201d\u91cd\u5927\u9879\u76ee\u6b63\u5f0f\u6295\u8fd0\u3002\u6839\u636e\u676d\u5dde\u5e02\u57ce\u4e61\u5efa\u8bbe\u59d4\u5458\u4f1a\u53d1\u5e03\u7684\u4fe1\u606f\uff0c\u9884\u8ba1\u5230\u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230480\u516c\u91cc\u3002\u4f4f\u5728\u8fd9\u91cc\u7684\u4eba\u4eec\u6b63\u5207\u8eab\u4f53\u4f1a\u5230\u6084\u7136\u53d1\u751f\u7684\u53d8\u5316\u2014\u2014\u4ea4\u901a\u65b9\u4fbf\u4e86\uff0c\u9053\u8def\u53d8\u7f8e\u4e86\uff0c\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u4e5f\u6108\u53d1\u5b8c\u5584\u3002<br \/>CN_02   480\u516c\u91cc   \u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230\u591a\u5c11\uff1f   \u65b0\u534e\u793e\u676d\u5dde9\u670822\u65e5\u7535\uff08\u8bb0\u8005\u5546\u610f\u76c8\u590f\u4eae\uff09\u7ade\u8d5b\u573a\u9986\u5168\u90e8\u7ae3\u5de5\u5e76\u5b8c\u6210\u8d5b\u4e8b\u529f\u80fd\u9a8c\u6536\uff0c\u201c\u8fce\u4e9a\u8fd0\u201d\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u5efa\u8bbe\u8fc8\u5165\u5feb\u8f66\u9053\uff0c\u4e9a\u8fd0\u573a\u9986\u63d0\u524d\u5f00\u653e\u6380\u8d77\u5168\u6c11\u5065\u8eab\u70ed\u6f6e\u2026\u202623\u65e5\uff0c\u5ef6\u671f\u540e\u7684\u676d\u5dde\u4e9a\u8fd0\u4f1a\u5c06\u8fce\u6765\u5012\u8ba1\u65f6\u4e00\u5468\u5e74\uff0c\u5404\u9879\u7b79\u5907\u5de5\u4f5c\u4e5f\u7ed3\u51fa\u7d2f\u7d2f\u7855\u679c\uff0c\u201c\u5929\u5802\u4e4b\u57ce\u201d\u518d\u6b21\u84c4\u52bf\u5f85\u53d1\u3002\u529e\u597d\u4e00\u4e2a\u4f1a\uff0c\u63d0\u5347\u4e00\u5ea7\u57ce\u30022020\u5e744\u6708\uff0c\u676d\u5dde\u53d1\u5e03\u4e86\u300a\u676d\u5dde\u5e02\u4e9a\u8fd0\u57ce\u5e02\u884c\u52a8\u8ba1\u5212\u7eb2\u8981\u300b\uff0c\u5176\u4e2d\u5305\u62ec\u57fa\u7840\u8bbe\u65bd\u63d0\u5347\u3001\u7eff\u6c34\u9752\u5c71\u5b88\u62a4\u3001\u6570\u5b57\u6cbb\u7406\u8d4b\u80fd\u7b49\u516b\u9879\u5177\u4f53\u884c\u52a8\u3002\u968f\u7740\u4e9a\u8fd0\u7684\u811a\u6b65\u65e5\u76ca\u4e34\u8fd1\uff0c\u676d\u5dde\u897f\u7ad9\u3001\u8427\u5c71\u56fd\u9645\u673a\u573a\u4e09\u671f\u3001\u5408\u676d\u9ad8\u94c1\u6e56\u676d\u6bb5\u3001\u673a\u573a\u8f68\u9053\u5feb\u7ebf\uff0819\u53f7\u7ebf\uff09\u7b49\u201c\u4e24\u70b9\u4e24\u7ebf\u201d\u91cd\u5927\u9879\u76ee\u6b63\u5f0f\u6295\u8fd0\u3002\u6839\u636e\u676d\u5dde\u5e02\u57ce\u4e61\u5efa\u8bbe\u59d4\u5458\u4f1a\u53d1\u5e03\u7684\u4fe1\u606f\uff0c\u9884\u8ba1\u5230\u4eca\u5e749\u6708\u5e95\u5168\u5e02\u5feb\u901f\u8def\u603b\u91cc\u7a0b\u5c06\u8fbe\u5230480\u516c\u91cc\u3002\u4f4f\u5728\u8fd9\u91cc\u7684\u4eba\u4eec\u6b63\u5207\u8eab\u4f53\u4f1a\u5230\u6084\u7136\u53d1\u751f\u7684\u53d8\u5316\u2014\u2014\u4ea4\u901a\u65b9\u4fbf\u4e86\uff0c\u9053\u8def\u53d8\u7f8e\u4e86\uff0c\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u4e5f\u6108\u53d1\u5b8c\u5584\u3002\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3EasyNLP\u540c\u6837\u652f\u6301\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\uff0c\u53ea\u9700\u8981\u5728user_defined_parameters\u4e2d\u6307\u5b9alanguage=en\uff0c\u5373\u53ef\u6307\u5b9a\u82f1\u6587\u6587\u672c\u9884\u5904\u7406\u7684\u76f8\u5e94\u914d\u7f6e\u3002\u4e4b\u540e\u4fbf\u53ef\u9009\u62e9EasyNLP\u4e2d\u5df2\u96c6\u6210\u597d\u7684\u82f1\u6587\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\uff0c\u5982 bert-base-uncased\u3001bert-large-uncased \u7b49\uff0c\u8fdb\u884c\u673a\u5668\u9605\u8bfb\u7406\u89e3\u7684\u8bad\u7ec3\u4e0e\u9884\u6d4b\u3002EasyNLP\u4e2d\u96c6\u6210\u4e86\u4e30\u5bcc\u7684\u9884\u8bad\u7ec3\u6a21\u578b\u5e93\uff0c\u5982\u679c\u60f3\u5c1d\u8bd5\u5176\u4ed6\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u5982RoBERTa\u3001ALBERT\u7b49\uff0c\u4e5f\u53ef\u4ee5\u5728user_defined_parameters\u4e2d\u8fdb\u884c\u76f8\u5e94\u4fee\u6539\uff0c\u5177\u4f53\u7684\u6a21\u578b\u540d\u79f0\u53ef\u89c1\u6a21\u578b\u5217\u8868\u3002\u6a21\u578b\u7684\u6570\u636e\u8f7d\u5165\u3001\u8bad\u7ec3\u3001\u8bc4\u4f30\u3001\u9884\u6d4b\u90e8\u5206\u4ee3\u7801\u8c03\u7528\u65b9\u5f0f\u4e0e\u4e0a\u8ff0\u4e2d\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4e2d\u5b8c\u5168\u4e00\u81f4\uff0cEasyNLP\u5df2\u5c06\u4e2d\u82f1\u6587\u5dee\u5f02\u6027\u5904\u7406\u7684\u4ee3\u7801\u96c6\u6210\u5728\u5404\u90e8\u5206\u51fd\u6570\u4e2d\uff0c\u7528\u6237\u53ea\u9700\u7b80\u5355\u6307\u5b9a\u53c2\u6570language=en\/zh\uff0c\u5373\u53ef\u81ea\u5982\u5207\u6362\u4e2d\u82f1\u6587\u76f8\u5e94\u914d\u7f6e\u3002\u6211\u4eec\u5bf9 bert-base-uncased \u548c bert-large-uncased \u4e24\u4e2a\u6a21\u578b\u5206\u522b\u5728\u82f1\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u6570\u636e\u4e0a\u8fdb\u884c\u4e86 finetune\uff0c\u6a21\u578b\u7ed3\u679c\u5982\u4e0b\uff1a\u82f1\u6587EMF1bert-base-uncased76.6485.33bert-large-uncased79.5987.68\u00a0\u00a0\u4e0e\u4e2d\u6587\u673a\u5668\u9605\u8bfb\u7406\u89e3\u4e00\u6837\uff0c\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u5e26\u53c2\u6570\u8fd0\u884cmain.py\uff08\u6307\u5b9alanguage=en\u548c\u76f8\u5e94\u7684pretrain_model_name_or_path\uff09\uff0c\u4e00\u6b65\u6267\u884c\u6240\u6709\u8bad\u7ec3\/\u8bc4\u4f30\/\u9884\u6d4b\u64cd\u4f5c\u3002# \u6a21\u578b\u8bad\u7ec3<br \/>python main.py \/<\/p>\n<pre><code>--mode train \/\n--app_name=machine_reading_comprehension \/\n--worker_gpu=1 \/\n--tables=train.tsv,dev.tsv \/\n--input_schema=qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1 \/\n--first_sequence=question_text \/\n--second_sequence=context_text \/\n--sequence_length=384 \/\n--checkpoint_dir=.\/model_dir \/\n--learning_rate=3.5e-5 \/\n--epoch_num=3 \/\n--random_seed=42 \/\n--save_checkpoint_steps=500 \/\n--train_batch_size=16 \/\n--user_defined_parameters='\n    pretrain_model_name_or_path=bert-base-uncased\n    language=en\n    answer_name=answer_text\n    qas_id=qas_id\n    start_position_name=start_position_character\n    doc_stride=128\n    max_query_length=64\n'# \u6a21\u578b\u9884\u6d4b<\/code><\/pre>\n<p>python main.py \/<\/p>\n<pre><code>--mode predict \/\n--app_name=machine_reading_comprehension \/\n--worker_gpu=1 \/\n--tables=dev.tsv \/\n--outputs=dev.pred.csv \/\n--input_schema=qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1 \/\n--output_schema=unique_id,best_answer,query,context \/\n--first_sequence=question_text \/\n--second_sequence=context_text \/\n--sequence_length=384 \/\n--checkpoint_dir=.\/model_dir \/\n--micro_batch_size=256 \/\n--user_defined_parameters='\n    pretrain_model_name_or_path=bert-base-uncased\n    language=en\n    qas_id=qas_id\n    answer_name=answer_text\n    start_position_name=start_position_character\n    max_query_length=64\n    max_answer_length=30\n    doc_stride=128\n    n_best_size=10\n    output_answer_file=dev.ans.csv\n'\u00a0\u9664\u4e86main.py\u4e00\u6b65\u6267\u884c\u4e4b\u5916\uff0c\u6211\u4eec\u540c\u6837\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4\u884c\u6267\u884c\u7684\u65b9\u5f0f\u8fdb\u884c\u5feb\u901f\u8bad\u7ec3\/\u9884\u6d4b\uff0c\u547d\u4ee4\u5982\u4e0b\uff1a# \u6a21\u578b\u8bad\u7ec3<\/code><\/pre>\n<p>easynlp \/<\/p>\n<pre><code>--mode train \/\n--app_name=machine_reading_comprehension \/\n--worker_gpu=1 \/\n--tables=train.tsv,dev.tsv \/\n--input_schema=qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1 \/\n--first_sequence=question_text \/\n--second_sequence=context_text \/\n--sequence_length=384 \/\n--checkpoint_dir=.\/model_dir \/\n--learning_rate=3.5e-5 \/\n--epoch_num=5 \/\n--random_seed=42 \/\n--save_checkpoint_steps=600 \/\n--train_batch_size=16 \/\n--user_defined_parameters='\n    pretrain_model_name_or_path=bert-base-uncased\n    language=en\n    answer_name=answer_text\n    qas_id=qas_id\n    start_position_name=start_position_character\n    doc_stride=128\n    max_query_length=64\n'# \u6a21\u578b\u9884\u6d4b<\/code><\/pre>\n<p>easynlp \/<\/p>\n<pre><code>--mode predict \/\n--app_name=machine_reading_comprehension \/\n--worker_gpu=1 \/\n--tables=dev.tsv \/\n--outputs=dev.pred.csv \/\n--input_schema=qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1 \/\n--output_schema=unique_id,best_answer,query,context \/\n--first_sequence=question_text \/\n--second_sequence=context_text \/\n--sequence_length=384 \/\n--checkpoint_dir=.\/model_dir \/\n--micro_batch_size=256 \/\n--user_defined_parameters='\n    pretrain_model_name_or_path=bert-base-uncased\n    language=en\n    qas_id=qas_id\n    answer_name=answer_text\n    start_position_name=start_position_character\n    max_query_length=64\n    max_answer_length=30\n    doc_stride=128\n    n_best_size=10\n    output_answer_file=dev.ans.csv\n'\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u5728EasyNLP\/examples\/appzoo_tutorials\/machine_reading_comprehension\/\u6587\u4ef6\u5939\u4e0b\u5c01\u88c5\u597d\u4e86\u591a\u79cd\u53ef\u76f4\u63a5\u6267\u884c\u7684bash\u811a\u672c\uff0c\u7528\u6237\u540c\u6837\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528bash\u6587\u4ef6\u547d\u4ee4\u884c\u6267\u884c\u7684\u65b9\u5f0f\u6765\u4e00\u6b65\u5b8c\u6210\u6a21\u578b\u7684\u8bad\u7ec3\/\u8bc4\u4f30\/\u9884\u6d4b\u3002bash\u6587\u4ef6\u9700\u8981\u4f20\u5165\u4e24\u4e2a\u53c2\u6570\uff0c\u7b2c\u4e00\u4e2a\u53c2\u6570\u4e3a\u8fd0\u884c\u7a0b\u5e8f\u7684GPU\u7f16\u53f7\uff0c\u4e00\u822c\u4e3a0\uff1b\u7b2c\u4e8c\u4e2a\u53c2\u6570\u4ee3\u8868\u6a21\u578b\u7684\u8bad\u7ec3\/\u8bc4\u4f30\/\u9884\u6d4b\u3002# \u6a21\u578b\u8bad\u7ec3<\/code><\/pre>\n<p>! bash run_train_eval_predict_user_defined_local_en.sh 0 train<\/p>\n<h2>\u6a21\u578b\u9884\u6d4b<\/h2>\n<p>! bash run_train_eval_predict_user_defined_local_en.sh 0 predict\u00a0\u6a21\u578b\u8bad\u7ec3\u597d\u4e4b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u4efb\u610f\u82f1\u6587\u6587\u672c\u8fdb\u884c\u9605\u8bfb\u7406\u89e3\uff0c\u53ea\u8981\u5c06\u6587\u672c\u8f6c\u6210\u7b26\u5408\u4e0a\u8ff0\u6a21\u578b\u8f93\u5165\u7684\u683c\u5f0f\uff0c\u6dfb\u52a0\u76f8\u5e94\u7684\u95ee\u9898\uff0c\u4fbf\u53ef\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u5e76\u5f97\u5230\u7b54\u6848\u3002\u6211\u4eec\u9009\u53d6\u8fd1\u65e5\u8d39\u5fb7\u52d2\u9000\u5f79\u540e\uff0c\u7eb3\u8fbe\u5c14\u4e3a\u4ed6\u53d1\u8868\u7684Twitter\u4f5c\u4e3a\u7bc7\u7ae0\u6587\u672c\uff0c\u4eba\u5de5\u6dfb\u52a0\u4e24\u4e2a\u95ee\u9898\uff1a&#8221;Where will the Laver Cup take place?&#8221; &#038; &#8220;Who is he&#8217;s wife?&#8221;\uff0c\u901a\u8fc7\u6a21\u578b\u9884\u6d4b\uff0c\u4fbf\u53ef\u5f97\u5230\u6b63\u786e\u7684\u7ed3\u679c\uff1a&#8221;London&#8221; &#038; &#8220;Mirka&#8221;\u3002EN_01   London    Where will the Laver Cup take place?    I wish this day had never come. It is a sad day for me personally and for people all over the world who love sports. I told you when we talked and here it is. It has been a pleasure, but also an honor and a privilege to share all these years with you, having so many wonderful moments together, on and off the court. We will still have many moments to share to share together in the future, but there are still many things to do together, we know. For now, I would sincerely wish you the best with your wife Mirka, your children, your family and enjoy for what the future will give you. We will see you in London for the Laver Cup.<br \/>EN_02   Mirka,    Who is he&#8217;s wife?   I wish this day had never come. It is a sad day for me personally and for people all over the world who love sports. I told you when we talked and here it is. It has been a pleasure, but also an honor and a privilege to share all these years with you, having so many wonderful moments together, on and off the court. We will still have many moments to share to share together in the future, but there are still many things to do together, we know. For now, I would sincerely wish you the best with your wife Mirka, your children, your family and enjoy for what the future will give you. We will see you in London for the Laver Cup.\u672a\u6765\u5c55\u671b\u5728\u672a\u6765\uff0c\u6211\u4eec\u8ba1\u5212\u5728EasyNLP\u6846\u67b6\u4e2d\u8fdb\u4e00\u6b65\u5b8c\u5584NLU\u76f8\u5173\u80fd\u529b\uff0c\u5e76\u5728\u6b64\u57fa\u7840\u4e0a\u66f4\u8fdb\u4e00\u6b65\uff0c\u5f00\u53d1\u767d\u76d2\u5316\u53ef\u62c6\u89e3\u53ef\u8fc1\u79fb\u590d\u7528\u7684\u591a\u8f6e\u667a\u80fd\u5bf9\u8bdd\u7cfb\u7edf\uff0c\u656c\u8bf7\u671f\u5f85\u3002\u6211\u4eec\u4e5f\u5c06\u5728EasyNLP\u6846\u67b6\u4e2d\u96c6\u6210\u66f4\u591aSOTA\u6a21\u578b\uff08\u7279\u522b\u662f\u4e2d\u2f42\u6a21\u578b\uff09\uff0c\u6765\u2f40\u6301\u5404\u79cdNLP\u548c\u591a\u6a21\u6001\u4efb\u52a1\u3002\u6b64\u5916\uff0c\u963f\u2fa5\u4e91\u673a\u5668\u5b66\u4e60PAI\u56e2\u961f\u4e5f\u5728\u6301\u7eed\u63a8\u8fdb\u4e2d\u6587\u9605\u8bfb\u7406\u89e3\u3001\u5bf9\u8bdd\u7cfb\u7edf\u53ca\u4e2d\u2f42\u591a\u6a21\u6001\u6a21\u578b\u7684\u2f83\u7814\u2f2f\u4f5c\uff0c\u6b22\u8fce\u2f64\u6237\u6301\u7eed\u5173\u6ce8\u6211\u4eec\uff0c\u4e5f\u6b22\u8fce\u52a0\u2f0a\u6211\u4eec\u7684\u5f00\u6e90\u793e\u533a\uff0c\u5171\u5efa\u4e2d\u2f42NLP\u548c\u591a\u6a21\u6001\u7b97\u6cd5\u5e93\uff01Github\u5730\u5740\uff1a<a href=\"https:\/\/link.segmentfault.com\/?enc=iG7d93PELVBvgZxNPokBow%3D%3D.E4VHPrcPgNqNfbvDfhdrdoishNuB9AsTene3FJiQaiVS%2FS%2FDFByFdLmUrDScLl7t\" rel=\"nofollow\">https:\/\/github.com\/alibaba\/Ea&#8230;<\/a>\u53c2\u8003\u6587\u732eChengyu Wang, et al. \u00a0EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing. \u00a0arXiv preprintDanqi Chen. \u00a0Neural Reading Comprehension and Beyond. \u00a0Stanford dissertation.Shanshan Liu. \u00a0Neural Machine Reading Comprehension: Methods and Trends. \u00a0arXiv preprintRajpurkar, et al. &#8220;SQuAD: 100,000+ Questions for Machine Comprehension of Text.&#8221;\u00a0EMNLP 2016Devlin, Jacob, et al. \u00a0&#8220;BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.&#8221; \u00a0NAACL 2019Yiming Cui, et al. &#8220;A Span-Extraction Dataset for Chinese Machine Reading Comprehension&#8221; \u00a0EMNLP 2019Yiming Cui, et al. &#8220;Pre-Training with Whole Word Masking for Chinese BERT.&#8221; \u00a0TASLP 2021\u963f\u91cc\u7075\u6770\u56de\u987e\u963f\u91cc\u7075\u6770\uff1a\u963f\u91cc\u4e91\u673a\u5668\u5b66\u4e60PAI\u5f00\u6e90\u4e2d\u6587NLP\u7b97\u6cd5\u6846\u67b6EasyNLP\uff0c\u52a9\u529bNLP\u5927\u6a21\u578b\u843d\u5730\u963f\u91cc\u7075\u6770\uff1a\u9884\u8bad\u7ec3\u77e5\u8bc6\u5ea6\u91cf\u6bd4\u8d5b\u593a\u51a0\uff01\u963f\u91cc\u4e91PAI\u53d1\u5e03\u77e5\u8bc6\u9884\u8bad\u7ec3\u5de5\u5177\u963f\u91cc\u7075\u6770\uff1aEasyNLP\u5e26\u4f60\u73a9\u8f6cCLIP\u56fe\u6587\u68c0\u7d22\u963f\u91cc\u7075\u6770\uff1aEasyNLP\u4e2d\u6587\u6587\u56fe\u751f\u6210\u6a21\u578b\u5e26\u4f60\u79d2\u53d8\u827a\u672f\u5bb6\u963f\u91cc\u7075\u6770\uff1aEasyNLP\u96c6\u6210K-BERT\u7b97\u6cd5\uff0c\u501f\u52a9\u77e5\u8bc6\u56fe\u8c31\u5b9e\u73b0\u66f4\u4f18Finetune\u963f\u91cc\u7075\u6770\uff1aEasyNLP\u73a9\u8f6c\u6587\u672c\u6458\u8981\uff08\u65b0\u95fb\u6807\u9898\uff09\u751f\u6210\u539f\u6587\u94fe\u63a5\uff1a<a href=\"https:\/\/link.segmentfault.com\/?enc=m1XU7gO0oxc4BnJsYRQzQw%3D%3D.MiK9eFxoGigFdE%2B9CXGzd9FdKKr4J9EpD4KbtGzcKVZbLvGvl8XlqI%2Bzv4xcfkAQ\" rel=\"nofollow\">https:\/\/click.aliyun.com\/m\/10&#8230;<\/a>\u672c\u6587\u4e3a\u963f\u91cc\u4e91\u539f\u521b\u5185\u5bb9\uff0c\u672a\u7ecf\u5141\u8bb8\u4e0d\u5f97\u8f6c\u8f7d\u3002<\/p>\n<p><a href=\"https:\/\/segmentfault.com\/a\/1190000042566813\">https:\/\/segmentfault.com\/a\/1190000042566813<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u7b80\u4ecb\uff1a\u00a0\u672c\u2f42\u5c06\u63d0\u4f9b\u5bf9MacBERT\u6a21\u578b\u7684\u6280\u672f\u89e3\u8bfb\uff0c\u4ee5\u53ca\u5982\u4f55\u5728EasyNLP\u6846\u67b6\u4e2d\u4f7f\u2f64MacBERT\u53ca\u5176\u4ed6\u9884\u8bad\u7ec3 [&hellip;]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-10006642","post","type-post","status-publish","format-standard","hentry"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/itmax.cn\/index.php?rest_route=\/wp\/v2\/posts\/10006642","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/itmax.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/itmax.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/itmax.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=10006642"}],"version-history":[{"count":0,"href":"https:\/\/itmax.cn\/index.php?rest_route=\/wp\/v2\/posts\/10006642\/revisions"}],"wp:attachment":[{"href":"https:\/\/itmax.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10006642"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/itmax.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10006642"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/itmax.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10006642"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}