{"id":4188,"date":"2022-07-23T22:08:49","date_gmt":"2022-07-23T14:08:49","guid":{"rendered":"http:\/\/www.sniper97.cn\/?p=4188"},"modified":"2022-07-23T22:08:49","modified_gmt":"2022-07-23T14:08:49","slug":"%e3%80%90arxiv%e3%80%91nsp-bert-a-prompt-based-zero-shot-learner-through-an-original-pre-training-task-next-sentence-prediction","status":"publish","type":"post","link":"http:\/\/www.sniper97.cn\/index.php\/note\/deep-learning\/paper-deep-learning\/4188\/","title":{"rendered":"\u3010arxiv\u3011NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task–Next Sentence Prediction"},"content":{"rendered":"\n

<Bert>\u3001<NSP>\u3001<zero-shot><\/p>\n\n\n

\u8bba\u6587\u89e3\u8bfb\u4ec5\u4ee3\u8868\u4e2a\u4eba\u89c2\u70b9\uff0c\u624d\u758f\u5b66\u6d45\uff0c\u5982\u6709\u9519\u8bef\u6b22\u8fce\u6307\u6b63\uff0c\u672a\u7ecf\u6388\u6743\u7981\u6b62\u8f6c\u8f7d\u3002<\/p>\n\n\n

\u8bba\u6587\u539f\u6587\uff1ahttps:\/\/arxiv.org\/abs\/2109.03564<\/a><\/a><\/p>\n\n\n

\u8fd9\u662f\u4e00\u7bc7\u5c1a\u672a\u6536\u5f55\u7684\u6587\u7ae0\uff0c\u76ee\u524d\u5728arxiv\u4e0b\uff0c\u6839\u636e\u6539\u8bba\u6587github<\/a>\u66f4\u65b0\u663e\u793a\uff0c\u8be5\u4ed3\u5e93\u4e0d\u4e45\u540e\u4f1a\u6709\u4e00\u4e2a\u8f83\u5927\u66f4\u65b0\uff0c\u731c\u6d4b\u662f\u8bba\u6587\u5f55\u7528\u4ee5\u53ca\u5b9e\u9a8c\u8865\u5145\u3002<\/p>\n\n\n

\u6458\u8981<\/h2>\n\n\n

\u8fd1\u5e74\u6765\uff0c\u5f88\u591a\u5b9e\u9a8c\u90fd\u8868\u660eNSP\u662f\u4e00\u4e2a\u5931\u8d25\u7684\u9884\u8bad\u7ec3\u4efb\u52a1\uff0c\u76f8\u8f83\u4e8eMLM\u4efb\u52a1\uff0cNSP\u4efb\u52a1\u8fc7\u4e8e\u7b80\u5355\uff0c\u56e0\u6b64\u5728\u6a21\u578b\u8bad\u7ec3\u4e0a\u5e76\u6ca1\u6709\u4ec0\u4e48\u660e\u663e\u7684\u63d0\u5347\uff0c\u8bad\u7ec3\u8fc7\u7a0b\u4e2dNSP\u7684acc\u5f88\u5feb\u5c31\u4f1a\u8fbe\u523090%\u751a\u81f3\u66f4\u9ad8\u3002<\/p>\n\n\n

\u56e0\u6b64\u4f8b\u5982Roberta\u3001Albert\u7b49\uff0c\u5747\u5c06NSP\u4efb\u52a1\u505a\u4e86\u4e0d\u540c\u7a0b\u5ea6\u7684\u4fee\u6539\u3002<\/p>\n\n\n

\u4f46\u662f\u672c\u6587\u63ed\u793a\u4e86Bert\u7684NSP\u4e00\u4e2a\u5176\u4ed6\u7528\u9014\uff0c\u901a\u8fc7\u8be5\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u4ee5zero-shot\u6216\u8005few-shot\u7684\u65b9\u5f0f\u8fdb\u884c\u4f8b\u5982\u6587\u672c\u5206\u7c7b\u3001\u5b8c\u5f62\u586b\u7a7a\u7b49\u4efb\u52a1\uff0c\u4ece\u800c\u83b7\u5f97\u66f4\u591a\u7684\u4f2a\u6807\u7b7e\u4ee5\u4f5c\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u3002<\/p>\n\n\n

\u65b9\u6cd5<\/h2>\n\n\n

\u672c\u6587\u7684\u65b9\u6cd5\u5982\u4e0b\uff0c\u5bf9\u4e8e\u4e0d\u540c\u7684\u4efb\u52a1\uff0c\u5728\u7b2c\u4e8c\u53e5\u8f93\u5165\u4e0d\u540c\u7684\u9009\u9879\uff0c\u901a\u8fc7Bert\u7684NSP\u8f93\u51fa\u5224\u65ad\u9009\u9879\u662f\u5426\u201c\u5408\u7406\u201d\u3002<\/p>\n\n\n

\"\"\/<\/figure><\/div>\n\n\n

\u5b9e\u9a8c<\/h2>\n\n\n

\u7b49\u5168\u6587\u53d1\u8868\u540e\u8865\u8db3\u3002<\/p>\n\n\n

\u7ed3\u8bba<\/h2>\n\n\n

\n\u7b49\u5168\u6587\u53d1\u8868\u540e\u8865\u8db3\u3002\n<\/p>\n\n\n

\u603b\u7ed3<\/h2>\n\n\n

\u5176\u5b9e\u8fd9\u7bc7\u6587\u7ae0\u5df2\u7ecf\u51fa\u73b0\u8fc7\u4e00\u6bb5\u65f6\u95f4\u4e86\uff0c\u540c\u65f6\u5904\u4e8e\u201c\u5c1a\u672a\u53d1\u8868\u201d\u7684\u72b6\u6001\uff0c \u662f\u4e00\u4e2a\u53c8\u65b0\u53c8\u65e7\u7684\u8bba\u6587\uff0c\u4e4b\u6240\u4ee5\u51b7\u996d\u70ed\u7092\u62ff\u51fa\u6765\uff0c\u5c31\u662f\u56e0\u4e3a\u6700\u8fd1\u53ef\u80fd\u9700\u8981\u4e00\u4e9b\u201c\u4f2a\u6807\u7b7e\u201d\u7c7b\u4f3c\u7684\u6570\u636e\u6807\u6ce8\uff0c\u505a\u4e86\u4e00\u4e0b\u76f8\u5173\u7684\u5b9e\u9a8c\uff0c\u62ff\u51fa\u6765\u6574\u7406\u6c47\u603b\u4e00\u4e0b\u3002<\/p>\n\n\n

\u4e0b\u9762\u662f\u6211\u7684\u5b9e\u9a8c\u90e8\u5206\u3002<\/p>\n\n\n

\u5b9e\u9a8c\u4ee3\u7801\u6bd4\u8f83\u7b80\u5355\uff0c\u56e0\u4e3a\u5c31\u662f\u57fa\u4e8e\u539f\u751fBert\u7684NSP\u7ed3\u679c\uff08\u4ee5\u4e0b\u5b9e\u9a8c\u7ed3\u679c\u5747\u57fa\u4e8ezero-shot\uff09\u3002<\/p>\n\n\n

import os\nos.environ['TF_KERAS'] = '1'\nfrom bert4keras.tokenizers import Tokenizer\nfrom bert4keras.snippets import sequence_padding\nfrom bert4keras.models import build_transformer_model\ntexts = [\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u662f\u4e00\u7bc7\u4f53\u80b2\u65b0\u95fb'],\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u662f\u4e00\u7bc7\u97f3\u4e50\u65b0\u95fb'],\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u662f\u4e00\u7bc7\u5a31\u4e50\u65b0\u95fb'],\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u8fd9\u662f\u4e00\u7bc7\u4f53\u80b2\u65b0\u95fb'],\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u8fd9\u662f\u4e00\u7bc7\u97f3\u4e50\u65b0\u95fb'],\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u8fd9\u662f\u4e00\u7bc7\u5a31\u4e50\u65b0\u95fb'],\n]\nmodel = build_transformer_model(\n    config_path='.\/chinese_L-12_H-768_A-12\/bert_config.json',\n    checkpoint_path='.\/chinese_L-12_H-768_A-12\/bert_model.ckpt',\n    with_nsp=True\n)\ntokenizer = Tokenizer('.\/chinese_L-12_H-768_A-12\/vocab.txt')\nbatch_token_ids = []\nbatch_segment_ids = []\nfor text in texts:\n    token_ids, segment_ids = tokenizer.encode(text[0], text[1])\n    batch_token_ids.append(token_ids)\n    batch_segment_ids.append(segment_ids)\nbatch_token_ids = sequence_padding(batch_token_ids)\nbatch_segment_ids = sequence_padding(batch_segment_ids)\noutput = model.predict([batch_token_ids, batch_segment_ids])<\/code><\/pre>\n\n\n
\u5bf9\u4e8e\u6587\u672c\u5206\u7c7b\u4efb\u52a1\uff0c\u8bba\u6587\u4f7f\u7528\u7684\u662fprompt\u5f62\u5f0f\u7684\u8f93\u5165\uff08idx:0\u8d8a\u5927\u4ee3\u8868next\u7684\u6982\u7387\u8d8a\u5927\uff09\uff1a<\/p>\n\n\n
    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u662f\u4e00\u7bc7\u4f53\u80b2\u65b0\u95fb'],0.99795,0.00205\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u662f\u4e00\u7bc7\u97f3\u4e50\u65b0\u95fb'],0.08799,0.91201\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u662f\u4e00\u7bc7\u5a31\u4e50\u65b0\u95fb'],0.86283,0.13717\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u8fd9\u662f\u4e00\u7bc7\u4f53\u80b2\u65b0\u95fb'],0.93378,0.06622\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u8fd9\u662f\u4e00\u7bc7\u97f3\u4e50\u65b0\u95fb'],0.99938,0.00062\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u8fd9\u662f\u4e00\u7bc7\u5a31\u4e50\u65b0\u95fb'],0.99727,0.00273<\/code><\/pre>\n\n\n
\u53ef\u4ee5\u770b\u51fa\uff0c\u6574\u4f53\u6765\u8bf4\uff0c\u7b2c\u4e00\u6761\u88ab\u9884\u6d4b\u4e3a\u4f53\u80b2\uff0c\u7b2c\u4e8c\u6761\u88ab\u9884\u6d4b\u4e3a\u97f3\u4e50\/\u5a31\u4e50\u3002\u4e2a\u4eba\u8ba4\u4e3a\u8fd9\u4e24\u4e2a\u90fd\u8fd8\u7b97\u5bf9\u3002<\/p>\n\n\n
\u4f46\u662f\u6211\u53d1\u73b0\uff0czero-shot\u6a21\u5f0f\u4e0b\uff0c\u4e00\u4e2a\u5b57\u7b26\u7684\u53d8\u5316\u5c31\u53ef\u80fd\u4f1a\u5bf9\u7ed3\u679c\u4ea7\u751f\u5f88\u5927\u7684\u5f71\u54cd\uff1a<\/p>\n\n\n
    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u662f\u4e00\u7bc7\u5a31\u4e50\u65b0\u95fb'],0.86283,0.13717\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b\u3002', '\u8fd9\u662f\u4e00\u7bc7\u5a31\u4e50\u65b0\u95fb'],0.04528,0.93378<\/code><\/pre>\n\n\n
\u4f46\u662f\u503c\u76f8\u5bf9\u4e0d\u662f\u5f88\u9ad8\uff0c\u901a\u8fc7\u5361\u9608\u503c\u6216\u8005few-shot\u53ef\u4ee5\u7f13\u89e3\u8fd9\u4e00\u95ee\u9898\u3002<\/p>\n\n\n
\u540c\u65f6\uff0c\u6211\u8fd8\u5c1d\u8bd5\u4e86\u4e0d\u540c\u7684prompt\u65b9\u5f0f\u4e0e\u4e0d\u662f\u7528prompt\uff1a<\/p>\n\n\n
    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u53e5\u8bdd\u662f\u5173\u4e8e\u4f53\u80b2\u7684'],0.72072,0.27928\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u53e5\u8bdd\u662f\u5173\u4e8e\u97f3\u4e50\u7684'],0.00951,0.99049\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u8fd9\u53e5\u8bdd\u662f\u5173\u4e8e\u5a31\u4e50\u7684'],0.05801,0.94199\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u8fd9\u53e5\u8bdd\u662f\u5173\u4e8e\u4f53\u80b2\u7684'],0.02010,0.97990\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u8fd9\u53e5\u8bdd\u662f\u5173\u4e8e\u97f3\u4e50\u7684'],0.82243,0.17757\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u8fd9\u53e5\u8bdd\u662f\u5173\u4e8e\u5a31\u4e50\u7684'],0.11396,0.88604\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u4f53\u80b2'],0.99745,0.00255\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u97f3\u4e50'],0.02688,0.97312\n    ['\u6885\u897f\u8d85\u8d8a\u8d1d\u5229\u6210\u4e3a\u5357\u7f8e\u5c04\u624b\u738b', '\u5a31\u4e50'],0.09481,0.90519\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u4f53\u80b2'],0.01490,0.98510\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u97f3\u4e50'],0.99447,0.00553\n    ['\u8d3e\u65af\u6c40\u6bd4\u4f2f\u53d1\u5e03\u65b0\u5355\u66f2', '\u5a31\u4e50'],0.97993,0.02007<\/code><\/pre>\n\n\n
\u53ef\u4ee5\u770b\u51fa\u5bf9\u4e8e\u6837\u672c2\uff0c\u66f4\u6362prompt\u65b9\u5f0f\u540e\uff0c\u97f3\u4e50\u7c7b\u522b\u4e0e\u5176\u4ed6\u7c7b\u522b\u7684\u533a\u522b\u66f4\u52a0\u660e\u663e\uff0c\u540c\u65f6\u5bf9\u4e8e\u4e0d\u4f7f\u7528prompt\u7684\u7ed3\u679c\uff0c\u548c\u539f\u6587prompt\u65b9\u5f0f\u7ed3\u679c\u5206\u5e03\u57fa\u672c\u4e00\u81f4\u3002\u8bf4\u660e\u6574\u4f53\u6765\u8bf4NSP-Bert\u6709\u6548\uff0c\u4f46\u662f\u6709\u4e00\u4e9b\u4f9d\u8d56\u4e8eprompt\u65b9\u5f0f\u3002<\/p>\n\n\n
\u800c\u5728\u610f\u56fe\u8bc6\u522b\u4e0a\uff0c<\/p>\n\n\n
    ['ok\u8c22\u8c22', '\u6ca1\u6709'],0.96902,0.99990\n    ['ok\u8c22\u8c22', '\u8c22\u8c22'],0.99990,0.00010\n    ['ok\u8c22\u8c22', '\u518d\u89c1'],0.98421,0.01579\n    ['ok\u8c22\u8c22', '\u597d\u7684'],0.99227,0.00773\n    ['ok\u8c22\u8c22', '\u8fd9\u53e5\u8bdd\u7684\u610f\u56fe\u662f\u201c\u6ca1\u6709\u201d'],0.94576,0.05424\n    ['ok\u8c22\u8c22', '\u8fd9\u53e5\u8bdd\u7684\u610f\u56fe\u662f\u201c\u8c22\u8c22\u201d'],0.99984,0.00016\n    ['ok\u8c22\u8c22', '\u8fd9\u53e5\u8bdd\u7684\u610f\u56fe\u662f\u201c\u518d\u89c1\u201d'],0.98268,0.01732\n    ['ok\u8c22\u8c22', '\u8fd9\u53e5\u8bdd\u7684\u610f\u56fe\u662f\u201c\u597d\u7684\u201d'],0.98432,0.01568<\/code><\/pre>\n\n\n
\u53ef\u4ee5\u53d1\u73b0\u5982\u679c\u5361\u4e00\u4e0b\u9608\u503c\uff0c\u57fa\u672c\u90fd\u5224\u65ad\u5bf9\u4e86\u2014\u2014\u81f3\u5c11\u8fd9\u53e5\u8bdd\u6ca1\u6709\u201c\u6ca1\u6709\u201d\u7684\u610f\u56fe\uff0c\u4e5f\u53ef\u4ee5\u53d1\u73b0\uff0c\u4e00\u4e2a\u597d\u7684prompt\u63d0\u793a\uff0c\u53ef\u4ee5\u62c9\u5927\u4e0d\u540c\u6807\u7b7e\u4e4b\u95f4\u7684\u5dee\u8ddd\u3002<\/p>\n\n\n
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    ['\u6bcf\u4e00\u4e2a\u4ec0\u4e48\u90fd\u6709\u4e24\u9762', '\u786c\u5e01'],0.44779,0.55221\n    ['\u6bcf\u4e00\u4e2a\u4ec0\u4e48\u90fd\u6709\u4e24\u9762', '\u6c7d\u8f66'],0.04262,0.95738<\/code><\/pre>\n\n\n
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