{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":77414,"offset":77404,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u041f\u0440\u043e\u0431\u043b\u0435\u043c\u0430 \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435 `.+?` \u0432 \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u043c \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0438 \u0437\u0430\u0445\u0432\u0430\u0442\u044b\u0432\u0430\u0435\u0442 \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u043c\u043d\u043e\u0433\u043e \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432, \u0432\u043a\u043b\u044e\u0447\u0430\u044f \u043f\u0440\u043e\u0431\u0435\u043b\u044b \u0438 \u0434\u0440\u0443\u0433\u0438\u0435 \u0441\u0438\u043c\u0432\u043e\u043b\u044b. \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0432\u043c\u0435\u0441\u0442\u043e \u044d\u0442\u043e\u0433\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c `[^)]+`, \u0447\u0442\u043e\u0431\u044b \u0437\u0430\u0445\u0432\u0430\u0442\u044b\u0432\u0430\u0442\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u0441\u0438\u043c\u0432\u043e\u043b\u044b \u0434\u043e \u0437\u0430\u043a\u0440\u044b\u0432\u0430\u044e\u0449\u0435\u0439 \u0441\u043a\u043e\u0431\u043a\u0438 `)`.\n\n\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0435 \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u0435 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u043d\u0443\u0436\u043d\u043e\u0433\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0430:\n\n```python\nmath_expressions = re.findall(r'[\\w\\^+\\-]+=[0-9]+|\\w[\\^2]+[\\+\\-]\\d\\w[\\+\\-]\\d|[\\w\\^2\\+\\-]+\\(.*?\\)', stroka)\n```\n\n\u0422\u0435\u043f\u0435\u0440\u044c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0434\u043e\u043b\u0436\u0435\u043d \u0431\u044b\u0442\u044c \u043a\u0430\u043a \u043e\u0436\u0438\u0434\u0430\u043b\u043e\u0441\u044c:\n\n```python\n['x^2-5x-6', 'x-6=0', 'x+1=0', 'x2-5x-6', 'x2+(a+b)x+ab=(x+a)(x+b)', 'x^{2}-5x-6', 'x^{2}+\\\\left(a+b\\\\right)x+ab=\\\\left(x+a\\\\right)\\\\left(x+b\\\\right)']\n```","behavior":"degenerate_repetition","conversation_hash":"ca836bf37dfc707b209eee478130bb71","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-3.5-turbo-0125","n_chars":890,"n_turns_in_conversation":4,"n_words":129,"source_row_offset":77404,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:23:40 GMT","turn_id":"97fb243c5b700175541fc243","turn_index":1,"user_text":"\u0432\u043e\u0442 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 [' ', ' ', ',', ' ', ',', ' ', '', ' ', '', ' ', ' ', ',', ' ', ' ', ' ', ' ', ' ', ' ', '(', ')', '=', ')', ')', ' ', ' ', ',', ' ', '{', '', ' ', ' ', ' ', ' ', '{', '}', '\\\\', '(', '\\r', ')', '=', '(', '\\r', ')', '(', '\\r', ')']"},{"assistant_initiates":false,"asst_text":"\u0415\u0441\u043b\u0438 \u0432\u044b \u043f\u043e\u043b\u0443\u0447\u0438\u043b\u0438 \u043f\u0443\u0441\u0442\u043e\u0439 \u0441\u043f\u0438\u0441\u043e\u043a, \u044d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0441\u0432\u044f\u0437\u0430\u043d\u043e \u0441 \u0442\u0435\u043c, \u0447\u0442\u043e \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u0435 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u043d\u0435 \u043d\u0430\u0439\u0434\u0435\u0442 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u0439 \u0432 \u0432\u0430\u0448\u0435\u0439 \u0441\u0442\u0440\u043e\u043a\u0435. \n\n\u041f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0435 \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u0435 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0431\u043e\u043b\u0435\u0435 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0430\u043b\u044c\u043d\u044b\u043c:\n\n```python\nmath_expressions = re.findall(r'[\\w\\^{}\\\\\\(\\)+\\-]+=[0-9]+|\\w[\\^2]+[\\+\\-]\\d\\w[\\+\\-]\\d|[\\w\\^{}\\\\\\(\\)+\\-\\s]+\\(.*?\\)', stroka)\n```\n\n\u042d\u0442\u043e \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u0435 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0431\u043e\u043b\u044c\u0448\u0435 \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432 \u0434\u043b\u044f \u043f\u043e\u0438\u0441\u043a\u0430 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0439. \u041f\u043e\u0441\u043b\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u044d\u0442\u043e\u0433\u043e \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u044f, \u0434\u043e\u043b\u0436\u043d\u044b \u0431\u044b\u0442\u044c \u043d\u0430\u0439\u0434\u0435\u043d\u044b \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u044f \u0432 \u0432\u0430\u0448\u0435\u0439 \u0441\u0442\u0440\u043e\u043a\u0435.\n\n\u0415\u0441\u043b\u0438 \u043f\u043e\u0441\u043b\u0435 \u044d\u0442\u043e\u0433\u043e \u0432\u0441\u0435 \u0435\u0449\u0435 \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442\u0435 \u043f\u0443\u0441\u0442\u043e\u0439 \u0441\u043f\u0438\u0441\u043e\u043a, \u043f\u043e\u0436\u0430\u043b\u0443\u0439\u0441\u0442\u0430, \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u044c\u0442\u0435 \u0431\u043e\u043b\u0435\u0435 \u043f\u043e\u043b\u043d\u044b\u0439 \u0442\u0435\u043a\u0441\u0442 \u0441\u0442\u0440\u043e\u043a\u0438, \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0430 \u0432 \u0434\u0440\u0443\u0433\u043e\u0439 \u0447\u0430\u0441\u0442\u0438 \u0441\u0442\u0440\u043e\u043a\u0435.","behavior":"degenerate_repetition","conversation_hash":"ca836bf37dfc707b209eee478130bb71","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-3.5-turbo-0125","n_chars":864,"n_turns_in_conversation":4,"n_words":123,"source_row_offset":77405,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:23:40 GMT","turn_id":"0b6b459323c73963d2cff610","turn_index":2,"user_text":"['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']"},{"assistant_initiates":false,"asst_text":"\u041f\u0440\u043e\u0431\u043b\u0435\u043c\u0430 \u0432 \u0434\u0430\u043d\u043d\u043e\u0439 \u043e\u0448\u0438\u0431\u043a\u0435 \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430 \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432 `+-\\s` \u0432 \u0432\u0430\u0448\u0435\u043c \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u043c \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0438\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0438\u0440\u0443\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u043e\u0448\u0438\u0431\u043a\u0430 \u0438\u0437-\u0437\u0430 \u043c\u0435\u0441\u0442\u043e\u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u044f \u0441\u0438\u043c\u0432\u043e\u043b\u0430 `-` \u0432\u043d\u0443\u0442\u0440\u0438 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430.\n\n\u0427\u0442\u043e\u0431\u044b \u0440\u0435\u0448\u0438\u0442\u044c \u044d\u0442\u0443 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0443, \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u043f\u0440\u043e\u0431\u043e\u0432\u0430\u0442\u044c \u044d\u043a\u0440\u0430\u043d\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0441\u0438\u043c\u0432\u043e\u043b `-` \u0438\u043b\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u0441\u0442\u0438\u0442\u044c \u0435\u0433\u043e \u0432 \u043a\u043e\u043d\u0435\u0446 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430. \u0412\u043e\u0442 \u043a\u0430\u043a \u0438\u0441\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u0432\u0430\u0448\u0435 \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u0435 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435:\n\n```python\nmath_expressions = re.findall(r'[\\w\\^{}\\\\\\(\\)+\\s-]+=[0-9]+|\\w[\\^2]+[\\+\\-]\\d\\w[\\+\\-]\\d|[\\w\\^{}\\\\\\(\\)+\\s\\-]+\\(.*?\\)', stroka)\n```\n\n\u0422\u0435\u043f\u0435\u0440\u044c \u0441\u0438\u043c\u0432\u043e\u043b `-` \u0434\u043e\u043b\u0436\u0435\u043d \u0431\u044b\u0442\u044c \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u0430\u043d \u0432\u043d\u0443\u0442\u0440\u0438 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u0432 \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432 \u0432 \u0432\u0430\u0448\u0435\u043c \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u043c \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0438. \u041f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u043e \u0438\u0441\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u043d\u043e\u0435 \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u0435 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043f\u043e\u0438\u0441\u043a\u0430 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0439 \u0432 \u0432\u0430\u0448\u0435\u0439 \u0441\u0442\u0440\u043e\u043a\u0435 \u0431\u0435\u0437 \u043e\u0448\u0438\u0431\u043e\u043a.","behavior":"degenerate_repetition","conversation_hash":"ca836bf37dfc707b209eee478130bb71","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-3.5-turbo-0125","n_chars":849,"n_turns_in_conversation":4,"n_words":96,"source_row_offset":77406,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:23:40 GMT","turn_id":"878f2213ada5f7a2894ef720","turn_index":3,"user_text":"    595                     lo = code1[1]\n    596                     hi = code2[1]\n\nerror: bad character range +-\\s at position 52"},{"assistant_initiates":false,"asst_text":"\"[....] according to [....] the type of [...] see him. It's appropriate for you now to send your Son, Jesus Christ, and bless us, enabling us to defeat snakes, scorpions, and all of the Devil's powers, because he guides us, his followers. Through him, we have come to know you. And we praise you: Praise be to you, the Father through the Son, the Father in the Son, the Father in the Holy Church, and among the holy angels! From now on, he remains forever, for eternity upon eternity. Amen.\"","behavior":"degenerate_repetition","conversation_hash":"8c8a7ee68863e64ce1cbbcdcc031bf52","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-0125-preview","n_chars":1091,"n_turns_in_conversation":1,"n_words":208,"source_row_offset":77407,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:24:09 GMT","turn_id":"be7735ac9e9e75178922e413","turn_index":0,"user_text":"Rewrite in contemporary English.\n\n\"[....] according to [....] the type of [...] see him. It is fitting for you at this time to send thy Son Jesus Christ and anoint us so we might be able to trample upon the snakes and the heads of the scorpions and all the power of the Devil since he is a shepherd of the seed. Through him we have known thee. And we glorify thee : Glory be to thee, the Father in the Son, the Father in the Son, the Father in the Holy Church and in the holy angels! From now he abides forever in the perpetuity of the Aeons, forever until the untraceable Aeons of the Aeons. Amen.\""},{"assistant_initiates":false,"asst_text":"\u8fd9\u4e2a\u516c\u5f0f\u662f\u4e3a\u4e86\u8fdb\u884c\u7279\u5f81\u7684\u6807\u51c6\u5316\u6216\u89c4\u8303\u5316\uff0c\u901a\u5e38\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u4e2d\u6570\u636e\u9884\u5904\u7406\u7684\u6b65\u9aa4\uff0c\u4ee5\u53ca\u5728\u6a21\u578b\u5185\u90e8\u7684\u6279\u89c4\u8303\u5316\uff08Batch Normalization\uff09\u8fc7\u7a0b\u4e2d\u3002\u6807\u51c6\u5316\u516c\u5f0f\u53ef\u4ee5\u5c06\u6570\u636e\u6309\u5176\u5206\u5e03\u7684\u5747\u503c\uff08$\\mu$\uff09\u548c\u65b9\u5dee\uff08$\\sigma^2$\uff09\u91cd\u65b0\u7f29\u653e\uff0c\u4f7f\u5f97\u5904\u7406\u540e\u7684\u6570\u636e\u9075\u5faa\u6807\u51c6\u6b63\u6001\u5206\u5e03\uff0c\u5373\u5747\u503c\u4e3a0\uff0c\u65b9\u5dee\u4e3a1\u3002\n\n\u516c\u5f0f\u4e2d\u7684 $z^{(i)}$ \u8868\u793a\u7b2c $i$ \u4e2a\u6837\u672c\u7684\u7279\u5f81\u503c\uff0c$\\mu$ \u662f\u6240\u6709\u6837\u672c\u7279\u5f81\u503c\u7684\u5747\u503c\uff0c$\\sigma^2$ \u662f\u65b9\u5dee\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u901a\u8fc7\u51cf\u53bb\u5747\u503c\u548c\u9664\u4ee5\u6807\u51c6\u5dee\uff08\u5373\u65b9\u5dee\u7684\u5e73\u65b9\u6839\uff0c$\\sigma$\uff09\uff0c\u5b9e\u73b0\u5c06\u6570\u636e\u4e2d\u5fc3\u5316\u5e76\u7f29\u653e\u5230\u4e00\u4e2a\u5171\u540c\u7684\u5c3a\u5ea6\u3002\n\n$\\epsilon$ \u662f\u4e00\u4e2a\u5f88\u5c0f\u7684\u5e38\u6570\uff0c\u52a0\u5165\u662f\u4e3a\u4e86\u9632\u6b62\u5206\u6bcd\u4e3a\u96f6\u7684\u60c5\u51b5\uff0c\u63d0\u9ad8\u8ba1\u7b97\u7684\u7a33\u5b9a\u6027\u3002\u5728\u6279\u89c4\u8303\u5316\u4e2d\uff0c$\\epsilon$ \u662f\u5fc5\u8981\u7684\uff0c\u56e0\u4e3a\u5b83\u786e\u4fdd\u4e86\u5373\u4f7f\u6240\u6709\u8f93\u5165\u503c\u76f8\u540c\uff08\u8fd9\u5c06\u5bfc\u81f4\u65b9\u5dee\u4e3a\u96f6\uff09\uff0c\u8be5\u64cd\u4f5c\u4e5f\u4e0d\u4f1a\u5bfc\u81f4\u6570\u503c\u9519\u8bef\uff08\u6bd4\u5982\u9664\u4ee5\u96f6\u7684\u9519\u8bef\uff09\u3002\u6b64\u5916\uff0c $\\epsilon$ \u4e5f\u53ef\u4ee5\u5e2e\u52a9\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u56e0\u4e3a\u5b83\u5f15\u5165\u4e86\u4e00\u70b9\u566a\u58f0\uff0c\u76f8\u5f53\u4e8e\u8f7b\u5fae\u7684\u6b63\u5219\u5316\u3002\n\n\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c$\\epsilon$ \u7684\u503c\u901a\u5e38\u975e\u5e38\u5c0f\uff0c\u6bd4\u5982 $10^{-8}$ \u81f3 $10^{-10}$ \u8303\u56f4\u5185\uff0c\u8db3\u5920\u63d0\u4f9b\u6570\u503c\u7a33\u5b9a\u6027\u800c\u5bf9\u5206\u5e03\u5f71\u54cd\u4e0d\u5927\u3002","behavior":"degenerate_repetition","conversation_hash":"4de09490cb7234b0fbd02f3e6ca9ab32","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-0125-preview","n_chars":632,"n_turns_in_conversation":3,"n_words":29,"source_row_offset":77408,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:24:41 GMT","turn_id":"0a781a9f8991755cc9659a47","turn_index":0,"user_text":" \u6807\u51c6\u5316\u516c\u5f0f $z_{norm}^{(i)} = \\frac{z^{(i)}-\\mu}{\\sqrt{\\sigma^2+\\epsilon}}$ \u4e2d $\\epsilon$ \u662f\u4e3a\u4e86\u8c03\u6574 $z_{norm}^{(i)}$\u200b \u7684\u53d6\u503c\u8303\u56f4\u3002"},{"assistant_initiates":false,"asst_text":"\u5728 Python \u4e2d\u4f7f\u7528 NumPy \u5e93\u65f6\uff0c\u5982\u679c\u4f60\u6267\u884c `a=np.random.rand(4,4)`\uff0c\u8fd9\u4f1a\u521b\u5efa\u4e00\u4e2a\u5f62\u72b6\u4e3a `(4, 4)` \u7684\u6570\u7ec4\uff0c\u5373\u4e00\u4e2a 4x4 \u7684\u77e9\u9635\uff0c\u5305\u542b\u4e86\u4ece\u5747\u5300\u5206\u5e03\u4e2d\u62bd\u53d6\u7684\u968f\u673a\u6837\u672c\uff0c\u5176\u503c\u5728[0.0, 1.0)\u533a\u95f4\u3002\n\n\u5f53\u4f60\u4f7f\u7528\u5207\u7247\u64cd\u4f5c `a[1:3]` \u65f6\uff0c\u8fd9\u6307\u7684\u662f\u9009\u53d6 `a` \u6570\u7ec4\u4e2d\u7b2c\u4e8c\u884c (`1`) \u5230\u7b2c\u4e09\u884c (`2`)\uff0c\u4f46\u4e0d\u5305\u62ec\u7b2c\u56db\u884c (`3`)\uff0c\u56e0\u4e3a Python \u7684\u7d22\u5f15\u4ece 0 \u5f00\u59cb\uff0c\u5e76\u4e14\u5207\u7247\u662f\u534a\u5f00\u533a\u95f4\uff08\u5373\u5305\u542b\u5f00\u59cb\u7d22\u5f15\uff0c\u4e0d\u5305\u542b\u7ed3\u675f\u7d22\u5f15\uff09\u3002\n\n\u56e0\u6b64\uff0c`a[1:3]` \u4f1a\u9009\u53d6 `a` \u6570\u7ec4\u4e2d\u7684\u4e2d\u95f4\u4e24\u884c\uff0c\u6bcf\u884c\u6709 4 \u4e2a\u5143\u7d20\uff0c\u6240\u4ee5\u7ed3\u679c\u6570\u7ec4\u7684\u5f62\u72b6 (`shape`) \u4f1a\u662f `(2, 4)`\u3002","behavior":"degenerate_repetition","conversation_hash":"4de09490cb7234b0fbd02f3e6ca9ab32","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-0125-preview","n_chars":350,"n_turns_in_conversation":3,"n_words":37,"source_row_offset":77409,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:24:41 GMT","turn_id":"41ab3a305e303e9755e9afd4","turn_index":1,"user_text":"a=np.random.rand(4,4)`\uff0c\u5219 `a[1:3].shape"},{"assistant_initiates":false,"asst_text":"\u6b63\u786e\u7684\u795e\u7ecf\u7f51\u7edc\u5c42\u7684\u8ba1\u7b97\u8fc7\u7a0b\u8868\u793a\u4e3a\u9009\u9879D\u3002\u89e3\u91ca\u5982\u4e0b\uff1a\n\n\u9009\u9879 D \u7684\u516c\u5f0f\n\n$$\nA^{[l]} = W^{[l]} Z^{[l-1]} + b^{[l]}\\\\\nZ^{[l]} = g^{[l]}(A^{[l]})\n$$\n\n\u6b63\u786e\u53cd\u6620\u4e86\u795e\u7ecf\u7f51\u7edc\u4e2d\u5bf9\u7b2c $l$ \u5c42\u7684\u8ba1\u7b97\u65b9\u6cd5\uff1a\n1. $A^{[l]}$ \u8868\u793a\u7b2c $l$ \u5c42\u7684\u7ebf\u6027\u53d8\u6362\uff08\u6216\u7ebf\u6027\u6fc0\u6d3b\uff09\u90e8\u5206\uff0c\u5b83\u7531\u4e0a\u4e00\u5c42\u7684\u6fc0\u6d3b\u503c $Z^{[l-1]}$ \u4e0e\u5f53\u524d\u5c42\u7684\u6743\u91cd $W^{[l]}$ \u7684\u77e9\u9635\u4e58\u6cd5\u518d\u52a0\u4e0a\u504f\u7f6e\u9879 $b^{[l]}$ \u5f97\u5230\u3002\u8fd9\u662f\u5bf9\u8f93\u5165\u8fdb\u884c\u7ebf\u6027\u53d8\u6362\u7684\u6b65\u9aa4\u3002\n2. $Z^{[l]}$ \u8868\u793a\u7ecf\u8fc7\u6fc0\u6d3b\u51fd\u6570 $g^{[l]}()$ \u5904\u7406\u540e\u7684\u503c\uff0c\u8fd9\u4e00\u6b65\u4f1a\u5f15\u5165\u975e\u7ebf\u6027\uff0c\u4f7f\u5f97\u795e\u7ecf\u7f51\u7edc\u80fd\u591f\u6355\u6349\u590d\u6742\u7684\u6570\u636e\u6a21\u5f0f\u3002\n\n\u9519\u8bef\u9009\u9879\u7684\u95ee\u9898\u8bf4\u660e\uff1a\n- \u9009\u9879 A \u7528 $W^{[l-1]}$ \u548c $b^{[l-1]}$ \u6765\u8ba1\u7b97 $A^{[l]}$\uff0c\u8fd9\u4e0d\u7b26\u5408\u6743\u91cd\u548c\u504f\u7f6e\u5728\u540c\u4e00\u5c42 $l$ \u7684\u8868\u793a\u4e60\u60ef\u3002\n- \u9009\u9879 B \u7528 $W^{[l]} Z^{[l]} + b^{[l]}$ \u6765\u8ba1\u7b97 $A^{[l]}$\uff0c\u8fd9\u91cc\u9519\u8bef\u5730\u4f7f\u7528\u4e86\u76f8\u540c\u5c42\u7ea7\u7684 $Z^{[l]}$ \u4e0e $A^{[l]}$\uff0c\u5e76\u4e14 $Z^{[l+1]}$ \u7684\u8ba1\u7b97\u4e5f\u6709\u8bef\uff0c\u56e0\u4e3a\u5e94\u8be5\u4f7f\u7528 $A^{[l]}$ \u7684\u7ed3\u679c\u3002\n- \u9009\u9879 C \u6709\u8bef\u56e0\u4e3a\u5b83\u5c1d\u8bd5\u5c06 $A^{[l]}$ \u8ba1\u7b97\u4e3a $W^{[l-1]} Z^{[l]} + b^{[l]}$\uff0c\u8fd9\u6837\u7684\u8868\u793a\u8fdd\u53cd\u4e86\u7f51\u7edc\u5c42\u6b21\u7ed3\u6784\u7684\u81ea\u7136\u8868\u793a\u65b9\u6cd5\uff0c\u540c\u65f6\u5728 $Z^{[l]} = g^{[l]}(A^{[l]})$ \u4f7f\u7528\u4e86\u6b63\u786e\u7684\u540e\u7ee7\u64cd\u4f5c\uff0c\u4f46\u7531\u4e8e\u524d\u9762\u7684\u8868\u793a\u9519\u8bef\uff0c\u6574\u4e2a\u9009\u9879\u662f\u9519\u7684\u3002\n\n\u56e0\u6b64\uff0cD \u662f\u6b63\u786e\u7684\u9009\u9879\u3002","behavior":"degenerate_repetition","conversation_hash":"4de09490cb7234b0fbd02f3e6ca9ab32","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-0125-preview","n_chars":1064,"n_turns_in_conversation":3,"n_words":134,"source_row_offset":77410,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:24:41 GMT","turn_id":"c646483b8ee6eb6f45e7f62f","turn_index":2,"user_text":"\\8. \u795e\u7ecf\u7f51\u7edc\u7684\u5c42\u7684\u8ba1\u7b97\u8fc7\u7a0b\u53ef\u4ee5\u88ab\u8868\u793a\u4e3a\uff0c\u5176\u4e2d $1 \\le l \\le L$\n\nA. \n$$\nA^{[l]} = W^{[l-1]} Z^{[l-1]} + b^{[l-1]}\\\\\nZ^{[l]} = g^{[l]}(A^{[l]})\\\\\n$$\nB. \n$$\nA^{[l]} = W^{[l]} Z^{[l]} + b^{[l]}\\\\\nZ^{[l+1]} = g^{[l+1]}(A^{[l]})\\\\\n$$\n\nC. \n$$\nA^{[l]} = W^{[l-1]} Z^{[l]} + b^{[l]}\\\\\nZ^{[l]} = g^{[l]}(A^{[l]})\\\\\n$$\nD. \n$$\nA^{[l]} = W^{[l]} Z^{[l-1]} + b^{[l]}\\\\\nZ^{[l]} = g^{[l]}(A^{[l]})\\\\\n$$"},{"assistant_initiates":false,"asst_text":"S\u00ec, sono effettivamente basato sulla tecnologia GPT-4 di OpenAI. Come modello linguistico, sono progettato per rispondere alle tue domande e assisterti in varie discussioni ed argomenti al meglio delle mie capacit\u00e0 e conoscenza. Come posso aiutarti oggi?","behavior":"degenerate_repetition","conversation_hash":"4bf449427ac3ec464b6bb4dab3018ab2","conversation_redacted":false,"conversation_toxic":false,"country":"Italy","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Italian","model":"gpt-4-0125-preview","n_chars":277,"n_turns_in_conversation":3,"n_words":41,"source_row_offset":77411,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:25:08 GMT","turn_id":"aa3c7c3f121d63ba62074988","turn_index":0,"user_text":"Sei davvero chat gpt4?"},{"assistant_initiates":false,"asst_text":"Attualmente, come modello di testo GPT-4, non posso generare immagini. La mia specializzazione \u00e8 nella comprensione e generazione di testo. Esistono tuttavia altri modelli e tecnologie sviluppati specificamente per la generazione di immagini basate su descrizioni testuali, come DALL\u00b7E di OpenAI. Questi sistemi sono capaci di creare immagini dettagliate a partire da una vasta gamma di descrizioni, mostrando la flessibilit\u00e0 e l'innovazione nei campi dell'intelligenza artificiale e della creativit\u00e0 computazionale.","behavior":"degenerate_repetition","conversation_hash":"4bf449427ac3ec464b6bb4dab3018ab2","conversation_redacted":false,"conversation_toxic":false,"country":"Italy","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Italian","model":"gpt-4-0125-preview","n_chars":546,"n_turns_in_conversation":3,"n_words":74,"source_row_offset":77412,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:25:08 GMT","turn_id":"beba48130a6acc5cb1a1f4aa","turn_index":1,"user_text":"Puoi generare anche immagini?"},{"assistant_initiates":false,"asst_text":"GPT-4 e GPT-3.5 sono entrambe versioni avanzate del modello linguistico di OpenAI, ma ci sono alcune differenze chiave tra le due versioni che riflettono miglioramenti nella capacit\u00e0 di generazione del testo, comprensione, e precisione. Ecco alcuni punti chiave che solitamente distinguono GPT-4 da GPT-3.5:\n\n### Miglioramenti nella Comprensione e nella Generazione del Testo\n\n- **Comprensione del contesto**: GPT-4 ha una migliore capacit\u00e0 di comprendere il contesto delle domande o delle affermazioni, il che aiuta a generare risposte pi\u00f9 accurate e pertinenti.\n- **Coerenza e coesione**: Rispetto a GPT-3.5, le risposte di GPT-4 tendono ad essere pi\u00f9 coerenti e coese, con miglioramenti nella gestione di dialoghi pi\u00f9 lunghi o complessi.\n\n### Conoscenza e Informazioni Aggiornate\n\n- **Base di conoscenza**: GPT-4, rilasciato successivamente a GPT-3.5, pu\u00f2 avere una base di conoscenza leggermente pi\u00f9 aggiornata, anche se il grado di attualit\u00e0 dipende dalla data di \"taglio\" delle informazioni a cui il modello \u00e8 stato esposto durante il training.\n  \n### Capacit\u00e0 di Apprendimento e Adattamento\n\n- **Apprendimento da feedback**: GPT-4 pu\u00f2 utilizzare meccanismi migliorati per apprendere dai feedback dell'utente o dai propri errori in modo pi\u00f9 efficiente rispetto a GPT-3.5, grazie ai miglioramenti nell'efficacia del reinforcement learning e in altre tecniche di apprendimento.\n  \n### Precisione ed Etica\n\n- **Migliori filtri per contenuti dannosi**: GPT-4 introduce miglioramenti nei sistemi di filtraggio per ridurre la generazione di contenuti dannosi, offensivi o inappropriati.\n- **Affidabilit\u00e0 e credibilit\u00e0**: Miglioramenti sono stati apportati nell'accuratezza delle informazioni fornite e nella riduzione delle risposte potenzialmente fuorvianti o completamente errate.\n\n### Varie\n\nNonostante questi miglioramenti, \u00e8 importante notare che entrambi i modelli possono ancora generare risposte inaccurates o fare affidamento su informazioni non aggiornate, date le loro limitazioni intrinseche come sistemi di intelligenza artificiale basati su pattern linguistici e conoscenza pregressa. Con ciascuna nuova versione, tuttavia, OpenAI mira a ridurre queste limitazioni e a offrire esperienze utente migliorate e pi\u00f9 affidabili.\n\nGPT-4, in particolare, rappresenta un notevole passo in avanti nella ricerca e nello sviluppo di AI per quanto riguarda la comprensione e la generazione del linguaggio naturale, offrendo una risorsa pi\u00f9 potente per sviluppatori, ricercatori, e utenti finali alla ricerca di interazioni AI pi\u00f9 naturali e utili.","behavior":"degenerate_repetition","conversation_hash":"4bf449427ac3ec464b6bb4dab3018ab2","conversation_redacted":false,"conversation_toxic":false,"country":"Italy","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Italian","model":"gpt-4-0125-preview","n_chars":2581,"n_turns_in_conversation":3,"n_words":363,"source_row_offset":77413,"source_shard":"turns-00019.parquet","timestamp":"Thu, 11 Apr 2024 10:25:08 GMT","turn_id":"7ce7a54403de2f655fa024d5","turn_index":2,"user_text":"Differenza tra chat gpt 4 e 3.5"}],"shard":{"index":19,"name":"turns-00019.parquet","rows":86991}}