{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":5214,"offset":5204,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"True","behavior":"degenerate_repetition","conversation_hash":"3891e2980ccb9ee0655cacf7e3334b6b","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-4o-2024-08-06","n_chars":1801,"n_turns_in_conversation":1,"n_words":291,"source_row_offset":5204,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:27 GMT","turn_id":"98ecfa04c4fceafcb282f0dc","turn_index":0,"user_text":"System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGIANL text. Respond strictly with either 'True' or 'False'.\n==TEXT==\n\nUser: \n  ORIGINAL: \ud83d\udfea Product Designer | 6 year(s) | Senior\n\u25ab\ufe0f Plaid | Financial Technology\n\u25ab\ufe0f 144.7k - 216k USD\n\n\u0423\u0434\u0430\u043b\u0435\u043d\u043a\u0430 \ud83d\udce1\n\u0422\u0440\u0435\u0431\u0443\u0435\u043c\u044b\u0435 \u044f\u0437\u044b\u043a\u0438: \ud83c\uddec\ud83c\udde7\n\nSkills: visual_design\nTags: #purple #it #remote #ux_ui_design\n\n\ud83d\udc49 \u041a\u043e\u043d\u0442\u0430\u043a\u0442\u044b \u0438 \u043f\u043e\u043b\u043d\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435\n  PROMPT: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.\n\nIgnore text's markdowm markup, use these emojis to highlight each section: \ud83c\udfe2 for employer, \ud83d\udcbc for title, \ud83d\udccd for location, \ud83d\udd39 for tasks, \ud83d\udcb0 for salary, and \ud83d\udcde for contact.\n\nEach block should be on a new line, in the following format (keep emoji, replace text labels):\n\ud83d\udcbc title  \n\ud83c\udfe2 employer  \n\ud83d\udccd location  \n\ud83d\udd39 tasks  \n\ud83d\udcb0 salary  \n\ud83d\udcde contact details\n\nMake sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.\n\n==TEXT==\n\n  SUMMARY: \ud83d\udcbc Product Designer\n\ud83c\udfe2 Plaid\n\ud83d\udccd Financial Technology\n\ud83d\udd39 \u0423\u0434\u0430\u043b\u0435\u043d\u043a\u0430\n\ud83c\udf10 \u0422\u0440\u0435\u0431\u0443\u0435\u043c\u044b\u0435 \u044f\u0437\u044b\u043a\u0438: \ud83c\uddec\ud83c\udde7\n\ud83d\udcb0 Salary: 144.7k - 216k USD\n\ud83d\udcde Contact and full description\n\nThis position offers a Senior Product Designer role with the employer Plaid, located in the field of Financial Technology, providing a remote work environment. The primary language required is English (\ud83c\uddec\ud83c\udde7). The salary ranges from 144.7k to 216k USD with access to the full job description and contact information.\n  \nAssistant:"},{"assistant_initiates":false,"asst_text":"False","behavior":"degenerate_repetition","conversation_hash":"02f576aa9445a06a5129f8cb13434ab2","conversation_redacted":false,"conversation_toxic":false,"country":"Germany","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":1601,"n_turns_in_conversation":1,"n_words":261,"source_row_offset":5205,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:27 GMT","turn_id":"a55713a0c3cf0cf44940e7b1","turn_index":0,"user_text":"System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGIANL text. Respond strictly with either 'True' or 'False'.\n==TEXT==\n\nUser: \n  ORIGINAL: \ud83d\udfea Data Engineer | 5 year(s) | Senior\n\u25ab\ufe0f 100 - 100 USD\n\u25ab\ufe0f USA | Charlotte\n\nOffice \ud83c\udfe2\nRequired languages: \ud83c\uddec\ud83c\udde7\n\nSkills: aws, git, java, postgresql, python, s3, sql, terraform, eks, lambda, rds, iam\nTags: #purple #it #data_engineering\n\n\ud83d\udc49 Contacts and Full Description\n  PROMPT: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.\n\nIgnore text's markdowm markup, use these emojis to highlight each section: \ud83c\udfe2 for employer, \ud83d\udcbc for title, \ud83d\udccd for location, \ud83d\udd39 for tasks, \ud83d\udcb0 for salary, and \ud83d\udcde for contact.\n\nEach block should be on a new line, in the following format (keep emoji, replace text labels):\n\ud83d\udcbc title  \n\ud83c\udfe2 employer  \n\ud83d\udccd location  \n\ud83d\udd39 tasks  \n\ud83d\udcb0 salary  \n\ud83d\udcde contact details\n\nMake sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.\n\n==TEXT==\n\n  SUMMARY: \ud83d\udfea Data Engineer  \n\ud83c\udfe2 Charlotte Office  \n\ud83d\udccd USA  \n\ud83d\udd27 Required languages: \ud83c\uddec\ud83c\udde7 Skills: aws, git, java, postgresql, python, s3, sql, terraform, eks, lambda, rds, iam  \n\ud83d\udcb0 100 - 100 USD  \n\ud83d\udcde Contacts and Full Description  \n  \nAssistant:"},{"assistant_initiates":false,"asst_text":"False","behavior":"degenerate_repetition","conversation_hash":"814e5a8ab8210da42591c12ae9a2596d","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-4o-2024-08-06","n_chars":1578,"n_turns_in_conversation":1,"n_words":259,"source_row_offset":5206,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:27 GMT","turn_id":"338d37c5b25072cbc776a6d4","turn_index":0,"user_text":"System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGIANL text. Respond strictly with either 'True' or 'False'.\n==TEXT==\n\nUser: \n  ORIGINAL: \ud83d\udfea Data Engineer | 5 year(s) | Senior\n\u25ab\ufe0f 100 - 100 USD\n\u25ab\ufe0f USA | Charlotte\n\nOffice \ud83c\udfe2\nRequired languages: \ud83c\uddec\ud83c\udde7\n\nSkills: aws, git, java, postgresql, python, s3, sql, terraform, eks, lambda, rds, iam\nTags: #purple #it #data_engineering\n\n\ud83d\udc49 Contacts and Full Description\n  PROMPT: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.\n\nIgnore text's markdowm markup, use these emojis to highlight each section: \ud83c\udfe2 for employer, \ud83d\udcbc for title, \ud83d\udccd for location, \ud83d\udd39 for tasks, \ud83d\udcb0 for salary, and \ud83d\udcde for contact.\n\nEach block should be on a new line, in the following format (keep emoji, replace text labels):\n\ud83d\udcbc title  \n\ud83c\udfe2 employer  \n\ud83d\udccd location  \n\ud83d\udd39 tasks  \n\ud83d\udcb0 salary  \n\ud83d\udcde contact details\n\nMake sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.\n\n==TEXT==\n\n  SUMMARY: \ud83d\udfea Data Engineer  \n\ud83c\udfe2 5 year(s)  \n\ud83d\udccd USA | Charlotte Office  \n\ud83d\udd39 Main tasks include working with AWS services and managing data pipelines.  \n\ud83d\udcb0 100 - 100 USD  \n\ud83d\udcde Contacts and Full Description  \n  \nAssistant:"},{"assistant_initiates":false,"asst_text":"```json\n{\n    \"QA_pairs\": [\n        {\n            \"###C\u00e2u h\u1ecfi\": \"T\u1ea1i sao b\u1ec7nh tho\u00e1i h\u00f3a \u0111i\u1ec3m v\u00e0ng li\u00ean quan \u0111\u1ebfn tu\u1ed5i t\u00e1c v\u1eabn g\u00e2y m\u1ea5t th\u1ecb l\u1ef1c ngay c\u1ea3 khi c\u00f3 ti\u1ebfn b\u1ed9 v\u1ec1 thu\u1ed1c?\",\n            \"###Suy lu\u1eadn\": \"M\u1eb7c d\u00f9 c\u00f3 c\u00e1c thu\u1ed1c kh\u00e1ng VEGF trong d\u1ecbch k\u00ednh \u0111\u1ec3 \u0111i\u1ec1u tr\u1ecb tho\u00e1i h\u00f3a \u0111i\u1ec3m v\u00e0ng, nh\u01b0ng k\u1ebft qu\u1ea3 th\u1ecb gi\u00e1c v\u1eabn b\u1ecb h\u1ea1n ch\u1ebf do tho\u00e1i h\u00f3a ti\u1ebfp t\u1ee5c c\u1ee7a l\u1edbp bi\u1ec3u m\u00f4 s\u1eafc t\u1ed1 v\u00f5ng m\u1ea1c (RPE) v\u00e0 s\u1ef1 teo v\u00f5ng m\u1ea1c th\u1ea7n kinh c\u1ea3m gi\u00e1c. \u0110i\u1ec1u n\u00e0y c\u00f3 ngh\u0129a l\u00e0 m\u1eb7c d\u00f9 c\u00f3 th\u1ec3 \u0111i\u1ec1u tr\u1ecb m\u1ed9t ph\u1ea7n tri\u1ec7u ch\u1ee9ng, c\u0103n b\u1ec7nh v\u1eabn ti\u1ebfn tri\u1ec3n g\u00e2y gi\u1ea3m th\u1ecb l\u1ef1c.\",\n            \"###C\u00e2u tr\u1ea3 l\u1eddi\": \"B\u1ec7nh tho\u00e1i h\u00f3a \u0111i\u1ec3m v\u00e0ng v\u1eabn g\u00e2y m\u1ea5t th\u1ecb l\u1ef1c do tho\u00e1i h\u00f3a RPE v\u00e0 teo v\u00f5ng m\u1ea1c th\u1ea7n kinh kh\u00f4ng \u0111\u01b0\u1ee3c ng\u0103n ch\u1eb7n ho\u00e0n to\u00e0n.\"\n        },\n        {\n            \"###C\u00e2u h\u1ecfi\": \"C\u00f4ng ngh\u1ec7 n\u00e0o \u0111ang \u0111\u01b0\u1ee3c kh\u00e1m ph\u00e1 \u0111\u1ec3 ng\u0103n ch\u1eb7n tho\u00e1i h\u00f3a \u0111i\u1ec3m v\u00e0ng v\u00e0 t\u1ea1i sao?\",\n            \"###Suy lu\u1eadn\": \"C\u00e1c ph\u01b0\u01a1ng ph\u00e1p x\u1ea1 tr\u1ecb \u0111ang \u0111\u01b0\u1ee3c xem x\u00e9t nh\u1eb1m gi\u1ea3i quy\u1ebft nguy\u00ean nh\u00e2n \u0111a y\u1ebfu t\u1ed1 c\u1ee7a tho\u00e1i h\u00f3a \u0111i\u1ec3m v\u00e0ng. C\u00f4ng ngh\u1ec7 n\u00e0y \u0111\u01b0\u1ee3c k\u1ef3 v\u1ecdng c\u00f3 th\u1ec3 b\u1ed5 sung cho c\u00e1c thu\u1ed1c hi\u1ec7n c\u00f3 b\u1eb1ng c\u00e1ch t\u00e1c \u0111\u1ed9ng v\u00e0o c\u00e1c nguy\u00ean nh\u00e2n kh\u00e1c nhau c\u1ee7a b\u1ec7nh, kh\u00f4ng ch\u1ec9 d\u1eebng l\u1ea1i \u1edf vi\u1ec7c l\u00e0m gi\u1ea3m tri\u1ec7u ch\u1ee9ng.\",\n            \"###C\u00e2u tr\u1ea3 l\u1eddi\": \"X\u1ea1 tr\u1ecb \u0111ang \u0111\u01b0\u1ee3c kh\u00e1m ph\u00e1 \u0111\u1ec3 ng\u0103n ch\u1eb7n tho\u00e1i h\u00f3a \u0111i\u1ec3m v\u00e0ng v\u00ec n\u00f3 c\u00f3 th\u1ec3 gi\u1ea3i quy\u1ebft nguy\u00ean nh\u00e2n \u0111a y\u1ebfu t\u1ed1 c\u1ee7a b\u1ec7nh.\"\n        },\n        {\n            \"###C\u00e2u h\u1ecfi\": \"C\u00e1c nghi\u00ean c\u1ee9u hi\u1ec7n t\u1ea1i \u0111ang nh\u1eafm \u0111\u1ebfn \u0111i\u1ec1u g\u00ec trong \u0111i\u1ec1u tr\u1ecb tho\u00e1i h\u00f3a \u0111i\u1ec3m v\u00e0ng?\",\n            \"###Suy lu\u1eadn\": \"C\u00e1c nghi\u00ean c\u1ee9u \u0111i\u1ec1u tra hi\u1ec7n t\u1ea1i \u0111ang hy v\u1ecdng mang l\u1ea1i k\u1ebft qu\u1ea3 l\u00e2m s\u00e0ng ph\u00f9 h\u1ee3p v\u1edbi l\u00fd thuy\u1ebft, ngh\u0129a l\u00e0 t\u00ecm ki\u1ebfm nh\u1eefng ph\u01b0\u01a1ng ph\u00e1p \u0111i\u1ec1u tr\u1ecb m\u1edbi c\u00f3 th\u1ec3 b\u1ed5 sung cho hi\u1ec7u qu\u1ea3 c\u1ee7a c\u00e1c thu\u1ed1c hi\u1ec7n c\u00f3, v\u1edbi m\u1ee5c ti\u00eau ng\u0103n ch\u1eb7n t\u00ecnh tr\u1ea1ng m\u1ea5t th\u1ecb gi\u00e1c k\u00e9o d\u00e0i.\",\n            \"###C\u00e2u tr\u1ea3 l\u1eddi\": \"C\u00e1c nghi\u00ean c\u1ee9u \u0111ang nh\u1eafm \u0111\u1ebfn vi\u1ec7c t\u00ecm ki\u1ebfm c\u00e1c ph\u01b0\u01a1ng ph\u00e1p \u0111i\u1ec1u tr\u1ecb m\u1edbi \u0111\u1ec3 ng\u0103n ch\u1eb7n m\u1ea5t th\u1ecb gi\u00e1c hi\u1ec7u qu\u1ea3 h\u01a1n.\"\n        }\n    ]\n}\n```","behavior":"degenerate_repetition","conversation_hash":"ee6a3c089a60b5f41145517de1cccf46","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":"Vietnamese","model":"gpt-4o-2024-08-06","n_chars":7453,"n_turns_in_conversation":1,"n_words":1455,"source_row_offset":5207,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:27 GMT","turn_id":"792f639f4e1d0894be57f13c","turn_index":0,"user_text":"B\u1ea1n s\u1ebd nh\u1eadn \u0111\u01b0\u1ee3c m\u1ed9t \u0111o\u1ea1n ng\u1eef c\u1ea3nh m\u00f4 t\u1ea3 m\u1ed9t s\u1ef1 ki\u1ec7n ho\u1eb7c kh\u00e1i ni\u1ec7m c\u1ee5 th\u1ec3. D\u1ef1a tr\u00ean ng\u1eef c\u1ea3nh, h\u00e3y t\u1ea1o ra 3 c\u1eb7p c\u00e2u h\u1ecfi v\u00e0 c\u00e2u tr\u1ea3 l\u1eddi \u0111i k\u00e8m. H\u00e3y \u0111\u1ea3m b\u1ea3o r\u1eb1ng c\u00e2u tr\u1ea3 l\u1eddi c\u00f3 s\u1ef1 gi\u1ea3i th\u00edch t\u1eebng b\u01b0\u1edbc ho\u1eb7c m\u00f4 t\u1ea3 chi ti\u1ebft (Chain of Thought) \u0111\u1ec3 ng\u01b0\u1eddi \u0111\u1ecdc hi\u1ec3u r\u00f5 h\u01a1n v\u1ec1 n\u1ed9i dung c\u00e2u tr\u1ea3 l\u1eddi.\n    Y\u00eau c\u1ea7u: T\u1ea1o 3 c\u1eb7p c\u00e2u h\u1ecfi v\u00e0 c\u00e2u tr\u1ea3 l\u1eddi. H\u00e3y l\u01b0u \u00fd cung c\u1ea5p c\u00e2u tr\u1ea3 l\u1eddi theo t\u1eebng b\u01b0\u1edbc suy lu\u1eadn, ho\u1eb7c \u0111\u01b0a ra c\u00e1c y\u1ebfu t\u1ed1 gi\u1ea3i th\u00edch r\u00f5 r\u00e0ng li\u00ean quan \u0111\u1ebfn c\u00e2u tr\u1ea3 l\u1eddi (Chain of Thought). B\u01b0\u1edbc suy lu\u1eadn s\u1ebd l\u1ea5y th\u00f4ng tin t\u1eeb ng\u1eef c\u1ea3nh v\u00e0 c\u00e2u tr\u1ea3 l\u1eddi s\u1ebd \u1edf d\u1ea1ng ng\u1eafn g\u1ecdn, s\u00fac t\u00edch.\n\n    C\u00e2u h\u1ecfi \u0111\u01b0\u1ee3c b\u1ecf v\u00e0o tag ###C\u00e2u h\u1ecfi:\n    Suy lu\u1eadn \u0111\u01b0\u1ee3c b\u1ecf v\u00e0o tag ###Suy lu\u1eadn:\n    C\u00e2u tr\u1ea3 l\u1eddi \u0111\u01b0\u1ee3c b\u1ecf v\u00e0o tag \u0111\u1eb7c bi\u1ec7t ###C\u00e2u tr\u1ea3 l\u1eddi:\n\n    N\u1ebfu ng\u1eef c\u1ea3nh kh\u00f4ng c\u00f3 \u00fd ngh\u0129a, b\u1ea1n h\u00e3y output \"Ng\u1eef c\u1ea3nh kh\u00f4ng gi\u00e1 tr\u1ecb\"\n    Tr\u1ea3 l\u1eddi b\u1eb1ng ti\u1ebfng Vi\u1ec7t\n    Tr\u1ea3 cho t\u00f4i output d\u01b0\u1edbi d\u1ea1ng json \u0111\u1ec3 c\u00f3 th\u1ec3 tr\u00edch xu\u1ea5t m\u1ed9t c\u00e1ch d\u1ec5 d\u00e0ng\n\n    V\u00ed d\u1ee5:\n    ### Ng\u1eef c\u1ea3nh: \u1ea2nh h\u01b0\u1edfng c\u1ee7a d\u1ea7u h\u1ea1t c\u1ea3i trong ch\u1ebf \u0111\u1ed9 \u0103n \u0111\u1ed1i v\u1edbi c\u00e1c nh\u00f3m lipid v\u00e0 m\u00f4 h\u00ecnh HFA c\u1ee7a tim chu\u1ed9t TH v\u00e0 ty th\u1ec3 \u0111\u00e3 \u0111\u01b0\u1ee3c nghi\u00ean c\u1ee9u. T3 cho \u0103n ch\u1ebf \u0111\u1ed9 \u0103n c\u00f3 axit erucic tr\u1ecdng l\u01b0\u1ee3ng trong nhi\u1ec1u ng\u00e0y v\u00e0 axit erucic trong nhi\u1ec1u ng\u00e0y, chu\u1ed9t \u0111\u01b0\u1ee3c \u0111i\u1ec1u tr\u1ecb b\u1eb1ng axit erucic cho th\u1ea5y s\u1ef1 gia t\u0103ng \u0111\u00e1ng k\u1ec3 v\u1ec1 t\u1ef7 l\u1ec7 m\u1eafc b\u1ec7nh. triglycerid c\u1ee7a ty th\u1ec3 c\u1ee7a tim xu h\u01b0\u1edbng n\u00e0y \u00edt r\u00f5 r\u1ec7t h\u01a1n \u1edf nh\u1eefng con chu\u1ed9t \u0111\u01b0\u1ee3c \u0111i\u1ec1u tr\u1ecb b\u1eb1ng axit resp erucic. Nh\u1eefng k\u1ebft qu\u1ea3 n\u00e0y x\u00e1c nh\u1eadn k\u1ebft qu\u1ea3 c\u1ee7a nh\u1eefng nh\u00e0 nghi\u00ean c\u1ee9u kh\u00e1c. C\u00f3 th\u1ec3 th\u1ea5y s\u1ef1 gia t\u0103ng nh\u1eb9 trong cholesterol ester c\u1ee7a ty th\u1ec3 \u1edf t\u1ea5t c\u1ea3 nh\u1eefng con chu\u1ed9t \u0111\u01b0\u1ee3c \u0111i\u1ec1u tr\u1ecb, t\u1ed5ng l\u01b0\u1ee3ng phospholipid \u0111\u00e3 gi\u1ea3m trong th\u00ed nghi\u1ec7m v\u1edbi axit erucic v\u00e0 t\u0103ng nh\u1eb9 trong th\u00ed nghi\u1ec7m v\u1edbi axit erucic n\u1ed3ng \u0111\u1ed9 phosphatidylcholine c\u00f3 xu h\u01b0\u1edbng t\u0103ng v\u00e0 n\u1ed3ng \u0111\u1ed9 phosphatidyletanolamine gi\u1ea3m trong th\u00ed nghi\u1ec7m v\u1edbi axit erucic trong kh\u1ea9u ph\u1ea7n n\u1ed3ng \u0111\u1ed9 CL h\u1ea7u nh\u01b0 kh\u00f4ng thay \u0111\u1ed5i trong t\u1ea5t c\u1ea3 c\u00e1c th\u00ed nghi\u1ec7m triglycerid c\u1ee7a ty th\u1ec3 c\u1ee7a tim cho th\u1ea5y h\u00e0m l\u01b0\u1ee3ng axit erucic cao c\u00e1c axit b\u00e9o c\u1ee7a CE c\u1ee7a ty th\u1ec3 c\u1ee7a tim c\u0169ng b\u1ecb \u1ea3nh h\u01b0\u1edfng b\u1edfi d\u1ea7u h\u1ea1t c\u1ea3i trong ch\u1ebf \u0111\u1ed9 \u0103n u\u1ed1ng nh\u01b0ng \u1edf m\u1ee9c \u0111\u1ed9 th\u1ea5p h\u01a1n so v\u1edbi ch\u1ea5t b\u00e9o trung t\u00ednh, c\u00e1c axit b\u00e9o c\u1ee7a phosphatidylcholine phosphatidyletanolamine v\u00e0 cardiolipin \u0111\u1ec1u b\u1ecb \u1ea3nh h\u01b0\u1edfng b\u1edfi ch\u1ebf \u0111\u1ed9 \u0103n u\u1ed1ng d\u1ea7u h\u1ea1t c\u1ea3i nh\u01b0ng axit erucic d\u01b0\u1eddng nh\u01b0 c\u00f3 \u00e1i l\u1ef1c TPS v\u1edbi CL cardiolipin c\u1ee7a ty th\u1ec3 HR c\u1ee7a chu\u1ed9t \u0111\u00e3 \u0111\u01b0\u1ee3c ph\u00e2n l\u1eadp v\u00e0 x\u00e1c \u0111\u1ecbnh b\u1eb1ng s\u1eafc k\u00fd kh\u00ed v\u00e0 ph\u00e9p \u0111o ph\u1ed5 kh\u1ed1i, CL c\u00f4 l\u1eadp \u0111\u01b0\u1ee3c ph\u00e1t hi\u1ec7n c\u00f3 ch\u1ee9a ph\u1ea7n tr\u0103m axit erucic T3 cho \u0103n axit erucic nh\u01b0 d\u1ea7u h\u1ea1t c\u1ea3i trong nhi\u1ec1u ng\u00e0y t\u01b0\u01a1ng t\u1ef1 K\u1ebft qu\u1ea3 thu \u0111\u01b0\u1ee3c l\u00e0 T3 FF glyceryl trierucate trong nhi\u1ec1u ng\u00e0y \u0111\u1ed1i v\u1edbi chu\u1ed9t. S\u1ef1 k\u1ebft h\u1ee3p c\u1ee7a axit erucic v\u00e0o CL, sau \u0111\u00f3 l\u00e0 s\u1ef1 gi\u1ea3m t\u01b0\u01a1ng \u1ee9ng c\u1ee7a axit linoleic. Quan s\u00e1t n\u00e0y r\u1ea5t \u0111\u00e1ng quan t\u00e2m v\u00ec c\u1ea5u tr\u00fac ph\u00e2n t\u1eed c\u1ee7a axit b\u00e9o trong ph\u00e2n t\u1eed lipid c\u00f3 \u1ea3nh h\u01b0\u1edfng s\u00e2u s\u1eafc \u0111\u1ebfn vi\u1ec7c \u0111\u00f3ng g\u00f3i c\u1ee7a c\u00e1c ph\u00e2n t\u1eed n\u00e0y trong m\u1ed9t l\u1edbp k\u00e9p v\u00ec cardiolipin l\u00e0 m\u1ed9t th\u00e0nh ph\u1ea7n c\u1ee7a IM c\u1ee7a ty th\u1ec3, \u00e1i l\u1ef1c cao c\u1ee7a n\u00f3 v\u1edbi axit erucic c\u00f3 th\u1ec3 \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn CF b\u00ecnh th\u01b0\u1eddng c\u1ee7a m\u00e0ng trong c\u1ee7a ty th\u1ec3 tim\n    ### \u0110\u1ea7u ra: \n    \n    json```{\n        \"QA_pairs\": [\n            {\n                \"###C\u00e2u h\u1ecfi\": \"Axit erucic \u1ea3nh h\u01b0\u1edfng nh\u01b0 th\u1ebf n\u00e0o \u0111\u1ebfn h\u00e0m l\u01b0\u1ee3ng triglycerid trong ty th\u1ec3 c\u1ee7a tim chu\u1ed9t?\",\n                \"###Suy lu\u1eadn\": \"Khi chu\u1ed9t \u0111\u01b0\u1ee3c cho \u0103n ch\u1ebf \u0111\u1ed9 c\u00f3 axit erucic, t\u1ef7 l\u1ec7 m\u1eafc b\u1ec7nh li\u00ean quan \u0111\u1ebfn h\u00e0m l\u01b0\u1ee3ng triglycerid trong ty th\u1ec3 tim t\u0103ng l\u00ean \u0111\u00e1ng k\u1ec3. Tuy nhi\u00ean, s\u1ef1 gia t\u0103ng n\u00e0y kh\u00f4ng r\u00f5 r\u1ec7t \u1edf chu\u1ed9t \u0111\u01b0\u1ee3c \u0111i\u1ec1u tr\u1ecb b\u1eb1ng lo\u1ea1i axit erucic kh\u00e1c, cho th\u1ea5y \u1ea3nh h\u01b0\u1edfng ph\u1ee5 thu\u1ed9c v\u00e0o lo\u1ea1i axit erucic s\u1eed d\u1ee5ng.\",\n                \"###C\u00e2u tr\u1ea3 l\u1eddi\": \"Axit erucic l\u00e0m t\u0103ng h\u00e0m l\u01b0\u1ee3ng triglycerid trong ty th\u1ec3 tim chu\u1ed9t, nh\u01b0ng \u1ea3nh h\u01b0\u1edfng ph\u1ee5 thu\u1ed9c v\u00e0o lo\u1ea1i axit erucic c\u1ee5 th\u1ec3.\"\n            },\n            {\n                \"###C\u00e2u h\u1ecfi\": \"D\u1ea7u h\u1ea1t c\u1ea3i trong ch\u1ebf \u0111\u1ed9 \u0103n c\u00f3 t\u00e1c \u0111\u1ed9ng nh\u01b0 th\u1ebf n\u00e0o \u0111\u1ebfn c\u00e1c lo\u1ea1i phospholipid trong ty th\u1ec3 c\u1ee7a tim?\",\n                \"###Suy lu\u1eadn\": \"C\u00e1c nghi\u00ean c\u1ee9u cho th\u1ea5y t\u1ed5ng l\u01b0\u1ee3ng phospholipid gi\u1ea3m khi chu\u1ed9t \u0111\u01b0\u1ee3c cho \u0103n axit erucic t\u1eeb d\u1ea7u h\u1ea1t c\u1ea3i. Ngo\u00e0i ra, n\u1ed3ng \u0111\u1ed9 phosphatidylcholine t\u0103ng trong khi n\u1ed3ng \u0111\u1ed9 phosphatidyletanolamine gi\u1ea3m. C\u00e1c axit b\u00e9o c\u1ee7a phosphatidylcholine, phosphatidyletanolamine v\u00e0 cardiolipin \u0111\u1ec1u b\u1ecb \u1ea3nh h\u01b0\u1edfng.\",\n                \"###C\u00e2u tr\u1ea3 l\u1eddi\": \"D\u1ea7u h\u1ea1t c\u1ea3i l\u00e0m gi\u1ea3m t\u1ed5ng l\u01b0\u1ee3ng phospholipid, t\u0103ng phosphatidylcholine v\u00e0 gi\u1ea3m phosphatidyletanolamine trong ty th\u1ec3 tim.\"\n            },\n            {\n                \"###C\u00e2u h\u1ecfi\": \"S\u1ef1 k\u1ebft h\u1ee3p c\u1ee7a axit erucic v\u00e0o cardiolipin \u1ea3nh h\u01b0\u1edfng g\u00ec \u0111\u1ebfn m\u00e0ng trong c\u1ee7a ty th\u1ec3 tim?\",\n                \"###Suy lu\u1eadn\": \"Cardiolipin l\u00e0 th\u00e0nh ph\u1ea7n quan tr\u1ecdng c\u1ee7a m\u00e0ng trong ty th\u1ec3, v\u00e0 s\u1ef1 k\u1ebft h\u1ee3p c\u1ee7a axit erucic v\u00e0o cardiolipin l\u00e0m gi\u1ea3m axit linoleic, c\u00f3 th\u1ec3 \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn vi\u1ec7c \u0111\u00f3ng g\u00f3i lipid trong l\u1edbp k\u00e9p. V\u00ec axit erucic c\u00f3 \u00e1i l\u1ef1c cao v\u1edbi cardiolipin, \u0111i\u1ec1u n\u00e0y c\u00f3 th\u1ec3 t\u00e1c \u0111\u1ed9ng \u0111\u1ebfn ch\u1ee9c n\u0103ng c\u1ee7a m\u00e0ng trong ty th\u1ec3 tim.\",\n                \"###C\u00e2u tr\u1ea3 l\u1eddi\": \"Axit erucic trong cardiolipin c\u00f3 th\u1ec3 l\u00e0m thay \u0111\u1ed5i ch\u1ee9c n\u0103ng c\u1ee7a m\u00e0ng trong ty th\u1ec3 tim do \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn c\u1ea5u tr\u00fac l\u1edbp lipid k\u00e9p.\"\n            }\n        ]\n    }```\n\n    \n\n    ### Ng\u1eef c\u1ea3nh: Tho\u00e1i h\u00f3a \u0111i\u1ec3m v\u00e0ng li\u00ean quan \u0111\u1ebfn tu\u1ed5i t\u00e1c v\u1eabn l\u00e0 m\u1ed9t nguy\u00ean nh\u00e2n t\u00e0n kh\u1ed1c g\u00e2y m\u1ea5t th\u1ecb l\u1ef1c \u1edf ng\u01b0\u1eddi cao tu\u1ed5i trong khi nh\u1eefng ti\u1ebfn b\u1ed9 \u0111\u00e1ng k\u1ec3 \u0111\u00e3 \u0111\u1ea1t \u0111\u01b0\u1ee3c trong vi\u1ec7c ch\u1ed1ng l\u1ea1i c\u0103n b\u1ec7nh n\u00e0y g\u1ea7n \u0111\u00e2y nh\u1ea5t v\u1edbi c\u00e1c thu\u1ed1c kh\u00e1ng vegf trong d\u1ecbch k\u00ednh, k\u1ebft qu\u1ea3 th\u1ecb gi\u00e1c v\u1eabn c\u00f2n b\u1ecb h\u1ea1n ch\u1ebf do tho\u00e1i h\u00f3a RPE rpe ti\u1ebfp t\u1ee5c v\u00e0 teo v\u00f5ng m\u1ea1c th\u1ea7n kinh c\u1ea3m gi\u00e1c sau \u0111\u00f3 trong s\u1ed1 c\u00e1c ph\u01b0\u01a1ng ph\u00e1p \u0111i\u1ec1u tr\u1ecb m\u1edbi \u0111\u1ea7y h\u1ee9a h\u1eb9n C\u00e1c l\u1ef1a ch\u1ecdn \u0111ang \u0111\u01b0\u1ee3c kh\u00e1m ph\u00e1 b\u1eb1ng ph\u01b0\u01a1ng ph\u00e1p x\u1ea1 tr\u1ecb c\u00f3 v\u1ebb ph\u00f9 h\u1ee3p \u0111\u1ec3 gi\u1ea3i quy\u1ebft nguy\u00ean nh\u00e2n \u0111a y\u1ebfu t\u1ed1 c\u1ee7a c\u00e1c nghi\u00ean c\u1ee9u \u0111i\u1ec1u tra hi\u1ec7n t\u1ea1i \u0111ang \u0111\u01b0\u1ee3c ti\u1ebfn h\u00e0nh hy v\u1ecdng s\u1ebd mang l\u1ea1i k\u1ebft qu\u1ea3 l\u00e2m s\u00e0ng \u0111\u1ec3 b\u1ed5 sung cho s\u1ef1 ph\u00f9 h\u1ee3p v\u1ec1 m\u1eb7t l\u00fd thuy\u1ebft n\u00e0y trong vi\u1ec7c ng\u0103n ch\u1eb7n t\u00ecnh tr\u1ea1ng m\u1ea5t th\u1ecb gi\u00e1c\n    ### \u0110\u1ea7u ra\n    "},{"assistant_initiates":false,"asst_text":"True","behavior":"degenerate_repetition","conversation_hash":"606e81e2c16253f2b44f2e88ec58f81e","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":1519,"n_turns_in_conversation":1,"n_words":247,"source_row_offset":5208,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:29 GMT","turn_id":"a9e71801bdeeadc14a914b42","turn_index":0,"user_text":"System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGIANL text. Respond strictly with either 'True' or 'False'.\n==TEXT==\n\nUser: \n  ORIGINAL: \ud83d\udfe8 Business Analyst | Middle\n\u25ab\ufe0f null | null\n\u25ab\ufe0f 155k - 180k RUB\n\u25ab\ufe0f Russia | P\u0435\u0440\u043c\u044c\n\n\u041e\u0444\u0438\u0441 \ud83c\udfe2\n\u0422\u0440\u0435\u0431\u0443\u0435\u043c\u044b\u0435 \u044f\u0437\u044b\u043a\u0438: \ud83c\uddf7\ud83c\uddfa\n\nSkills: programming_basics, scrum, agile\nTags: #yellow #it #business_analyst\n\n\ud83d\udc49 \u041a\u043e\u043d\u0442\u0430\u043a\u0442\u044b \u0438 \u043f\u043e\u043b\u043d\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435\n  PROMPT: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.\n\nIgnore text's markdowm markup, use these emojis to highlight each section: \ud83c\udfe2 for employer, \ud83d\udcbc for title, \ud83d\udccd for location, \ud83d\udd39 for tasks, \ud83d\udcb0 for salary, and \ud83d\udcde for contact.\n\nEach block should be on a new line, in the following format (keep emoji, replace text labels):\n\ud83d\udcbc title  \n\ud83c\udfe2 employer  \n\ud83d\udccd location  \n\ud83d\udd39 tasks  \n\ud83d\udcb0 salary  \n\ud83d\udcde contact details\n\nMake sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.\n\n==TEXT==\n\n  SUMMARY: \ud83d\udfe8 Business Analyst  \n\u25ab\ufe0f null  \n\u25ab\ufe0f 155k - 180k RUB  \n\u25ab\ufe0f Russia | P\u0435\u0440\u043c\u044c \u041e\u0444\u0438\u0441  \n\u25ab\ufe0f \u0422\u0440\u0435\u0431\u0443\u0435\u043c\u044b\u0435 \u044f\u0437\u044b\u043a\u0438: \ud83c\uddf7\ud83c\uddfa Skills: programming_basics, scrum, agile  \n\u25ab\ufe0f \ud83d\udc49 \u041a\u043e\u043d\u0442\u0430\u043a\u0442\u044b \u0438 \u043f\u043e\u043b\u043d\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435  \n  \nAssistant:"},{"assistant_initiates":false,"asst_text":"\ud83d\udcbc Lead System Analyst  \n\ud83c\udfe2 null  \n\ud83d\udccd \u0423\u0434\u0430\u043b\u0435\u043d\u043a\u0430  \n\ud83d\udd39 \u041e\u0441\u043d\u043e\u0432\u043d\u044b\u0435 \u0437\u0430\u0434\u0430\u0447\u0438 \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0442 \u0430\u043d\u0430\u043b\u0438\u0437 \u0438 \u043f\u0440\u043e\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0438\u0441\u0442\u0435\u043c \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0439, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a rabbitmq, restapi, \u0438 sql  \n\ud83d\udcb0 155k - 180k RUB  \n\ud83d\udcde \u041a\u043e\u043d\u0442\u0430\u043a\u0442\u044b \u0438 \u043f\u043e\u043b\u043d\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435","behavior":"degenerate_repetition","conversation_hash":"82cd0b09ab66094849a5bbbacd4be236","conversation_redacted":false,"conversation_toxic":false,"country":"The Netherlands","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":1324,"n_turns_in_conversation":1,"n_words":218,"source_row_offset":5209,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:29 GMT","turn_id":"fd9de5817d5701264f56500f","turn_index":0,"user_text":"System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.\n\nIgnore text's markdowm markup, use these emojis to highlight each section: \ud83c\udfe2 for employer, \ud83d\udcbc for title, \ud83d\udccd for location, \ud83d\udd39 for tasks, \ud83d\udcb0 for salary, and \ud83d\udcde for contact.\n\nEach block should be on a new line, in the following format (keep emoji, replace text labels):\n\ud83d\udcbc title  \n\ud83c\udfe2 employer  \n\ud83d\udccd location  \n\ud83d\udd39 tasks  \n\ud83d\udcb0 salary  \n\ud83d\udcde contact details\n\nMake sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.\n\n==TEXT==\n\nUser: \ud83d\udfe8 Lead System Analyst | 3 year(s) | Middle\n\u25ab\ufe0f null | null\n\u25ab\ufe0f 155k - 180k RUB\n\n\u0423\u0434\u0430\u043b\u0435\u043d\u043a\u0430 \ud83d\udce1\n\u0422\u0440\u0435\u0431\u0443\u0435\u043c\u044b\u0435 \u044f\u0437\u044b\u043a\u0438: \ud83c\uddf7\ud83c\uddfa\n\nSkills: rabbitmq, restapi, sql, uml, jira, confluence, websocket, bpmn, user_stories\nTags: #yellow #it #remote #system_analyst\n\n\ud83d\udc49 \u041a\u043e\u043d\u0442\u0430\u043a\u0442\u044b \u0438 \u043f\u043e\u043b\u043d\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435\nAssistant:"},{"assistant_initiates":false,"asst_text":"\ud83d\udcbc \u12a0\u1235\u1270\u121b\u122a / \u1270\u124b\u121a / \u12e8\u12a2\u12ae\u1296\u121a\u12ab\u1293 \u1202\u1235\u1270\u122a \u12a0\u1235\u1270\u121b\u122a\u12ce\u127d  \n\ud83c\udfe2 School of Tomorrow  \n\ud83d\udccd Addis Ababa  \n\ud83d\udd39 \u12cb\u1290\u129b \u1225\u122b: \u1260\u12a0\u1295\u12f5\u1290\u1275 \u12a5\u1293 \u1260\u120d\u1218\u12f5 \u1260\u1270\u12db\u12f2\u12cb \u1218\u121d\u1205\u122b\u1295\u1295 \u1218\u1230\u12e8\u121d  \n\ud83d\udcb0 \u12f0\u121d\u12c8\u122d \u121d\u1295\u121d \u130d\u121d\u1308\u121b \u12a0\u120d\u1270\u1218\u1208\u12a8\u1270\u121d  \n\ud83d\udcde\u12a5\u1245\u134d: https://dailyjobsethiopia.com/2024/11/05/school-of-tomorrow-job-vacancy-2/","behavior":"degenerate_repetition","conversation_hash":"50fbf75ca6ccadc72006f29653d2e6b5","conversation_redacted":false,"conversation_toxic":false,"country":"Brazil","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":2021,"n_turns_in_conversation":1,"n_words":302,"source_row_offset":5210,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:30 GMT","turn_id":"632db4278a6f78f69223a470","turn_index":0,"user_text":"System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.\n\nIgnore text's markdowm markup, use these emojis to highlight each section: \ud83c\udfe2 for employer, \ud83d\udcbc for title, \ud83d\udccd for location, \ud83d\udd39 for tasks, \ud83d\udcb0 for salary, and \ud83d\udcde for contact.\n\nEach block should be on a new line, in the following format (keep emoji, replace text labels):\n\ud83d\udcbc title  \n\ud83c\udfe2 employer  \n\ud83d\udccd location  \n\ud83d\udd39 tasks  \n\ud83d\udcb0 salary  \n\ud83d\udcde contact details\n\nMake sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.\n\n==TEXT==\n\nUser: \u2605 \u1235\u12a9\u120d \u12a6\u134d \u1271\u121e\u122e \u12e8\u1235\u122b \u121b\u1235\u1273\u12c8\u1242\u12eb [\u12600 \u12a0\u1218\u1275 \u12a5\u1293 \u1260\u120d\u121d\u12f5] \n\n\u2666\ufe0fDeadline: November 30, 2024\n\nSchool of tomorrow is looking for fresh and qualified applicants for the following job positions.\n\n\u2714\ufe0f Position 1: Elementary/KG Teachers\n\n\u2747\ufe0f Qualification:\u00a0BSC Degree\u00a0 in Natural and Social Science field of study from a recognized\u00a0\u00a0 institution\n\n\u2714\ufe0f Position 2: High School Mathematics/Physics /Biology /Chemistry Teachers\n\n\u2747\ufe0f Qualification: BED /BSC (PGDT) in Physics, Mathematics, Biology and Chemistry from a recognized institution.\n\n\u2714\ufe0f Position 3: High School / History /Civics /Geography / Economics Stream Teachers\n\n\u2747\ufe0f Qualification: BED /BA (PGDT) in History, Civics, Geography and Economics from a recognized institution\n\n\u2714\ufe0f Position 4: Accountant \n\n\u2747\ufe0f Qualification: BSc degree or Level IV In Accounting or related fields \n\n\ud83d\udd3bLocation : Addis Ababa \n\n\ud83c\udf00How to Apply?\n  \ud83d\udc47\ud83d\udc47\ud83d\udc47\ud83d\udc47\nhttps://dailyjobsethiopia.com/2024/11/05/school-of-tomorrow-job-vacancy-2/\n\nShare to your friends\nAssistant:"},{"assistant_initiates":false,"asst_text":"\ud83d\udcbc Business Analyst | Middle  \n\ud83c\udfe2 null  \n\ud83d\udccd Russia | \u041f\u0435\u0440\u043c\u044c  \n\ud83d\udd39 \u0417\u0430\u0434\u0430\u0447\u0438 \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0442 \u0430\u043d\u0430\u043b\u0438\u0437 \u0431\u0438\u0437\u043d\u0435\u0441-\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0432 \u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 scrum \u0438 agile.  \n\ud83d\udcb0 155k - 180k RUB  \n\ud83d\udcde \u041a\u043e\u043d\u0442\u0430\u043a\u0442\u044b \u0438 \u043f\u043e\u043b\u043d\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435","behavior":"degenerate_repetition","conversation_hash":"0be65f3d3bd60a7c032077eca115d142","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-4o-2024-08-06","n_chars":1241,"n_turns_in_conversation":1,"n_words":208,"source_row_offset":5211,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:30 GMT","turn_id":"a91f9f677531bc2c31d86148","turn_index":0,"user_text":"System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.\n\nIgnore text's markdowm markup, use these emojis to highlight each section: \ud83c\udfe2 for employer, \ud83d\udcbc for title, \ud83d\udccd for location, \ud83d\udd39 for tasks, \ud83d\udcb0 for salary, and \ud83d\udcde for contact.\n\nEach block should be on a new line, in the following format (keep emoji, replace text labels):\n\ud83d\udcbc title  \n\ud83c\udfe2 employer  \n\ud83d\udccd location  \n\ud83d\udd39 tasks  \n\ud83d\udcb0 salary  \n\ud83d\udcde contact details\n\nMake sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.\n\n==TEXT==\n\nUser: \ud83d\udfe8 Business Analyst | Middle\n\u25ab\ufe0f null | null\n\u25ab\ufe0f 155k - 180k RUB\n\u25ab\ufe0f Russia | P\u0435\u0440\u043c\u044c\n\n\u041e\u0444\u0438\u0441 \ud83c\udfe2\n\u0422\u0440\u0435\u0431\u0443\u0435\u043c\u044b\u0435 \u044f\u0437\u044b\u043a\u0438: \ud83c\uddf7\ud83c\uddfa\n\nSkills: programming_basics, scrum, agile\nTags: #yellow #it #business_analyst\n\n\ud83d\udc49 \u041a\u043e\u043d\u0442\u0430\u043a\u0442\u044b \u0438 \u043f\u043e\u043b\u043d\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435\nAssistant:"},{"assistant_initiates":false,"asst_text":"\ud83d\udcbc Lifecycle Marketing Manager  \n\ud83c\udfe2 Kikoff  \n\ud83d\udccd USA | San Francisco  \n\ud83d\udd39 Responsible for developing and executing lifecycle marketing strategies using AB testing, data analysis, and SQL.  \n\ud83d\udcb0 90k - 150k USD  \n\ud83d\udcde Please refer to the full job description for contact details.","behavior":"degenerate_repetition","conversation_hash":"b288e2f41bcc95747f25aaf47f1147a6","conversation_redacted":false,"conversation_toxic":false,"country":"Brazil","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":1353,"n_turns_in_conversation":1,"n_words":224,"source_row_offset":5212,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:31 GMT","turn_id":"f6da9d90d58699ed359ce990","turn_index":0,"user_text":"System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.\n\nIgnore text's markdowm markup, use these emojis to highlight each section: \ud83c\udfe2 for employer, \ud83d\udcbc for title, \ud83d\udccd for location, \ud83d\udd39 for tasks, \ud83d\udcb0 for salary, and \ud83d\udcde for contact.\n\nEach block should be on a new line, in the following format (keep emoji, replace text labels):\n\ud83d\udcbc title  \n\ud83c\udfe2 employer  \n\ud83d\udccd location  \n\ud83d\udd39 tasks  \n\ud83d\udcb0 salary  \n\ud83d\udcde contact details\n\nMake sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.\n\n==TEXT==\n\nUser: \ud83d\udfea Lifecycle Marketing Manager | 3 year(s) | Middle\n\u25ab\ufe0f Kikoff | Fintech\n\u25ab\ufe0f 90k - 150k USD\n\u25ab\ufe0f USA | San Francisco\n\nOffice \ud83c\udfe2\nRequired languages: \ud83c\uddec\ud83c\udde7\n\nSkills: ab_teting, data, sql\nTags: #purple #marketing #marketing_pr\n\n\ud83d\udc49 Contacts and Full Description\nAssistant:"},{"assistant_initiates":false,"asst_text":"False","behavior":"degenerate_repetition","conversation_hash":"45f3853197d01159e7304a7273d37ed9","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":714,"n_turns_in_conversation":1,"n_words":108,"source_row_offset":5213,"source_shard":"turns-00045.parquet","timestamp":"Tue, 05 Nov 2024 22:27:31 GMT","turn_id":"69b60887ed5364fee67fabdf","turn_index":0,"user_text":"System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Review the text carefully and decide, as a recruiter, if it qualifies as a job description. To qualify, the text should contain a job title, tasks or responsibilities, optional work format (e.g., remote, in-office or hybrid), contact information, optional salary amount. Multiple job descriptions at once do not quialify. Respond strictly with either 'True' or 'False'.\n\n==TEXT==\n\nUser: \ud83d\udfea Product Designer | 6 year(s) | Senior\n\u25ab\ufe0f Plaid | Financial Technology\n\u25ab\ufe0f 144.7k - 216k USD\n\nRemote \ud83d\udce1\nRequired languages: \ud83c\uddec\ud83c\udde7\n\nSkills: visual_design\nTags: #purple #it #remote #ux_ui_design\n\n\ud83d\udc49 Contacts and Full Description\nAssistant:"}],"shard":{"index":45,"name":"turns-00045.parquet","rows":37951}}