{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":9012,"offset":9002,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"Chuck McCann's memorable theme song performance incorporated elements from \"Bye Bye, Birdie,\" as he sang \"Put on a Happy Face.\" He included the lyric \"Stick out that noble chin\" while imitating Stan Laurel, complete with Stan's familiar grin and gesture of sticking his arms out with palms up. This unique imitation was a tribute to Laurel's iconic mannerisms seen in his films. Meanwhile, the show cleverly included a playful reference when a character teased Ann about her appearance, noting that before meeting her and considering her divorced status, he had expected her to resemble Maude. This in-joke alluded to Norman Lear's other successful feminist sitcom airing during the same era. Moreover, the show's cultural impact was such that it was spoofed by Mad Magazine under the title \"One Dame at a Time,\" reflecting its recognition and influence in popular media. Reply finished","behavior":"degenerate_repetition","conversation_hash":"690c5298317903627545218c945d3888","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":4492,"n_turns_in_conversation":1,"n_words":717,"source_row_offset":9002,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:15:48 GMT","turn_id":"16c722b68ac46b9783d520e3","turn_index":0,"user_text":"Generate a concise, detailed and straightforward text focusing on the topics i will give you about the tv series One Day at a Time from 1975. I'm looking for a straightforward and non-flowery style in this article with minimal use of adjectives. Please use simple and easily understandable language throughout the article. Try to avoid repeating the term 1975 tv series One Day at a Time too often in your response. This is used for a middle part in our script. Cut out intro and outro and jump right into the given facts. Using the information provided, craft a narrative or response that begins with a general statement or an engaging opening sentence that does not include the name or title of the context in question. Here Are the facts :\n \n1: Chuck McCann: McCann's theme song was \"Put on a Happy Face\" from \"Bye Bye, Birdie\". He sang the line \"Stick out that noble chin\", in Stan Laurel's voice, grinned like Stan, and stuck his arms out, palms up, as Stan often did in the films.\n2: At one point in the show, one of the characters teases Ann that before he met her, and when he found out she was divorced, he \"expected her to look like Maude.\" This was an in-joke about Norman Lear's other popular feminist sitcom that was on at that time, Maude (1972).\n3: Spoofed in Mad Magazine as \"One Dame at a Time\".\n\n\n        Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks.\n        In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.\n        Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response. \n        Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. \n        The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. \n        Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. \n        It is very important to provide only the final output without any additional comments or remarks for example: 'Certainly!' Here's the revised text.\n         \n        Please provide the text directly without any labels such as \"Conclusion:\", \"Introduction:\", or \"Transition:\". Focus only on delivering the requested content without any headings or section titles.\n        Please generate a complete and coherent response based on the provided input. Ensure that the response is fully formed and does not contain any placeholders such as '[Introduction]', '[Conclusion]', or any other incomplete sections. \n        The text should flow naturally without any gaps or missing information, and no placeholder markers or brackets should be used. \n        Please respond to my question, and always conclude your reply with the words \"Reply finished\" to indicate that your response is complete. \n        Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}\n         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you."},{"assistant_initiates":false,"asst_text":"Hello! How can I assist you today?","behavior":"degenerate_repetition","conversation_hash":"d6a9b5ec7baa0024275770df10b2d3b3","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Nolang","model":"gpt-4o-mini-2024-07-18","n_chars":34,"n_turns_in_conversation":1,"n_words":7,"source_row_offset":9003,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:15:48 GMT","turn_id":"5f50cb38f32ec211f62109db","turn_index":0,"user_text":""},{"assistant_initiates":false,"asst_text":"\u0421\u0418\u0413\u041d\u0410\u041b: LONG  \n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: MACD \u0438 ADX  \n\u0423\u0420\u041e\u0412\u041d\u0418: \u0432\u0445\u043e\u0434 171.15 / \u0441\u0442\u043e\u043f 170.05 / \u0446\u0435\u043b\u044c 175.29  \n\u0420\u0418\u0421\u041a: \u0421\u0420\u0415\u0414\u041d\u0418\u0419  \n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: MACD \u043f\u043e\u043b\u043e\u0436\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0439, \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043d\u0430 \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b \u0440\u043e\u0441\u0442\u0430; ADX \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u0441\u0440\u0435\u0434\u043d\u0438\u0439 \u0442\u0440\u0435\u043d\u0434  \n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: \u043d\u0435\u0442  \n3-5 \u0432\u0430\u0436\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439: MACD 0.44, ADX 27.25, RSI 51.5, Stoch.K 16.32, CCI20 -32.1  ","behavior":"degenerate_repetition","conversation_hash":"f098ca2ebbf87bb41b5ff6ace0c4ac19","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":3503,"n_turns_in_conversation":1,"n_words":265,"source_row_offset":9004,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:16:01 GMT","turn_id":"25e4546754f7d0d288ff2db1","turn_index":0,"user_text":"\u041f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0439 SOLUSDT (15m) \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e \u043a\u0440\u0430\u0442\u043a\u043e.\n\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 \u0442\u043e\u043b\u044c\u043a\u043e \u044d\u0442\u0438 \u0434\u0430\u043d\u043d\u044b\u0435:\nSummary: {'RECOMMENDATION': 'BUY', 'BUY': 11, 'SELL': 6, 'NEUTRAL': 9}\nIndicators: {'Recommend.Other': 0.09090909090909091, 'Recommend.All': 0.17878787878787877, 'Recommend.MA': 0.26666666666666666, 'RSI': 51.5034563482285, 'RSI[1]': 53.83482620910406, 'Stoch.K': 16.31911196226572, 'Stoch.D': 27.459610644971594, 'Stoch.K[1]': 20.435980187017766, 'Stoch.D[1]': 22.196608780288248, 'CCI20': -32.105662459486574, 'CCI20[1]': -20.033309540809242, 'ADX': 27.253069254678053, 'ADX+DI': 15.996758862510752, 'ADX-DI': 17.28787855456011, 'ADX+DI[1]': 16.71501695622247, 'ADX-DI[1]': 18.064108214683216, 'AO': 0.8518823529408905, 'AO[1]': 0.9806470588232798, 'Mom': -1.259999999999991, 'Mom[1]': -1.4000000000000057, 'MACD.macd': 0.4416490671551685, 'MACD.signal': 0.644529712925835, 'Rec.Stoch.RSI': 0, 'Stoch.RSI.K': 2.070900068785875, 'Rec.WR': 0, 'W.R': -87.1369294605808, 'Rec.BBPower': 1, 'BBPower': -0.35694578648829633, 'Rec.UO': 0, 'UO': 39.326890048673036, 'close': 171.15, 'EMA5': 171.36621788964214, 'SMA5': 171.368, 'EMA10': 171.49751715272484, 'SMA10': 171.7089999999999, 'EMA20': 171.27141359202008, 'SMA20': 171.56099999999975, 'EMA30': 170.89450934722038, 'SMA30': 170.819, 'EMA50': 170.25467783287652, 'SMA50': 169.9391999999999, 'EMA100': 169.74449653700466, 'SMA100': 168.10780000000014, 'EMA200': 170.04658496132348, 'SMA200': 170.7321499999999, 'Rec.Ichimoku': 0, 'Ichimoku.BLine': 171.065, 'Rec.VWMA': -1, 'VWMA': 171.71753350160574, 'Rec.HullMA9': 1, 'HullMA9': 171.14455555555557, 'Pivot.M.Classic.S3': 130.2466666666667, 'Pivot.M.Classic.S2': 148.7266666666667, 'Pivot.M.Classic.S1': 156.81333333333336, 'Pivot.M.Classic.Middle': 167.20666666666668, 'Pivot.M.Classic.R1': 175.29333333333335, 'Pivot.M.Classic.R2': 185.68666666666667, 'Pivot.M.Classic.R3': 204.16666666666666, 'Pivot.M.Fibonacci.S3': 148.7266666666667, 'Pivot.M.Fibonacci.S2': 155.7860266666667, 'Pivot.M.Fibonacci.S1': 160.14730666666668, 'Pivot.M.Fibonacci.Middle': 167.20666666666668, 'Pivot.M.Fibonacci.R1': 174.26602666666668, 'Pivot.M.Fibonacci.R2': 178.62730666666667, 'Pivot.M.Fibonacci.R3': 185.68666666666667, 'Pivot.M.Camarilla.S3': 159.818, 'Pivot.M.Camarilla.S2': 161.512, 'Pivot.M.Camarilla.S1': 163.20600000000002, 'Pivot.M.Camarilla.Middle': 167.20666666666668, 'Pivot.M.Camarilla.R1': 166.594, 'Pivot.M.Camarilla.R2': 168.288, 'Pivot.M.Camarilla.R3': 169.982, 'Pivot.M.Woodie.S3': 137.19, 'Pivot.M.Woodie.S2': 148.155, 'Pivot.M.Woodie.S1': 155.67, 'Pivot.M.Woodie.Middle': 166.635, 'Pivot.M.Woodie.R1': 174.14999999999998, 'Pivot.M.Woodie.R2': 185.11499999999998, 'Pivot.M.Woodie.R3': 192.62999999999997, 'Pivot.M.Demark.S1': 152.77, 'Pivot.M.Demark.Middle': 165.185, 'Pivot.M.Demark.R1': 171.25, 'open': 171.37, 'P.SAR': 172.85186530559997, 'BB.lower': 169.88331230549682, 'BB.upper': 173.2386876945027, 'AO[2]': 1.3097647058821167, 'volume': 17701.834, 'change': -0.12837719554180943, 'low': 171.06, 'high': 171.53}\n\n\u041e\u0442\u0432\u0435\u0442\u044c \u0441\u0442\u0440\u043e\u0433\u043e \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435:\n\u0421\u0418\u0413\u041d\u0410\u041b: [LONG/SHORT/NEUTRAL]\n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: [1-2 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u0438\u043d\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u0430]\n\u0423\u0420\u041e\u0412\u041d\u0418: [\u0432\u0445\u043e\u0434/\u0441\u0442\u043e\u043f/\u0446\u0435\u043b\u044c]\n\u0420\u0418\u0421\u041a: [\u041d\u0418\u0417\u041a\u0418\u0419/\u0421\u0420\u0415\u0414\u041d\u0418\u0419/\u0412\u042b\u0421\u041e\u041a\u0418\u0419]\n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: [\u043a\u0440\u0430\u0442\u043a\u043e]\n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: [\u0435\u0441\u0442\u044c/\u043d\u0435\u0442]\n3-5 \u0432\u0430\u0436\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439"},{"assistant_initiates":false,"asst_text":"\u0421\u0418\u0413\u041d\u0410\u041b: LONG  \n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: MA, MACD  \n\u0423\u0420\u041e\u0412\u041d\u0418: \u0432\u0445\u043e\u0434 171.15 / \u0441\u0442\u043e\u043f 169.80 / \u0446\u0435\u043b\u044c 174.63  \n\u0420\u0418\u0421\u041a: \u0421\u0420\u0415\u0414\u041d\u0418\u0419  \n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: \u0411\u044b\u0441\u0442\u0440\u044b\u0435 \u0441\u043a\u043e\u043b\u044c\u0437\u044f\u0449\u0438\u0435 \u0441\u0440\u0435\u0434\u043d\u0438\u0435 (MA) \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442 \u043d\u0430 \u0432\u043e\u0441\u0445\u043e\u0434\u044f\u0449\u0435\u0435 \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u0435, \u0438 MACD \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u0442 \u043f\u043e\u043b\u043e\u0436\u0438\u0442\u0435\u043b\u044c\u043d\u0443\u044e \u0434\u0438\u043d\u0430\u043c\u0438\u043a\u0443 \u0432\u044b\u0448\u0435 \u043b\u0438\u043d\u0438\u0438 \u0441\u0438\u0433\u043d\u0430\u043b\u0430.  \n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: \u043d\u0435\u0442  \n3-5 \u0432\u0430\u0436\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439: MA, MACD, RSI, Stoch.K, CCI20","behavior":"degenerate_repetition","conversation_hash":"4049b8499581a1ccab4c568602ad9735","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":3579,"n_turns_in_conversation":1,"n_words":265,"source_row_offset":9005,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:16:03 GMT","turn_id":"9ef72682b5d07c5280cbc5a7","turn_index":0,"user_text":"\u041f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0439 SOLUSDT (1h) \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e \u043a\u0440\u0430\u0442\u043a\u043e.\n\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 \u0442\u043e\u043b\u044c\u043a\u043e \u044d\u0442\u0438 \u0434\u0430\u043d\u043d\u044b\u0435:\nSummary: {'RECOMMENDATION': 'BUY', 'BUY': 15, 'SELL': 2, 'NEUTRAL': 9}\nIndicators: {'Recommend.Other': 0.09090909090909091, 'Recommend.All': 0.4454545454545455, 'Recommend.MA': 0.8, 'RSI': 56.45642916315914, 'RSI[1]': 57.540981938000336, 'Stoch.K': 67.16500553709874, 'Stoch.D': 74.73318694944186, 'Stoch.K[1]': 75.24450584665232, 'Stoch.D[1]': 80.81184847061485, 'CCI20': 100.95953274928021, 'CCI20[1]': 118.29956608268003, 'ADX': 22.985700463874963, 'ADX+DI': 21.618820208666953, 'ADX-DI': 18.049209798176797, 'ADX+DI[1]': 22.038851194377415, 'ADX-DI[1]': 18.399887000246494, 'AO': 2.9383823529409767, 'AO[1]': 2.584382352940935, 'Mom': 3.0800000000000125, 'Mom[1]': 2.3499999999999943, 'MACD.macd': 0.5755471788009743, 'MACD.signal': 0.10290444261204443, 'Rec.Stoch.RSI': 0, 'Stoch.RSI.K': 65.06744261619384, 'Rec.WR': 0, 'W.R': -34.88372093023241, 'Rec.BBPower': 0, 'BBPower': 2.132275645529859, 'Rec.UO': 0, 'UO': 50.84283355750478, 'close': 171.15, 'EMA5': 171.1490084073107, 'SMA5': 171.5759999999999, 'EMA10': 170.5425399641871, 'SMA10': 170.33800000000014, 'EMA20': 169.82426205216498, 'SMA20': 169.25150000000025, 'EMA30': 169.77004835846896, 'SMA30': 168.1700000000001, 'EMA50': 170.00727281163827, 'SMA50': 170.5183999999999, 'EMA100': 169.43240575964055, 'SMA100': 170.62979999999985, 'EMA200': 166.18084693973668, 'SMA200': 166.17729999999986, 'Rec.Ichimoku': 0, 'Ichimoku.BLine': 166.185, 'Rec.VWMA': 1, 'VWMA': 169.02519934335575, 'Rec.HullMA9': -1, 'HullMA9': 172.00240740740742, 'Pivot.M.Classic.S3': 118.43333333333337, 'Pivot.M.Classic.S2': 139.57333333333335, 'Pivot.M.Classic.S1': 153.48666666666668, 'Pivot.M.Classic.Middle': 160.71333333333334, 'Pivot.M.Classic.R1': 174.62666666666667, 'Pivot.M.Classic.R2': 181.85333333333332, 'Pivot.M.Classic.R3': 202.9933333333333, 'Pivot.M.Fibonacci.S3': 139.57333333333335, 'Pivot.M.Fibonacci.S2': 147.64881333333335, 'Pivot.M.Fibonacci.S1': 152.63785333333334, 'Pivot.M.Fibonacci.Middle': 160.71333333333334, 'Pivot.M.Fibonacci.R1': 168.78881333333334, 'Pivot.M.Fibonacci.R2': 173.77785333333333, 'Pivot.M.Fibonacci.R3': 181.85333333333332, 'Pivot.M.Camarilla.S3': 161.5865, 'Pivot.M.Camarilla.S2': 163.52433333333335, 'Pivot.M.Camarilla.S1': 165.46216666666666, 'Pivot.M.Camarilla.Middle': 160.71333333333334, 'Pivot.M.Camarilla.R1': 169.33783333333335, 'Pivot.M.Camarilla.R2': 171.27566666666667, 'Pivot.M.Camarilla.R3': 173.2135, 'Pivot.M.Woodie.S3': 135.69, 'Pivot.M.Woodie.S2': 141.245, 'Pivot.M.Woodie.S1': 156.82999999999998, 'Pivot.M.Woodie.Middle': 162.385, 'Pivot.M.Woodie.R1': 177.96999999999997, 'Pivot.M.Woodie.R2': 183.52499999999998, 'Pivot.M.Woodie.R3': 199.10999999999996, 'Pivot.M.Demark.S1': 157.10000000000002, 'Pivot.M.Demark.Middle': 162.52, 'Pivot.M.Demark.R1': 178.24, 'open': 171.37, 'P.SAR': 170.77448557014708, 'BB.lower': 166.02404109248187, 'BB.upper': 172.47895890751863, 'AO[2]': 2.1516764705880576, 'volume': 17701.834, 'change': -0.12837719554180943, 'low': 171.06, 'high': 171.53}\n\n\u041e\u0442\u0432\u0435\u0442\u044c \u0441\u0442\u0440\u043e\u0433\u043e \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435:\n\u0421\u0418\u0413\u041d\u0410\u041b: [LONG/SHORT/NEUTRAL]\n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: [1-2 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u0438\u043d\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u0430]\n\u0423\u0420\u041e\u0412\u041d\u0418: [\u0432\u0445\u043e\u0434/\u0441\u0442\u043e\u043f/\u0446\u0435\u043b\u044c]\n\u0420\u0418\u0421\u041a: [\u041d\u0418\u0417\u041a\u0418\u0419/\u0421\u0420\u0415\u0414\u041d\u0418\u0419/\u0412\u042b\u0421\u041e\u041a\u0418\u0419]\n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: [\u043a\u0440\u0430\u0442\u043a\u043e]\n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: [\u0435\u0441\u0442\u044c/\u043d\u0435\u0442]\n3-5 \u0432\u0430\u0436\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439"},{"assistant_initiates":false,"asst_text":"\u0421\u0418\u0413\u041d\u0410\u041b: LONG  \n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: MACD \u0438 AO  \n\u0423\u0420\u041e\u0412\u041d\u0418: \u0432\u0445\u043e\u0434 171.15, \u0441\u0442\u043e\u043f 169.21, \u0446\u0435\u043b\u044c 174.62  \n\u0420\u0418\u0421\u041a: \u0421\u0420\u0415\u0414\u041d\u0418\u0419  \n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: MACD \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u0431\u044b\u0447\u0438\u0439 \u0441\u0438\u0433\u043d\u0430\u043b \u0441 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u0441\u043b\u0430\u0431\u043e\u0433\u043e \u043f\u043e\u043b\u043e\u0436\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0433\u043e \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u044f AO, \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u043e\u0432\u0430\u043d\u043e \u043f\u043e\u043a\u0443\u043f\u043a\u0430  \n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: \u043d\u0435\u0442  \n\u0412\u0430\u0436\u043d\u044b\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438: MACD.macd (0.621), AO (-0.114), ADX+DI (26.077), BB.lower (164.75), BB.upper (178.64)","behavior":"degenerate_repetition","conversation_hash":"f5eea0f53d24639e106cdaee9673d527","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":3582,"n_turns_in_conversation":1,"n_words":264,"source_row_offset":9006,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:16:08 GMT","turn_id":"2b5a2c0f53fa65d1efe40140","turn_index":0,"user_text":"\u041f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0439 SOLUSDT (4h) \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e \u043a\u0440\u0430\u0442\u043a\u043e.\n\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 \u0442\u043e\u043b\u044c\u043a\u043e \u044d\u0442\u0438 \u0434\u0430\u043d\u043d\u044b\u0435:\nSummary: {'RECOMMENDATION': 'BUY', 'BUY': 13, 'SELL': 3, 'NEUTRAL': 10}\nIndicators: {'Recommend.Other': 0, 'Recommend.All': 0.3333333333333333, 'Recommend.MA': 0.6666666666666666, 'RSI': 54.468135963045185, 'RSI[1]': 55.37004155695811, 'Stoch.K': 56.06210769739014, 'Stoch.D': 51.0131042836692, 'Stoch.K[1]': 56.709956709956735, 'Stoch.D[1]': 49.567546482809114, 'CCI20': 3.7564029595887556, 'CCI20[1]': -13.585247071963046, 'ADX': 18.073959821105838, 'ADX+DI': 26.077724252962973, 'ADX-DI': 22.836266778684088, 'ADX+DI[1]': 25.500399833087634, 'ADX-DI[1]': 23.856187935771928, 'AO': -0.11455882352936442, 'AO[1]': -1.631411764705831, 'Mom': -0.75, 'Mom[1]': -3.1599999999999966, 'MACD.macd': 0.6212563555068016, 'MACD.signal': 1.17031869645604, 'Rec.Stoch.RSI': 0, 'Stoch.RSI.K': 37.236119021455146, 'Rec.WR': 0, 'W.R': -40.38652130822598, 'Rec.BBPower': 0, 'BBPower': 4.058740687052335, 'Rec.UO': 0, 'UO': 56.00160690991023, 'close': 171.15, 'EMA5': 169.96547623959367, 'SMA5': 169.46000000000026, 'EMA10': 169.91541727683605, 'SMA10': 169.18900000000025, 'EMA20': 169.8649421234252, 'SMA20': 171.69549999999987, 'EMA30': 168.9260632658483, 'SMA30': 170.16633333333326, 'EMA50': 166.26688787058612, 'SMA50': 166.38019999999997, 'EMA100': 160.68746449612067, 'SMA100': 157.95980000000006, 'EMA200': 154.79221771533506, 'SMA200': 153.0698500000001, 'Rec.Ichimoku': 0, 'Ichimoku.BLine': 169.21, 'Rec.VWMA': -1, 'VWMA': 171.3055070129305, 'Rec.HullMA9': 1, 'HullMA9': 170.89499999999998, 'Pivot.M.Classic.S3': 118.43333333333337, 'Pivot.M.Classic.S2': 139.57333333333335, 'Pivot.M.Classic.S1': 153.48666666666668, 'Pivot.M.Classic.Middle': 160.71333333333334, 'Pivot.M.Classic.R1': 174.62666666666667, 'Pivot.M.Classic.R2': 181.85333333333332, 'Pivot.M.Classic.R3': 202.9933333333333, 'Pivot.M.Fibonacci.S3': 139.57333333333335, 'Pivot.M.Fibonacci.S2': 147.64881333333335, 'Pivot.M.Fibonacci.S1': 152.63785333333334, 'Pivot.M.Fibonacci.Middle': 160.71333333333334, 'Pivot.M.Fibonacci.R1': 168.78881333333334, 'Pivot.M.Fibonacci.R2': 173.77785333333333, 'Pivot.M.Fibonacci.R3': 181.85333333333332, 'Pivot.M.Camarilla.S3': 161.5865, 'Pivot.M.Camarilla.S2': 163.52433333333335, 'Pivot.M.Camarilla.S1': 165.46216666666666, 'Pivot.M.Camarilla.Middle': 160.71333333333334, 'Pivot.M.Camarilla.R1': 169.33783333333335, 'Pivot.M.Camarilla.R2': 171.27566666666667, 'Pivot.M.Camarilla.R3': 173.2135, 'Pivot.M.Woodie.S3': 135.69, 'Pivot.M.Woodie.S2': 141.245, 'Pivot.M.Woodie.S1': 156.82999999999998, 'Pivot.M.Woodie.Middle': 162.385, 'Pivot.M.Woodie.R1': 177.96999999999997, 'Pivot.M.Woodie.R2': 183.52499999999998, 'Pivot.M.Woodie.R3': 199.10999999999996, 'Pivot.M.Demark.S1': 157.10000000000002, 'Pivot.M.Demark.Middle': 162.52, 'Pivot.M.Demark.R1': 178.24, 'open': 171.52, 'P.SAR': 159.3838, 'BB.lower': 164.747989726508, 'BB.upper': 178.64301027349174, 'AO[2]': -2.853441176470568, 'volume': 317552.101, 'change': -0.21571828358209216, 'low': 170.84, 'high': 173.25}\n\n\u041e\u0442\u0432\u0435\u0442\u044c \u0441\u0442\u0440\u043e\u0433\u043e \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435:\n\u0421\u0418\u0413\u041d\u0410\u041b: [LONG/SHORT/NEUTRAL]\n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: [1-2 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u0438\u043d\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u0430]\n\u0423\u0420\u041e\u0412\u041d\u0418: [\u0432\u0445\u043e\u0434/\u0441\u0442\u043e\u043f/\u0446\u0435\u043b\u044c]\n\u0420\u0418\u0421\u041a: [\u041d\u0418\u0417\u041a\u0418\u0419/\u0421\u0420\u0415\u0414\u041d\u0418\u0419/\u0412\u042b\u0421\u041e\u041a\u0418\u0419]\n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: [\u043a\u0440\u0430\u0442\u043a\u043e]\n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: [\u0435\u0441\u0442\u044c/\u043d\u0435\u0442]\n3-5 \u0432\u0430\u0436\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439"},{"assistant_initiates":false,"asst_text":"```json\n{\n    \"relevant_tasks\": [\n        {\n            \"task\": \"Risk Classification\",\n            \"examples\": [\n                {\n                    \"input\": \"The paragraph discusses potential risks associated with third-party vendor oversight.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The paragraph describes a risk-free relationship with third-party vendors.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Sentiment Classification\",\n            \"examples\": [\n                {\n                    \"input\": \"The sentiment expressed in the paragraph is one of concern regarding regulatory compliance.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The paragraph conveys a positive sentiment about the current regulatory environment.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Tense Identification\",\n            \"examples\": [\n                {\n                    \"input\": \"The paragraph mainly discusses future actions and expectations.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The paragraph discusses actions and events that have already occurred.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Regulatory Identification\",\n            \"examples\": [\n                {\n                    \"input\": \"The paragraph identifies regulatory requirements affecting third-party vendor management.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The paragraph makes no mention of any regulatory influences.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        }\n    ]\n}\n```","behavior":"degenerate_repetition","conversation_hash":"699eec18ce7b6f860893e7f024d028fd","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":4810,"n_turns_in_conversation":1,"n_words":539,"source_row_offset":9007,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:16:11 GMT","turn_id":"5469f15dce9c69631bd35b93","turn_index":0,"user_text":"System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). \n\nStep 1: Read the paragraph carefully. \nStep 2: Automatically decide which tasks are relevant based on the paragraph\u2019s content. Relevant tasks may include, but are not limited to: \n    - Topic Classification \n    - Risk Classification\n    - Sentiment Classification \n    - Tone Classification\n    - Event Detection \n    - Entity Recognition \n    - Geographical Recognition \n    - Trend Detection \n    - Intent Detection \n    - Sector Identification \n    - Impact Classification \n    - Tense Identification (Past, Present, Future) \n    - Uncertainty Identification \n\nStep 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. \nStep 4: Output the results in the following JSON format:\n\n{\n    'relevant_tasks': [\n        {\n            'task': <The automatically selected relevant task>,\n            'examples': [\n                {\n                    'input': <The entailment example based on the paragraph>,\n                    'label': 'entailment',\n                },\n                {\n                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,\n                    'label': 'non-entailment',\n                }\n            ]\n        },\n        ...\n    ]\n}\nUser: Our use of third-party vendors, including the financial technology company we partner with to create a customer portal, for certain information systems is subject to increasingly demanding regulatory requirements and attention by our federal bank regulators. Recent regulations require us to enhance our due diligence, ongoing monitoring and control over our third-party vendors and other ongoing third-party business relationships. In certain cases, we may be required to renegotiate our agreements with these vendors to meet these enhanced requirements, which could increase our costs. We expect that our regulators will hold us responsible for deficiencies in our oversight and control of our third-party relationships and in the performance of the parties with which we have these relationships. As a result, if our regulators conclude that we have not exercised adequate oversight and control over our third party vendors or other ongoing third party business relationships or that such third parties have not performed appropriately, we could be\nAssistant:"},{"assistant_initiates":false,"asst_text":"```json\n{\n    \"relevant_tasks\": [\n        {\n            \"task\": \"Risk Classification\",\n            \"examples\": [\n                {\n                    \"input\": \"The paragraph indicates a risk of increased labor costs and turnover rates.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The paragraph suggests that there is no risk of labor market impacts on the business.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Sentiment Classification\",\n            \"examples\": [\n                {\n                    \"input\": \"The sentiment expressed in the paragraph is generally negative and concerned about labor issues.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The sentiment expressed in the paragraph is overwhelmingly positive about the labor market.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Impact Classification\",\n            \"examples\": [\n                {\n                    \"input\": \"The paragraph discusses potential adverse impacts on the business due to labor shortages.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The paragraph indicates that labor shortages will have a beneficial impact on the business.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Trend Detection\",\n            \"examples\": [\n                {\n                    \"input\": \"The paragraph observes a trend of tightening and increasingly competitive local labor markets.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The paragraph identifies a trend of improving conditions in the labor market.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Tense Identification\",\n            \"examples\": [\n                {\n                    \"input\": \"The paragraph refers to labor market conditions observed in recent periods.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The paragraph primarily describes events occurring solely in the distant past.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        }\n    ]\n}\n```","behavior":"degenerate_repetition","conversation_hash":"4db85929c8689477c8ffc1ed6175eb41","conversation_redacted":false,"conversation_toxic":false,"country":"China","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":5380,"n_turns_in_conversation":1,"n_words":610,"source_row_offset":9008,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:16:16 GMT","turn_id":"33beccc2ba42b719399d9a58","turn_index":0,"user_text":"System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). \n\nStep 1: Read the paragraph carefully. \nStep 2: Automatically decide which tasks are relevant based on the paragraph\u2019s content. Relevant tasks may include, but are not limited to: \n    - Topic Classification \n    - Risk Classification\n    - Sentiment Classification \n    - Tone Classification\n    - Event Detection \n    - Entity Recognition \n    - Geographical Recognition \n    - Trend Detection \n    - Intent Detection \n    - Sector Identification \n    - Impact Classification \n    - Tense Identification (Past, Present, Future) \n    - Uncertainty Identification \n\nStep 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. \nStep 4: Output the results in the following JSON format:\n\n{\n    'relevant_tasks': [\n        {\n            'task': <The automatically selected relevant task>,\n            'examples': [\n                {\n                    'input': <The entailment example based on the paragraph>,\n                    'label': 'entailment',\n                },\n                {\n                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,\n                    'label': 'non-entailment',\n                }\n            ]\n        },\n        ...\n    ]\n}\nUser: A number of factors may adversely affect the labor force available to us or increase labor costs, including the high employment levels and decreased labor force size and participation rates in recent periods. Although we have not experienced any material labor shortage to date, we have recently observed an overall tightening and increasingly competitive local labor markets. A sustained labor shortage or increased turnover rates within our employee base could lead to increased costs, such as increased compensation expense to attract and retain employees. In addition, if we are unable to hire and retain employees capable of performing at a high-level, or if mitigation measures we take to respond to a decrease in labor availability have unintended negative effects, our business could be adversely affected. An overall labor shortage, lack of skilled labor, increased turnover or labor inflation, caused by general macroeconomic factors, could have a material adverse impact on our business, financial condition, results of operations and growth prospects.\nAssistant:"},{"assistant_initiates":false,"asst_text":"\u0421\u0418\u0413\u041d\u0410\u041b: LONG  \n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: MACD, ADX  \n\u0423\u0420\u041e\u0412\u041d\u0418: \u0432\u0445\u043e\u0434 171.00 / \u0441\u0442\u043e\u043f 169.00 / \u0446\u0435\u043b\u044c 174.50  \n\u0420\u0418\u0421\u041a: \u0421\u0420\u0415\u0414\u041d\u0418\u0419  \n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: \u0418\u043d\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u044b MACD \u0438 ADX \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442 \u043d\u0430 \u0432\u043e\u0441\u0445\u043e\u0434\u044f\u0449\u0438\u0439 \u0442\u0440\u0435\u043d\u0434, RSI \u0432\u044b\u0448\u0435 50.  \n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: \u043d\u0435\u0442  \n\u0412\u0430\u0436\u043d\u044b\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438:  \n1. MACD.macd = 0.575547  \n2. ADX = 22.9857  \n3. RSI = 56.4564  \n4. Stoch.K = 67.165  \n5. EMA5 \u2248 171.15","behavior":"degenerate_repetition","conversation_hash":"03ccafc05b7b1a969f9c45c9259b7cf8","conversation_redacted":false,"conversation_toxic":false,"country":"China","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":3590,"n_turns_in_conversation":1,"n_words":274,"source_row_offset":9009,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:16:21 GMT","turn_id":"be7be9c20bca723c99b93565","turn_index":0,"user_text":"\u041f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0439 SOLUSDT (1h) \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e \u043a\u0440\u0430\u0442\u043a\u043e.\n\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 \u0442\u043e\u043b\u044c\u043a\u043e \u044d\u0442\u0438 \u0434\u0430\u043d\u043d\u044b\u0435:\nSummary: {'RECOMMENDATION': 'BUY', 'BUY': 15, 'SELL': 2, 'NEUTRAL': 9}\nIndicators: {'Recommend.Other': 0.09090909090909091, 'Recommend.All': 0.4454545454545455, 'Recommend.MA': 0.8, 'RSI': 56.45642916315914, 'RSI[1]': 57.540981938000336, 'Stoch.K': 67.16500553709874, 'Stoch.D': 74.73318694944186, 'Stoch.K[1]': 75.24450584665232, 'Stoch.D[1]': 80.81184847061485, 'CCI20': 100.95953274928021, 'CCI20[1]': 118.29956608268003, 'ADX': 22.985700463874963, 'ADX+DI': 21.618820208666953, 'ADX-DI': 18.049209798176797, 'ADX+DI[1]': 22.038851194377415, 'ADX-DI[1]': 18.399887000246494, 'AO': 2.9383823529409767, 'AO[1]': 2.584382352940935, 'Mom': 3.0800000000000125, 'Mom[1]': 2.3499999999999943, 'MACD.macd': 0.5755471788009743, 'MACD.signal': 0.10290444261204443, 'Rec.Stoch.RSI': 0, 'Stoch.RSI.K': 65.06744261619384, 'Rec.WR': 0, 'W.R': -34.88372093023241, 'Rec.BBPower': 0, 'BBPower': 2.132275645529859, 'Rec.UO': 0, 'UO': 50.84283355750478, 'close': 171.15, 'EMA5': 171.1490084073107, 'SMA5': 171.5759999999999, 'EMA10': 170.5425399641871, 'SMA10': 170.33800000000014, 'EMA20': 169.82426205216498, 'SMA20': 169.25150000000025, 'EMA30': 169.77004835846896, 'SMA30': 168.1700000000001, 'EMA50': 170.00727281163827, 'SMA50': 170.5183999999999, 'EMA100': 169.43240575964055, 'SMA100': 170.62979999999985, 'EMA200': 166.18084693973668, 'SMA200': 166.17729999999986, 'Rec.Ichimoku': 0, 'Ichimoku.BLine': 166.185, 'Rec.VWMA': 1, 'VWMA': 169.02519934335575, 'Rec.HullMA9': -1, 'HullMA9': 172.00240740740742, 'Pivot.M.Classic.S3': 118.43333333333337, 'Pivot.M.Classic.S2': 139.57333333333335, 'Pivot.M.Classic.S1': 153.48666666666668, 'Pivot.M.Classic.Middle': 160.71333333333334, 'Pivot.M.Classic.R1': 174.62666666666667, 'Pivot.M.Classic.R2': 181.85333333333332, 'Pivot.M.Classic.R3': 202.9933333333333, 'Pivot.M.Fibonacci.S3': 139.57333333333335, 'Pivot.M.Fibonacci.S2': 147.64881333333335, 'Pivot.M.Fibonacci.S1': 152.63785333333334, 'Pivot.M.Fibonacci.Middle': 160.71333333333334, 'Pivot.M.Fibonacci.R1': 168.78881333333334, 'Pivot.M.Fibonacci.R2': 173.77785333333333, 'Pivot.M.Fibonacci.R3': 181.85333333333332, 'Pivot.M.Camarilla.S3': 161.5865, 'Pivot.M.Camarilla.S2': 163.52433333333335, 'Pivot.M.Camarilla.S1': 165.46216666666666, 'Pivot.M.Camarilla.Middle': 160.71333333333334, 'Pivot.M.Camarilla.R1': 169.33783333333335, 'Pivot.M.Camarilla.R2': 171.27566666666667, 'Pivot.M.Camarilla.R3': 173.2135, 'Pivot.M.Woodie.S3': 135.69, 'Pivot.M.Woodie.S2': 141.245, 'Pivot.M.Woodie.S1': 156.82999999999998, 'Pivot.M.Woodie.Middle': 162.385, 'Pivot.M.Woodie.R1': 177.96999999999997, 'Pivot.M.Woodie.R2': 183.52499999999998, 'Pivot.M.Woodie.R3': 199.10999999999996, 'Pivot.M.Demark.S1': 157.10000000000002, 'Pivot.M.Demark.Middle': 162.52, 'Pivot.M.Demark.R1': 178.24, 'open': 171.37, 'P.SAR': 170.77448557014708, 'BB.lower': 166.02404109248187, 'BB.upper': 172.47895890751863, 'AO[2]': 2.1516764705880576, 'volume': 17701.834, 'change': -0.12837719554180943, 'low': 171.06, 'high': 171.53}\n\n\u041e\u0442\u0432\u0435\u0442\u044c \u0441\u0442\u0440\u043e\u0433\u043e \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435:\n\u0421\u0418\u0413\u041d\u0410\u041b: [LONG/SHORT/NEUTRAL]\n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: [1-2 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u0438\u043d\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u0430]\n\u0423\u0420\u041e\u0412\u041d\u0418: [\u0432\u0445\u043e\u0434/\u0441\u0442\u043e\u043f/\u0446\u0435\u043b\u044c]\n\u0420\u0418\u0421\u041a: [\u041d\u0418\u0417\u041a\u0418\u0419/\u0421\u0420\u0415\u0414\u041d\u0418\u0419/\u0412\u042b\u0421\u041e\u041a\u0418\u0419]\n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: [\u043a\u0440\u0430\u0442\u043a\u043e]\n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: [\u0435\u0441\u0442\u044c/\u043d\u0435\u0442]\n3-5 \u0432\u0430\u0436\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439"},{"assistant_initiates":false,"asst_text":"\u0418\u0422\u041e\u0413: LONG  \n\u0422\u0410\u0419\u041c\u0424\u0420\u0415\u0419\u041c\u042b: 15m, 30m, 1h, 4h  \n\u041a\u041b\u042e\u0427\u0415\u0412\u042b\u0415 \u0423\u0420\u041e\u0412\u041d\u0418: \u0412\u0445\u043e\u0434 171.15 / \u0421\u0442\u043e\u043f 169.00 / \u0426\u0435\u043b\u044c 174.50  \n\u0420\u0418\u0421\u041a: \u0421\u0420\u0415\u0414\u041d\u0418\u0419  \n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: \u0412\u0441\u0435 \u0442\u0430\u0439\u043c\u0444\u0440\u0435\u0439\u043c\u044b \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442 \u043d\u0430 \u0441\u0438\u0433\u043d\u0430\u043b LONG \u0441 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u0438\u043d\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a MACD, ADX \u0438 RSI, \u0447\u0442\u043e \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0430\u0435\u0442 \u0432\u043e\u0441\u0445\u043e\u0434\u044f\u0449\u0438\u0439 \u0442\u0440\u0435\u043d\u0434. \u0422\u0435\u043a\u0443\u0449\u0430\u044f \u0446\u0435\u043d\u0430 \u0432\u044b\u0448\u0435 EMA5 \u0438 SMA5 \u043d\u0430 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0442\u0430\u0439\u043c\u0444\u0440\u0435\u0439\u043c\u0430\u0445.  \n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: \u041d\u0435\u0442  \n3-5 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u044f: MACD, ADX, RSI, EMA5, \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u0432\u0445\u043e\u0434\u0430 \u0438 \u0441\u0442\u043e\u043f\u0430  \n\u0421\u0443\u043c\u043c\u0430\u0440\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437: \u0412\u0441\u0435 \u0442\u0430\u0439\u043c\u0444\u0440\u0435\u0439\u043c\u044b \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442 \u0441\u043e\u0433\u043b\u0430\u0441\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0441\u0438\u0433\u043d\u0430\u043b LONG \u0441 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u0438\u043d\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432, \u0447\u0442\u043e \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043d\u0430 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u044b\u0439 \u0432\u043e\u0441\u0445\u043e\u0434\u044f\u0449\u0438\u0439 \u0442\u0440\u0435\u043d\u0434, \u043f\u0440\u0438 \u044d\u0442\u043e\u043c \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u0440\u0438\u0441\u043a\u0430 \u043e\u0446\u0435\u043d\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u0441\u0440\u0435\u0434\u043d\u0438\u0439.","behavior":"degenerate_repetition","conversation_hash":"a95b60881ea16da6599c8af83e594720","conversation_redacted":false,"conversation_toxic":false,"country":"Bulgaria","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":2316,"n_turns_in_conversation":1,"n_words":321,"source_row_offset":9010,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:16:33 GMT","turn_id":"8cc7f6e210dd80fcad19f4a7","turn_index":0,"user_text":"\u041d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0430\u043d\u0430\u043b\u0438\u0437\u043e\u0432 \u043f\u043e \u0442\u0430\u0439\u043c\u0444\u0440\u0435\u0439\u043c\u0430\u043c \u0441\u0434\u0435\u043b\u0430\u0439 \u0438\u0442\u043e\u0433\u043e\u0432\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437 SOLUSDT.\n\u0410\u043d\u0430\u043b\u0438\u0437\u044b \u043f\u043e \u0442\u0430\u0439\u043c\u0444\u0440\u0435\u0439\u043c\u0430\u043c:\n15m:\n\u0421\u0418\u0413\u041d\u0410\u041b: LONG  \n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: BUY \u0441\u0438\u0433\u043d\u0430\u043b (11) \u0438 RSI \u043e\u043a\u043e\u043b\u043e 51.5  \n\u0423\u0420\u041e\u0412\u041d\u0418: \u0412\u0445\u043e\u0434: 171.15 / \u0421\u0442\u043e\u043f: 170.25 / \u0426\u0435\u043b\u044c: 175.29  \n\u0420\u0418\u0421\u041a: \u0421\u0420\u0415\u0414\u041d\u0418\u0419  \n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: \u0422\u0435\u043a\u0443\u0449\u0430\u044f \u0446\u0435\u043d\u0430 \u0432\u044b\u0448\u0435 EMA5 \u0438 SMA5, \u0447\u0442\u043e \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043d\u0430 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0439 \u0440\u043e\u0441\u0442.  \n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: \u043d\u0435\u0442  \n3-5 \u0432\u0430\u0436\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439: RSI, BUY \u0441\u0438\u0433\u043d\u0430\u043b, \u0443\u0440\u043e\u0432\u043d\u0438 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0438 \u0438 \u0441\u043e\u043f\u0440\u043e\u0442\u0438\u0432\u043b\u0435\u043d\u0438\u044f.\n\n1h:\n\u0421\u0418\u0413\u041d\u0410\u041b: LONG  \n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: MACD, ADX  \n\u0423\u0420\u041e\u0412\u041d\u0418: \u0432\u0445\u043e\u0434 171.00 / \u0441\u0442\u043e\u043f 169.00 / \u0446\u0435\u043b\u044c 174.50  \n\u0420\u0418\u0421\u041a: \u0421\u0420\u0415\u0414\u041d\u0418\u0419  \n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: \u0418\u043d\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u044b MACD \u0438 ADX \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442 \u043d\u0430 \u0432\u043e\u0441\u0445\u043e\u0434\u044f\u0449\u0438\u0439 \u0442\u0440\u0435\u043d\u0434, RSI \u0432\u044b\u0448\u0435 50.  \n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: \u043d\u0435\u0442  \n\u0412\u0430\u0436\u043d\u044b\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438:  \n1. MACD.macd = 0.575547  \n2. ADX = 22.9857  \n3. RSI = 56.4564  \n4. Stoch.K = 67.165  \n5. EMA5 \u2248 171.15\n\n4h:\n\u0421\u0418\u0413\u041d\u0410\u041b: LONG  \n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: \u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u044f \"BUY\", RSI \u0432\u044b\u0448\u0435 50  \n\u0423\u0420\u041e\u0412\u041d\u0418: \u0432\u0445\u043e\u0434 171.15 / \u0441\u0442\u043e\u043f 169.00 / \u0446\u0435\u043b\u044c 174.63  \n\u0420\u0418\u0421\u041a: \u0421\u0420\u0415\u0414\u041d\u0418\u0419  \n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: \u0422\u0435\u043a\u0443\u0449\u0430\u044f \u0446\u0435\u043d\u0430 \u0432\u044b\u0448\u0435 EMA5 \u0438 SMA5, \u0447\u0442\u043e \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043d\u0430 \u0432\u043e\u0441\u0445\u043e\u0434\u044f\u0449\u0438\u0439 \u0442\u0440\u0435\u043d\u0434.  \n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: \u043d\u0435\u0442  \n\u0412\u0430\u0436\u043d\u044b\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438: RSI 54.47, MACD 0.62, ADX 18.07, Stoch.K 56.06, BB.upper 178.64\n\n30m:\n\u0421\u0418\u0413\u041d\u0410\u041b: \u041a\u0423\u041f\u0418\u0422\u042c\n\u041f\u0420\u0418\u0427\u0418\u041d\u0410: \u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u044f \u043f\u043b\u044e\u0441 \u0432\u0435\u0440\u0445\u043d\u044f\u044f \u0433\u0440\u0430\u043d\u0438\u0446\u0430 \u0411\u0411-\u0431\u0430\u043d\u0434\u044b\n\u0423\u0420\u041e\u0412\u041d\u0418: \u0412\u0445\u043e\u0434 \u043d\u0430 \u0440\u044b\u043d\u043e\u043a \u043d\u0430 \u0443\u0440\u043e\u0432\u043d\u0435 171.37 \u0441 \u0446\u0435\u043b\u044c\u044e 173.39, \u0441\u0442\u043e\u043f-\u043e\u0440\u0434inery \u043d\u0430 167.13\n\u0420\u0418\u0421\u041a: \u0421\u0420\u0415\u0414\u041d\u0418\u0419\n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: \u041f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u0430\u044f \u043f\u043e\u043a\u0443\u043f\u043a\u0430 SOLUSDT \u043d\u0430 \u0433\u0440\u0430\u043d\u0438\u0447\u043d\u044b\u0435 \u0443\u0440\u043e\u0432\u043d\u0438 \u0411\u0411-\u0431\u0430\u043d\u0434\u044b \u0441 \u0445\u043e\u0440\u043e\u0448\u0438\u043c \u0440\u0438\u0441\u043a/\u0432\u043e\u0437\u0432\u0440\u0430\u0442 \u0441\u043e\u043e\u0442\u043d\u043e\u0448\u0435\u043d\u0438\u0435\u043c.\n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: \u041d\u0435\u0442\n\u0412\u0430\u0436\u043d\u044b\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438: \u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u044f, \u0432\u0435\u0440\u0445\u043d\u044f\u044f \u0433\u0440\u0430\u043d\u0438\u0446\u0430 \u0411\u0411-\u0431\u0430\u043d\u0434\u044b, \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u0432\u0445\u043e\u0434\u0430, \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u0446\u0435\u043b\u0438, \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u0441\u0442\u043e\u043f-\u043e\u0440\u0434inery, \u0441\u0440\u0435\u0434\u043d\u0438\u0439 \u0440\u0438\u0441\u043a\n\n\n\u0414\u0430\u0439 \u043a\u0440\u0430\u0442\u043a\u0438\u0439 \u043e\u0442\u0432\u0435\u0442 \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435:\n\n\u0418\u0422\u041e\u0413: [LONG/SHORT/NEUTRAL]\n\u0422\u0410\u0419\u041c\u0424\u0420\u0415\u0419\u041c\u042b: [\u0441\u043e\u0432\u043f\u0430\u0434\u0430\u044e\u0449\u0438\u0435 \u0441\u0438\u0433\u043d\u0430\u043b\u044b]\n\u041a\u041b\u042e\u0427\u0415\u0412\u042b\u0415 \u0423\u0420\u041e\u0412\u041d\u0418: [\u0432\u0445\u043e\u0434/\u0441\u0442\u043e\u043f/\u0446\u0435\u043b\u044c]\n\u0420\u0418\u0421\u041a: [\u041d\u0418\u0417\u041a\u0418\u0419/\u0421\u0420\u0415\u0414\u041d\u0418\u0419/\u0412\u042b\u0421\u041e\u041a\u0418\u0419]\n\u041e\u0411\u041e\u0421\u041d\u041e\u0412\u0410\u041d\u0418\u0415: [\u043e\u0441\u043d\u043e\u0432\u043d\u044b\u0435 \u043f\u0440\u0438\u0447\u0438\u043d\u044b]\n\u0410\u041d\u041e\u041c\u0410\u041b\u0418\u0418: [\u0435\u0441\u0442\u044c/\u043d\u0435\u0442]\n3-5 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439\u0421\u0443\u043c\u043c\u0430\u0440\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437: [\u0438\u0442\u043e\u0433\u043e\u0432\u044b\u0439 \u0432\u044b\u0432\u043e\u0434]"},{"assistant_initiates":false,"asst_text":"```json\n{\n    \"relevant_tasks\": [\n        {\n            \"task\": \"Risk Classification\",\n            \"examples\": [\n                {\n                    \"input\": \"The paragraph discusses that commercial and industrial loans involve greater risks than other types of lending due to their dependency on the business climate.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The text suggests that commercial loans are less risky than residential loans.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Sentiment Classification\",\n            \"examples\": [\n                {\n                    \"input\": \"The client's tone is cautious as they express concern over the potential negative impacts of economic conditions on commercial loans.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The paragraph conveys a feeling of optimism regarding the state of commercial loans in the current economic environment.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Tense Identification\",\n            \"examples\": [\n                {\n                    \"input\": \"The statement identifies current and potential future risks associated with commercial and industrial loans.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The text examines only past issues related to commercial and industrial loans.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        },\n        {\n            \"task\": \"Impact Classification\",\n            \"examples\": [\n                {\n                    \"input\": \"Economic challenges and cash flow issues from businesses could have a material adverse effect on the financial condition described in the text.\",\n                    \"label\": \"entailment\"\n                },\n                {\n                    \"input\": \"The text implies that economic challenges will have no significant effect on the financial condition of the company.\",\n                    \"label\": \"non-entailment\"\n                }\n            ]\n        }\n    ]\n}\n```","behavior":"degenerate_repetition","conversation_hash":"e74c81fe3e1bf989b0a4b42f4bc4d352","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":6090,"n_turns_in_conversation":1,"n_words":755,"source_row_offset":9011,"source_shard":"turns-00040.parquet","timestamp":"Sat, 26 Oct 2024 23:16:36 GMT","turn_id":"5eab925bb4f867fc280e7336","turn_index":0,"user_text":"System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). \n\nStep 1: Read the paragraph carefully. \nStep 2: Automatically decide which tasks are relevant based on the paragraph\u2019s content. Relevant tasks may include, but are not limited to: \n    - Topic Classification \n    - Risk Classification\n    - Sentiment Classification \n    - Tone Classification\n    - Event Detection \n    - Entity Recognition \n    - Geographical Recognition \n    - Trend Detection \n    - Intent Detection \n    - Sector Identification \n    - Impact Classification \n    - Tense Identification (Past, Present, Future) \n    - Uncertainty Identification \n\nStep 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. \nStep 4: Output the results in the following JSON format:\n\n{\n    'relevant_tasks': [\n        {\n            'task': <The automatically selected relevant task>,\n            'examples': [\n                {\n                    'input': <The entailment example based on the paragraph>,\n                    'label': 'entailment',\n                },\n                {\n                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,\n                    'label': 'non-entailment',\n                }\n            ]\n        },\n        ...\n    ]\n}\nUser: Commercial and industrial loans represented 23.9% of our total loan portfolio at December 31, 2022. These loans are often larger and involve greater risks than other types of lending. Because payments on such loans are often dependent on the successful operation of the business involved, repayment of such loans is often more sensitive than other types of loans to the general business climate and economy. Accordingly, a challenging business and economic environment may increase our risk related to commercial loans. In the current economic environment, the cumulative effects of rising inflation, labor shortages and supply chain constraints and the threat of a recession may adversely affect commercial and industrial loans, especially if general economic conditions worsen. Unlike residential mortgage loans, which generally are made on the basis of the borrowers\u2019 ability to make repayment from their employment and other income and which are secured by real property whose value tends to be more easily ascertainable, commercial loans typically are made on the basis of the borrowers\u2019 ability to make repayment from the cash flow of the commercial venture. Our commercial and industrial loans are primarily made based on the identified cash flow of the borrower and secondarily on the collateral underlying the loans. Most often, this collateral consists of accounts receivable, inventory and equipment. Inventory and equipment may depreciate over time, may be difficult to appraise and may fluctuate in value based on the success of the business. If the cash flow from business operations is reduced, the borrower\u2019s ability to repay the loan may be impaired. Due to the larger average size of each commercial loan as compared with other loans such as residential loans, as well as collateral that is generally less readily-marketable, losses incurred on a small number of commercial loans could have a material adverse effect on our business, financial condition, results of operations and growth prospects.\nAssistant:"}],"shard":{"index":40,"name":"turns-00040.parquet","rows":45683}}