Scheduled Commit

data/retrieval_battle-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -10,3 +10,4 @@
 {"tstamp": 1726138211.604, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "4dfe5896af204393a6108869d3472387", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Was ist emergenz", "0_output": [["Was ist emergenz", "Title: Exceptional points-based optical amplifiers\n\nAbstract: The finite gain-bandwidth product is a fundamental figure of merit that restricts the operation of standard optical amplifiers. In microcavity setups, this becomes a serious problem due to the narrow bandwidth of the device. Here we introduce a new design paradigm based on exceptional points, that relaxes this limitation and allows for building a new generation of optical amplifiers that exhibits better gain-bandwidth scaling relations. Importantly, our results can be extended to other physical systems such as acoustics and microwaves."]], "0_corpus": "arxiv", "1_conv_id": "3ccce0468494419b8d56cfe99768eaf4", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Was ist emergenz", "1_output": [["Was ist emergenz", "Title: Die Erste Stunde (The first hour)\n\nAbstract: I recall my \"first hour\" events following on my meeting in Fall 1968 in the classroom with my academic teacher and thesis mentor Prof. Dr. Dr. h.c. multiple Walter Greiner. My comments focus on the creation of the new \"strong fields\" domain of physics in Frankfurt. I argue that this was the research field closest to Walter's heart during his lustrous academic career. I will describe the events that lead on to Greiner's course books, Walters actions leading to the rise of Frankfurt School of Theoretical Physics, and show how a stability principle defined his science."]], "1_corpus": "arxiv"}
 {"tstamp": 1726146768.5961, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "957000400105491d997d03b0cbfb5264", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "convert dataframe into json in python", "0_output": [["convert dataframe into json in python", "Stackoverflow Stackexchange\n\nQ: dataframe to json file I want to convert dataframe to json file. Currently my dataframe looks like this:\na           b \nP1          7950\nP2          1274\nP3          6160\n\nI want to conver this dataframe into json file in which values from first column would be keys and from second column would be values: so like {P1:7950, P2:1274, P3:6160) \nI tried this but it produces output in incorrect form (not the want I stated above)\ndf.set_index('a').to_json()\n\n\nA: First create Series with set_index and then Series.to_json:\nj = df.set_index('a')['b'].to_json()\nprint (j)\n{\"P1\":7950,\"P2\":1274,\"P3\":6160}\n\nFor file:\ndf.set_index('a')['b'].to_json(filename)\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "f8cfc64502a94500b20d73bc69c4ddb8", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "convert dataframe into json in python", "1_output": [["convert dataframe into json in python", "Stackoverflow Stackexchange\n\nQ: dataframe to json file I want to convert dataframe to json file. Currently my dataframe looks like this:\na           b \nP1          7950\nP2          1274\nP3          6160\n\nI want to conver this dataframe into json file in which values from first column would be keys and from second column would be values: so like {P1:7950, P2:1274, P3:6160) \nI tried this but it produces output in incorrect form (not the want I stated above)\ndf.set_index('a').to_json()\n\n\nA: First create Series with set_index and then Series.to_json:\nj = df.set_index('a')['b'].to_json()\nprint (j)\n{\"P1\":7950,\"P2\":1274,\"P3\":6160}\n\nFor file:\ndf.set_index('a')['b'].to_json(filename)\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1726158606.4146, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "16a35f139a9144c8ba84aff7158792db", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "In English when to use –, — vs -?", "0_output": [["In English when to use –, — vs -?", "English Stackexchange\n\nQ: When to use -, – and —? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (–) and em-dashes (—).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London–Amsterdam flight is delayed.\n\n*The meeting is from 3:00–4:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire—it had been awake half the night—flew out of its coffin.\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "c30a4872bc4949b9922751e5d28f8b52", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "In English when to use –, — vs -?", "1_output": [["In English when to use –, — vs -?", "English Stackexchange\n\nQ: What is \":-\" used for? \nPossible Duplicate:\nHenry Adam’s use of punctuation, “:—” \n\nI've seen a couple of times, \":-\" occurring where I could expect just a colon to occur, perhaps preceding a list.\nWhen is it correct to use \":-\"? \nWhat is it called?\nAm I simply wrong and think I remember seeing this, when in actual fact I didn't?\n\nA: This is an old usage, now obsolete. Graves and Hodge (The Reader Over Your Shoulder, 1943) describe it thus:\n\n A long dash may be put after a colon, for emphasis. For example:  \n\n\n  ‘The Captain arose and said: “Come, Antonio, amuse the men, and tell them one of your favourite stories!” Antonio arose, rolled the quid from side to side in his coarse mouth and, after a pause, began thus:—\n        “About  the year 1874, in Lisbon . . . ”’\n\n\n\nNote that the colon-dash construction is distinct from the internal colon.\nOED 1 employs  :— in etymologies to signify an “extant representative, or regular phonetic descendant of”. According to tchrist, OED 2 and OED 3 employ it similarly to signify “normal development of”.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1726168146.4298, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "28485453fe8f42c095021f6848832ecb", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "abc1df66e88b45f7b6f922f6ccba746b", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}

data/retrieval_individual-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -40,3 +40,7 @@
 {"tstamp": 1726146824.7071, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1726146822.5378, "finish": 1726146824.7071, "ip": "", "conv_id": "7daa78f3d46c4e7999ed0cda3df11ef0", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Indian Hero from bollywood", "output": [["Indian Hero from bollywood", "Bollywood Hero\n\nBollywood Hero is an American musical comedy television miniseries that aired on the IFC in three parts on August 6–8, 2009. The miniseries were shot in Mumbai and Los Angeles.\nThe show is a musical miniseries about Chris Kattan and his journey to Mumbai to become a leading man in Bollywood. After having some successful runs as a comedian in America, Kattan wishes for more and soon discovers that making it in Bollywood is just as hard and he has to start from scratch.\nDance numbers are choreographed by Longinus Fernandes who is known for the \"Jai Ho\" finale in Slumdog Millionaire.\nCharacters\nMajor characters\nCameos\nGaurav Ghatnekar as Hipster Partygoer\nMaya Rudolph as herself\nKeanu Reeves as himself\nDavid Alan Grier as himself\nLonginus Fernandes as himself\nInternational Broadcasting"]], "corpus": "wikipedia"}
 {"tstamp": 1726158541.4903, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1726158539.213, "finish": 1726158541.4903, "ip": "", "conv_id": "16a35f139a9144c8ba84aff7158792db", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "In English when to use –, — vs -?", "output": [["In English when to use –, — vs -?", "English Stackexchange\n\nQ: When to use -, – and —? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (–) and em-dashes (—).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London–Amsterdam flight is delayed.\n\n*The meeting is from 3:00–4:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire—it had been awake half the night—flew out of its coffin.\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1726158541.4903, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726158539.213, "finish": 1726158541.4903, "ip": "", "conv_id": "c30a4872bc4949b9922751e5d28f8b52", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "In English when to use –, — vs -?", "output": [["In English when to use –, — vs -?", "English Stackexchange\n\nQ: What is \":-\" used for? \nPossible Duplicate:\nHenry Adam’s use of punctuation, “:—” \n\nI've seen a couple of times, \":-\" occurring where I could expect just a colon to occur, perhaps preceding a list.\nWhen is it correct to use \":-\"? \nWhat is it called?\nAm I simply wrong and think I remember seeing this, when in actual fact I didn't?\n\nA: This is an old usage, now obsolete. Graves and Hodge (The Reader Over Your Shoulder, 1943) describe it thus:\n\n A long dash may be put after a colon, for emphasis. For example:  \n\n\n  ‘The Captain arose and said: “Come, Antonio, amuse the men, and tell them one of your favourite stories!” Antonio arose, rolled the quid from side to side in his coarse mouth and, after a pause, began thus:—\n        “About  the year 1874, in Lisbon . . . ”’\n\n\n\nNote that the colon-dash construction is distinct from the internal colon.\nOED 1 employs  :— in etymologies to signify an “extant representative, or regular phonetic descendant of”. According to tchrist, OED 2 and OED 3 employ it similarly to signify “normal development of”.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1726168142.6103, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1726168140.3766, "finish": 1726168142.6103, "ip": "", "conv_id": "28485453fe8f42c095021f6848832ecb", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
+{"tstamp": 1726168142.6103, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726168140.3766, "finish": 1726168142.6103, "ip": "", "conv_id": "abc1df66e88b45f7b6f922f6ccba746b", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
+{"tstamp": 1726168176.1092, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1726168175.8878, "finish": 1726168176.1092, "ip": "", "conv_id": "47e04715bb05444ea19c14501a18670a", "model_name": "GritLM/GritLM-7B", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
+{"tstamp": 1726168255.7032, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1726168255.4713, "finish": 1726168255.7032, "ip": "", "conv_id": "b7a6017828de46bfb6098e6f60afb86c", "model_name": "GritLM/GritLM-7B", "prompt": "I am searching for a good and large-scale benchmark for testing the performance of word embeddings.", "output": [["I am searching for a good and large-scale benchmark for testing the performance of word embeddings.", "Title: WordRep: A Benchmark for Research on Learning Word Representations\n\nAbstract: WordRep is a benchmark collection for the research on learning distributed word representations (or word embeddings), released by Microsoft Research. In this paper, we describe the details of the WordRep collection and show how to use it in different types of machine learning research related to word embedding. Specifically, we describe how the evaluation tasks in WordRep are selected, how the data are sampled, and how the evaluation tool is built. We then compare several state-of-the-art word representations on WordRep, report their evaluation performance, and make discussions on the results. After that, we discuss new potential research topics that can be supported by WordRep, in addition to algorithm comparison. We hope that this paper can help people gain deeper understanding of WordRep, and enable more interesting research on learning distributed word representations and related topics."]], "corpus": "arxiv"}

data/retrieval_single_choice-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -0,0 +1 @@
+{"tstamp": 1726168235.287, "task_type": "retrieval", "type": "downvote", "models": "GritLM/GritLM-7B", "ip": "", "conv_id": "47e04715bb05444ea19c14501a18670a", "model_name": "GritLM/GritLM-7B", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}