Scheduled Commit

data/retrieval_individual-89f7d27e-df7d-414d-a213-dcd1c0b2f2a2.jsonl

@@ -231,3 +231,9 @@
 {"tstamp": 1730295965.9479, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1730295963.7538, "finish": 1730295965.9479, "ip": "", "conv_id": "d94c748c74134476b473190e99e37d95", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why do the machines allow/initiate the building of a new Zion? In the Matrix Reloaded, the Architect says:\n\nThe function of the One is now to return to the Source, allowing a temporary dissemination of the code you carry, reinserting the prime program. After which, you will be required to select from the Matrix 23 individuals - 16 female, 7 male - to rebuild Zion. Failure to comply with this process will result in a cataclysmic system crash, killing everyone connected to the Matrix, which, coupled with the extermination of Zion, will ultimately result in the extinction of the entire human race.\n\nWhy do the machines initiate the re-building of Zion?\n\nA: As was discussed elsewhere on the site, the machines are not interested in humans being wiped out, they (as shown in Animatrix) just want to co-exist with humans.\nAs such, the machines allow the building of new Zion so that humans would not - as the quote you provided says - would not go extinct.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1730301679.2041, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1730301676.8383, "finish": 1730301679.2041, "ip": "", "conv_id": "739bfbd0043b45298bd97fe597a7a42b", "model_name": "embed-english-v3.0", "prompt": "כתוב סיפור בן 100 מילים או יותר על לטאה פחדנית, קיפוד ביישן ונמרה שחורה.", "output": [["כתוב סיפור בן 100 מילים או יותר על לטאה פחדנית, קיפוד ביישן ונמרה שחורה.", "100\n\nThe U.S. hundred-dollar bill has Benjamin Franklin's portrait; the \"Benjamin\" is the largest U.S. bill in print. American savings bonds of $100 have Thomas Jefferson's portrait, while American $100 treasury bonds have Andrew Jackson's portrait.\nIn sports\nThe number of yards in an American football field (not including the end zones).\nThe number of points required for a snooker player to score a century break, a significant milestone.\nThe record number of points scored in one NBA game by a single player, set by Wilt Chamberlain of the Philadelphia Warriors on March 2, 1962.\nThe 100-yard dash and the 100-metre dash are sprint track events.\nThe 100-kilometre walk is a racewalking event.\nThe Hundred (cricket), a 100-ball cricket competition.\nIn other fields\nOne hundred is also:\nThe number of years in a century.\nThe number of centimeters in a meter.\nThe number of pounds in an American short hundredweight.\nIn Greece, India, Israel and Nepal, 100 is the police telephone number.\nIn Belgium, 100 is the ambulance and firefighter telephone number.\nIn United Kingdom, 100 is the operator telephone number.\nThe HTTP status code indicating that the client should continue with its request.\nThe age at which a person becomes a centenarian."]], "corpus": "wikipedia"}
 {"tstamp": 1730301679.2041, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1730301676.8383, "finish": 1730301679.2041, "ip": "", "conv_id": "0e623ba438c04364bc16954edbd23065", "model_name": "text-embedding-004", "prompt": "כתוב סיפור בן 100 מילים או יותר על לטאה פחדנית, קיפוד ביישן ונמרה שחורה.", "output": [["כתוב סיפור בן 100 מילים או יותר על לטאה פחדנית, קיפוד ביישן ונמרה שחורה.", "Centenarian\n\nA centenarian is a person who has reached the age of 100 years. Because life expectancies worldwide are below 100, the term is invariably associated with longevity. The United Nations in 2012 estimated that there were 316,600 living centenarians worldwide.\nAs world population and life expectancy continue to increase, the number of centenarians is expected to increase substantially in the twenty-first century. According to the Office of National Statistics in the United Kingdom, one-third of babies born in the country in 2013 are expected to live to 100.\nThe United Nations estimates that currently there are 573,000 centenarians, almost quadruple the estimate of 151,000 made in 2000. According to a 1998 United Nations demographic survey, Japan is expected to have 272,000 centenarians by 2050; other sources suggest that the number could be closer to 1 million. The incidence of centenarians in Japan was one per 3,522 people in 2008.\nIn Japan, the number of centenarians is highly skewed towards females. Japan in fiscal year 2016 had 57,525 female centenarians, while there were 8,167 males, a ratio of 7:1. The increase of centenarians was even more skewed at 11.6:1."]], "corpus": "wikipedia"}
+{"tstamp": 1730303562.3395, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1730303560.0836, "finish": 1730303562.3395, "ip": "", "conv_id": "150f927f6e19497081f7b67faf5eb7ce", "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": 1730303562.3395, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1730303560.0836, "finish": 1730303562.3395, "ip": "", "conv_id": "5274d158109b47e1a3aa7a6552bdf944", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Artificial general intelligence\n\nMany interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider additional traits such as imagination (the ability to form novel mental images and concepts) and autonomy.\nComputer-based systems that exhibit many of these capabilities exist (e.g. see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent). However, no consensus holds that modern AI systems possess them to an adequate degree.\nPhysical traits\nOther capabilities are considered desirable in intelligent systems, as they may affect intelligence or aid in its expression. These include:\nthe ability to sense (e.g. see, hear, etc.), and\nthe ability to act (e.g. move and manipulate objects, change location to explore, etc.)\nThis includes the ability to detect and respond to hazard.\nTests for human-level AGI\nSeveral tests meant to confirm human-level AGI have been considered, including:\nThe Turing Test (Turing)\nA machine and a human both converse unseen with a second human, who must evaluate which of the two is the machine, which passes the test if it can fool the evaluator a significant fraction of the time. Note: Turing does not prescribe what should qualify as intelligence, only that knowing that it is a machine should disqualify it. The AI Eugene Goostman, imitating a 13-year-old boy, achieved Turing's estimate of convincing 33% of judges that it was human in 2014.\nThe Robot College Student Test (Goertzel)"]], "corpus": "wikipedia"}
+{"tstamp": 1730303633.0776, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1730303630.8121, "finish": 1730303633.0776, "ip": "", "conv_id": "3d7965628be34e5ba12c315793b4b1c2", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Please find me the paper on training code large language models to follow instructions via git commits.", "output": [["Please find me the paper on training code large language models to follow instructions via git commits.", "Title: OctoPack: Instruction Tuning Code Large Language Models\n\nAbstract: Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack."]], "corpus": "arxiv"}
+{"tstamp": 1730303633.0776, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1730303630.8121, "finish": 1730303633.0776, "ip": "", "conv_id": "7c96c2e3fb3c4a178c622a8825d56455", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Please find me the paper on training code large language models to follow instructions via git commits.", "output": [["Please find me the paper on training code large language models to follow instructions via git commits.", "Title: OctoPack: Instruction Tuning Code Large Language Models\n\nAbstract: Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack."]], "corpus": "arxiv"}
+{"tstamp": 1730303668.1166, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1730303667.8171, "finish": 1730303668.1166, "ip": "", "conv_id": "c1d522542fb04e50b23cb905a9b2501e", "model_name": "Alibaba-NLP/gte-Qwen2-7B-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": 1730303668.1166, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1730303667.8171, "finish": 1730303668.1166, "ip": "", "conv_id": "a141a0cb2a884c289b6a0247cd964329", "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"}

data/retrieval_side_by_side-89f7d27e-df7d-414d-a213-dcd1c0b2f2a2.jsonl

@@ -7,3 +7,4 @@
 {"tstamp": 1730027130.5741, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "jinaai/jina-embeddings-v2-base-en"], "ip": "", "0_conv_id": "a2dd7012038d4296a5faaea0efa36675", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "which artist painted scream", "0_output": [["which artist painted scream", "The Scream\n\nThe Scream is a composition created by Norwegian artist Edvard Munch in 1893. The Norwegian name of the piece is  (Scream), and the German title under which it was first exhibited is   (The Scream of Nature). The agonized face in the painting has become one of the most iconic images in art, seen as symbolizing the anxiety of the human condition. Munch's work, including The Scream, had a formative influence on the Expressionist movement.\nMunch recalled that he had been out for a walk at sunset when suddenly the setting sun's light turned the clouds \"a blood red\". He sensed an \"infinite scream passing through nature\". Scholars have located the spot to a fjord overlooking Oslo and have suggested other explanations for the unnaturally orange sky, ranging from the effects of a volcanic eruption to a psychological reaction by Munch to his sister's commitment at a nearby lunatic asylum.\nMunch created two versions in paint and two in pastels, as well as a lithograph stone from which several prints survive. Both painted versions have been stolen, but since recovered. In 2012, one of the pastel versions commanded the at-the-time highest nominal price paid for an artwork at a public auction."]], "0_corpus": "wikipedia", "1_conv_id": "da15cb1b779a480b8a8fb72450d6a35d", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "which artist painted scream", "1_output": [["which artist painted scream", "The Scream\n\nThe Scream is a composition created by Norwegian artist Edvard Munch in 1893. The Norwegian name of the piece is  (Scream), and the German title under which it was first exhibited is   (The Scream of Nature). The agonized face in the painting has become one of the most iconic images in art, seen as symbolizing the anxiety of the human condition. Munch's work, including The Scream, had a formative influence on the Expressionist movement.\nMunch recalled that he had been out for a walk at sunset when suddenly the setting sun's light turned the clouds \"a blood red\". He sensed an \"infinite scream passing through nature\". Scholars have located the spot to a fjord overlooking Oslo and have suggested other explanations for the unnaturally orange sky, ranging from the effects of a volcanic eruption to a psychological reaction by Munch to his sister's commitment at a nearby lunatic asylum.\nMunch created two versions in paint and two in pastels, as well as a lithograph stone from which several prints survive. Both painted versions have been stolen, but since recovered. In 2012, one of the pastel versions commanded the at-the-time highest nominal price paid for an artwork at a public auction."]], "1_corpus": "wikipedia"}
 {"tstamp": 1730027238.0937, "task_type": "retrieval", "type": "leftvote", "models": ["GritLM/GritLM-7B", "jinaai/jina-embeddings-v2-base-en"], "ip": "", "0_conv_id": "33bed33c47f8451f9be5c2a917ca5ae9", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "where is meridian greenwich", "0_output": [["where is meridian greenwich", "Prime meridian (Greenwich)\n\nThe Greenwich meridian is a prime meridian, a geographical reference line that passes through the Royal Observatory, Greenwich, in London, England. From 1884 to 1974, the Greenwich meridian was the  international standard prime meridian, used worldwide for timekeeping and navigation. The modern standard, the IERS Reference Meridian, is based on the Greenwich meridian, but differs slightly from it. This prime meridian (at the time, one of many) was first established by Sir George Airy in 1851, and by 1884, over two-thirds of all ships and tonnage used it as the reference meridian on their charts and maps. In October of that year, at the invitation of the President of the United States, 41 delegates from 25 nations met in Washington, D.C., United States, for the International Meridian Conference.  This conference selected the meridian passing through Greenwich as the world standard prime meridian due to its popularity. However, France abstained from the vote, and French maps continued to use the Paris meridian for several decades. In the 18th century, London lexicographer Malachy Postlethwayt published his African maps showing the \"Meridian of London\" intersecting the Equator a few degrees west of the later meridian and Accra, Ghana.\nThe plane of the prime meridian is parallel to the local gravity vector at the Airy transit circle () of the Greenwich observatory. The prime meridian was therefore long symbolised by a brass strip in the courtyard, now replaced by stainless steel, and since 16 December 1999, it has been marked by a powerful green laser shining north across the London night sky."]], "0_corpus": "wikipedia", "1_conv_id": "8b0dc60032694d86a21012ab95cf1884", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "where is meridian greenwich", "1_output": [["where is meridian greenwich", "Greenwich\n\nGreenwich ( ,  ,  ) is a town in south-east London, England, within the ceremonial county of Greater London. It is situated  east-south-east of Charing Cross."]], "1_corpus": "wikipedia"}
 {"tstamp": 1730056471.4156, "task_type": "retrieval", "type": "bothbadvote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "intfloat/multilingual-e5-large-instruct"], "ip": "", "0_conv_id": "e93fb276b9914a41b7d150785f08cc2a", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "من هو عبدالعزيز السعود", "0_output": [["من هو عبدالعزيز السعود", "Abdulaziz bin Fahd Al Saud\n\nAbdulaziz bin Fahd Al Saud ( ʿAbd al-ʿAzīz bin Fahd Āl Suʿūd) is a Saudi prince, being a son of King Fahd.\nEarly life and education\nHis mother is Al Jawhara bint Ibrahim Al Ibrahim, belonging to the wealthy Al Ibrahim family.\nAbdulaziz bin Fahd received a bachelor of arts degree in administrative sciences from King Saud University.\nProfessional experience\nPrince Abdulaziz was first appointed as minister of state without portfolio in May 1998. Then, he was made head of the Office of the Council of Ministers in January 2000, when he was 28 years old. It was reported that after King Fahd's death, he began to live in Switzerland and came to Saudi Arabia to participate in the meetings of the Council of Ministers.\nOn 26 June 2011, he was relieved from his position as head of the court of Cabinet affairs by a royal decree. It was declared that he resigned from his posts of minister of State and member of the Council of Ministers at his own request in June 2011."]], "0_corpus": "wikipedia", "1_conv_id": "ada2db1a4f754ec8a8c139c34f45f741", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "من هو عبدالعزيز السعود", "1_output": [["من هو عبدالعزيز السعود", "Faisal bin Musaid Al Saud\n\nFaisal bin Musaid Al Saud (, Fayṣal bin Musāʿid ʾĀl Suʿūd; 4 April 194418 June 1975) was the assassin and nephew of King Faisal of Saudi Arabia."]], "1_corpus": "wikipedia"}
+{"tstamp": 1730303653.3919, "task_type": "retrieval", "type": "tievote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "intfloat/e5-mistral-7b-instruct"], "ip": "", "0_conv_id": "3d7965628be34e5ba12c315793b4b1c2", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Please find me the paper on training code large language models to follow instructions via git commits.", "0_output": [["Please find me the paper on training code large language models to follow instructions via git commits.", "Title: OctoPack: Instruction Tuning Code Large Language Models\n\nAbstract: Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack."]], "0_corpus": "arxiv", "1_conv_id": "7c96c2e3fb3c4a178c622a8825d56455", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Please find me the paper on training code large language models to follow instructions via git commits.", "1_output": [["Please find me the paper on training code large language models to follow instructions via git commits.", "Title: OctoPack: Instruction Tuning Code Large Language Models\n\nAbstract: Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack."]], "1_corpus": "arxiv"}