Scheduled Commit

data/retrieval_battle-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

@@ -80,3 +80,4 @@
 {"tstamp": 1722435682.4732, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "e3e0803a5ed3495e879fbdcf95b60e6b", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "0_corpus": "wikipedia", "1_conv_id": "6b0f0b1750eb4bb5b4313281fda7164d", "1_model_name": "voyage-multilingual-2", "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 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"]], "1_corpus": "wikipedia"}
 {"tstamp": 1722435844.6878, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "02c643933a4f421fbf9566730bc39465", "0_model_name": "voyage-multilingual-2", "0_prompt": "Orca life expectancy", "0_output": [["Orca life expectancy", "Orca\n\nLife cycle Female orcas begin to mature at around the age of 10 and reach peak fertility around 20, experiencing periods of polyestrous cycling separated by non-cycling periods of three to 16 months. Females can often breed until age 40, followed by a rapid decrease in fertility. Orcas are among the few animals that undergo menopause and live for decades after they have finished breeding. The lifespans of wild females average 50 to 80 years. Some are claimed to have lived substantially longer: Granny (J2) was estimated by some researchers to have been as old as 105 years at the time of her death, though a biopsy sample indicated her age as 65 to 80 years. It is thought that orcas held in captivity tend to have shorter lives than those in the wild, although this is subject to scientific debate.\nMales mate with females from other pods, which prevents inbreeding. Gestation varies from 15 to 18 months. Mothers usually calve a single offspring about once every five years. In resident pods, births occur at any time of year, although winter is the most common. Mortality is extremely high during the first seven months of life, when 37–50% of all calves die. Weaning begins at about 12 months of age, and is complete by two years. According to observations in several regions, all male and female pod members participate in the care of the young."]], "0_corpus": "wikipedia", "1_conv_id": "b3487cab0720415f8e58696097136d9c", "1_model_name": "embed-english-v3.0", "1_prompt": "Orca life expectancy", "1_output": [["Orca life expectancy", "Captive orcas\n\nDisease and lifespan The lifespan of killer whales in captivity versus wild killer whales is disputed.  Several studies published in scientific journals show that the average mortality rate for captive killer whales is approximately three times higher than in the wild. A 2015 study in the Journal of Mammalogy, authored by SeaWorld's vice-president of theriogenology, Todd Robeck, concluded that the life expectancy for killer whales born at SeaWorld is the same as those in the wild. In the wild, female killer whales have a typical lifespan of 60–80 years, and a maximum recorded lifespan of 103 years.  The average lifespan for males in the wild is 30 years, but some live up to 50–60 years. The 2015 study has been criticised by Trevor Willis, senior lecturer in marine biology at the University of Portsmouth, who stated that the study is misleading, \"clearly wrong\" and indicative of \"poor practice\".  He stated that it is misleading in two ways: \"First, it compares two completely different circumstances: the controlled environment of a swimming pool, with highly trained vets on hand; and the wild ocean. \"There are no predators in a swimming pool. Second, and in the absence of any other information, it appears they've looked at the survival rate of calves in the first two years of life and extrapolated it out 50 years into the future.\" He also stated that no captive orca has lived for 55.8 years, the recorded average life expectancy of adult orcas at SeaWorld."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722435987.9464, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "bc9d5486eaed40f8be91f71650db13c7", "0_model_name": "BM25", "0_prompt": "The best Sentinel-2 bands for wildfire-burned area segmentation", "0_output": [["The best Sentinel-2 bands for wildfire-burned area segmentation", "Title: Sen2Fire: A Challenging Benchmark Dataset for Wildfire Detection using Sentinel Data\n\nAbstract: Utilizing satellite imagery for wildfire detection presents substantial potential for practical applications. To advance the development of machine learning algorithms in this domain, our study introduces the \\textit{Sen2Fire} dataset--a challenging satellite remote sensing dataset tailored for wildfire detection. This dataset is curated from Sentinel-2 multi-spectral data and Sentinel-5P aerosol product, comprising a total of 2466 image patches. Each patch has a size of 512$\\times$512 pixels with 13 bands. Given the distinctive sensitivities of various wavebands to wildfire responses, our research focuses on optimizing wildfire detection by evaluating different wavebands and employing a combination of spectral indices, such as normalized burn ratio (NBR) and normalized difference vegetation index (NDVI). The results suggest that, in contrast to using all bands for wildfire detection, selecting specific band combinations yields superior performance. Additionally, our study underscores the positive impact of integrating Sentinel-5 aerosol data for wildfire detection. The code and dataset are available online (https://zenodo.org/records/10881058)."]], "0_corpus": "arxiv", "1_conv_id": "d679afd9ac124e39a9e06dbe8710a5ff", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "The best Sentinel-2 bands for wildfire-burned area segmentation", "1_output": [["The best Sentinel-2 bands for wildfire-burned area segmentation", "Title: Robust Burned Area Delineation through Multitask Learning\n\nAbstract: In recent years, wildfires have posed a significant challenge due to their increasing frequency and severity. For this reason, accurate delineation of burned areas is crucial for environmental monitoring and post-fire assessment. However, traditional approaches relying on binary segmentation models often struggle to achieve robust and accurate results, especially when trained from scratch, due to limited resources and the inherent imbalance of this segmentation task. We propose to address these limitations in two ways: first, we construct an ad-hoc dataset to cope with the limited resources, combining information from Sentinel-2 feeds with Copernicus activations and other data sources. In this dataset, we provide annotations for multiple tasks, including burned area delineation and land cover segmentation. Second, we propose a multitask learning framework that incorporates land cover classification as an auxiliary task to enhance the robustness and performance of the burned area segmentation models. We compare the performance of different models, including UPerNet and SegFormer, demonstrating the effectiveness of our approach in comparison to standard binary segmentation."]], "1_corpus": "arxiv"}
+{"tstamp": 1722445144.9651, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "e5dc54f50f85414484339a9b0ce90ef8", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "how to write time and date in military", "0_output": [["how to write time and date in military", "Date-time group\n\nMilitary Date Time Group A form of DTG is used in the US Military's message traffic (a form of Automated Message Handling System). In US military messages and communications (e.g., on maps showing troop movements) the format is DD HHMM (SS) Z MON YY. Although occasionally seen with spaces, it can also be written as a single string of characters. Three different formats can be found:\n- Full time (used for software timestamps)\n- shortened time (used e.g. for timestamps manually written)\n- short time (e.g. used for planning)\nreferences the military identifier of time zone:\nUTC-12: Y (e.g., Baker Island)\nUTC-11: X (American Samoa, Niue)\nUTC-10: W (Honolulu, HI)\nUTC-9: V (Juneau, AK)\nUTC-8: U (PST, Los Angeles, CA)\nUTC-7: T (MST, Denver, CO)\nUTC-6: S (CST, Dallas, TX)\nUTC-5: R (EST, New York, NY)\nUTC-4: Q (Halifax, Nova Scotia)\nUTC-3: P (Buenos Aires, Argentina; Rio de Janeiro, Brazil)\nUTC-2: O (South Georgia and the South Sandwich Islands)\nUTC-1: N (Azores)\nUTC+-0: Z (Zulu time)\nUTC+1: A (France)\nUTC+2: B (Athens, Greece)\nUTC+3: C (Arab Standard Time, Iraq, Bahrain, Kuwait, Saudi Arabia, Yemen, Qatar, as well as Moscow in Russia)\nUTC+4: D (Oman, the UAE)\nUTC+5: E (Pakistan, western Kazakhstan, Tajikistan, Uzbekistan, and Turkmenistan)\nUTC+6: F (eastern Kazakhstan, Bangladesh)\nUTC+7: G (Thailand)\nUTC+8: H (Beijing, China, Singapore)\nUTC+9: I (Tokyo, Japan)\nUTC+10: K (Brisbane, Australia)\nUTC+11: L (Sydney, Australia)\nUTC+12: M (Wellington, New Zealand)\nExamples\nExample 1:  represents the 5th day of the current month 11:00 (UTC).\nExample 2:  represents  9 July 2011 4:30 pm (MST).\nExample 3:  represents the current time of refresh:  (UTC)."]], "0_corpus": "wikipedia", "1_conv_id": "941220ab10f8447faa3721af54701b53", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "how to write time and date in military", "1_output": [["how to write time and date in military", "List of Dutch inventions and innovations\n\nAn artificial kidney is a machine and its related devices which clean blood for patients who have an acute or chronic failure of their kidneys. The first artificial kidney was developed by Dutchman Willem Johan Kolff. The procedure of cleaning the blood by this means is called dialysis, a type of renal replacement therapy that is used to provide an artificial replacement for lost kidney function due to renal failure. It is a life support treatment and does not treat disease.\nArtificial heart (1957)\nOn 12 December 1957, Kolff implanted an artificial heart into a dog at Cleveland Clinic. The dog lived for 90 minutes. In 1967, Dr. Kolff left Cleveland Clinic to start the Division of Artificial Organs at the University of Utah and pursue his work on the artificial heart. Under his supervision, a team of surgeons, chemists, physicists and bioengineers developed an artificial heart and made it ready for industrial production. To help manage his many endeavors, Dr. Kolff assigned project managers. Each project was named after its manager. Graduate student Robert Jarvik was the project manager for the artificial heart, which was subsequently renamed the Jarvik-7. Based on lengthy animal trials, this first artificial heart was successfully implanted into the thorax of patient Barney Clark in December 1982. Clark survived 112 days with the device.\nMilitary"]], "1_corpus": "wikipedia"}

data/retrieval_individual-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -18,3 +18,5 @@
 {"tstamp": 1722443999.2944, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722443998.2791, "finish": 1722443999.2944, "ip": "", "conv_id": "e87880dd7cd94e34b26d6ec08910f019", "model_name": "text-embedding-004", "prompt": "Fine-tuning BERT for optimal text classification performance on multiple datasets", "output": [["Fine-tuning BERT for optimal text classification performance on multiple datasets", "Title: How to Fine-Tune BERT for Text Classification?\n\nAbstract: Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets."]], "corpus": "arxiv"}
 {"tstamp": 1722444489.0388, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722444488.784, "finish": 1722444489.0388, "ip": "", "conv_id": "17817cacbabd4dcf9fa57b72945df25a", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "What is the antipode of heaven?", "output": [["What is the antipode of heaven?", "Heaven in Christianity\n\nIn Christianity, heaven is traditionally the location of the throne of God and the angels of God, and in most forms of Christianity it is the abode of the righteous dead in the afterlife. In some Christian denominations it is understood as a temporary stage before the resurrection of the dead and the saints' return to the New Earth.\nIn the Book of Acts, the resurrected Jesus ascends to heaven where, as the Nicene Creed states, he now sits at the right hand of God and will return to earth in the Second Coming. According to Catholic, Eastern Orthodox, and Oriental Orthodox teaching, Mary, mother of Jesus, is said to have been assumed into heaven without the corruption of her earthly body; she is venerated as Queen of Heaven.\nIn the Christian Bible, concepts about Christian eschatology, the future \"kingdom of heaven\", and the resurrection of the dead are found, particularly in the book of Revelation and in 1 Corinthians 15.\nDescription\nThe Bible does not provide much information on what Heaven is supposed to be like. As a result, Christian theologians are usually not very specific when describing heaven.\nThe Book of Revelation states that the New Jerusalem will be transported from Heaven to Earth, rather than people from Earth going to Heaven. The description of the gates of New Jerusalem in  inspired the idea of the Pearly gates, which is the informal name for the gateway to heaven according to some Christian denominations."]], "corpus": "wikipedia"}
 {"tstamp": 1722444489.0388, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722444488.784, "finish": 1722444489.0388, "ip": "", "conv_id": "2d381decf1d1476fbbd0c1c804ed3088", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What is the antipode of heaven?", "output": [["What is the antipode of heaven?", "Heaven\n\nHeaven, or the heavens, is a common religious cosmological or transcendent supernatural place where beings such as deities, angels, souls, saints, or venerated ancestors are said to originate, be enthroned, or reside. According to the beliefs of some religions, heavenly beings can descend to Earth or incarnate and earthly beings can ascend to Heaven in the afterlife or, in exceptional cases, enter Heaven without dying.\nHeaven is often described as a \"highest place\", the holiest place, a Paradise, in contrast to hell or the Underworld or the \"low places\" and universally or conditionally accessible by earthly beings according to various standards of divinity, goodness, piety, faith, or other virtues or right beliefs or simply divine will. Some believe in the possibility of a heaven on Earth in a world to come."]], "corpus": "wikipedia"}
+{"tstamp": 1722445177.4698, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722445177.0449, "finish": 1722445177.4698, "ip": "", "conv_id": "c4c4c875854a41f8a4f23c4dd4df5074", "model_name": "embed-english-v3.0", "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": 1722445177.4698, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722445177.0449, "finish": 1722445177.4698, "ip": "", "conv_id": "25390507c1a14ef1b32bb1574a1c3f26", "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_individual-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

@@ -332,3 +332,5 @@
 {"tstamp": 1722435933.2884, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722435933.1316, "finish": 1722435933.2884, "ip": "", "conv_id": "d679afd9ac124e39a9e06dbe8710a5ff", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "The best Sentinel-2 bands for wildfire-burned area segmentation", "output": [["The best Sentinel-2 bands for wildfire-burned area segmentation", "Title: Robust Burned Area Delineation through Multitask Learning\n\nAbstract: In recent years, wildfires have posed a significant challenge due to their increasing frequency and severity. For this reason, accurate delineation of burned areas is crucial for environmental monitoring and post-fire assessment. However, traditional approaches relying on binary segmentation models often struggle to achieve robust and accurate results, especially when trained from scratch, due to limited resources and the inherent imbalance of this segmentation task. We propose to address these limitations in two ways: first, we construct an ad-hoc dataset to cope with the limited resources, combining information from Sentinel-2 feeds with Copernicus activations and other data sources. In this dataset, we provide annotations for multiple tasks, including burned area delineation and land cover segmentation. Second, we propose a multitask learning framework that incorporates land cover classification as an auxiliary task to enhance the robustness and performance of the burned area segmentation models. We compare the performance of different models, including UPerNet and SegFormer, demonstrating the effectiveness of our approach in comparison to standard binary segmentation."]], "corpus": "arxiv"}
 {"tstamp": 1722445108.5632, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722445108.2586, "finish": 1722445108.5632, "ip": "", "conv_id": "e5dc54f50f85414484339a9b0ce90ef8", "model_name": "GritLM/GritLM-7B", "prompt": "how to write time and date in military", "output": [["how to write time and date in military", "Date-time group\n\nMilitary Date Time Group A form of DTG is used in the US Military's message traffic (a form of Automated Message Handling System). In US military messages and communications (e.g., on maps showing troop movements) the format is DD HHMM (SS) Z MON YY. Although occasionally seen with spaces, it can also be written as a single string of characters. Three different formats can be found:\n- Full time (used for software timestamps)\n- shortened time (used e.g. for timestamps manually written)\n- short time (e.g. used for planning)\nreferences the military identifier of time zone:\nUTC-12: Y (e.g., Baker Island)\nUTC-11: X (American Samoa, Niue)\nUTC-10: W (Honolulu, HI)\nUTC-9: V (Juneau, AK)\nUTC-8: U (PST, Los Angeles, CA)\nUTC-7: T (MST, Denver, CO)\nUTC-6: S (CST, Dallas, TX)\nUTC-5: R (EST, New York, NY)\nUTC-4: Q (Halifax, Nova Scotia)\nUTC-3: P (Buenos Aires, Argentina; Rio de Janeiro, Brazil)\nUTC-2: O (South Georgia and the South Sandwich Islands)\nUTC-1: N (Azores)\nUTC+-0: Z (Zulu time)\nUTC+1: A (France)\nUTC+2: B (Athens, Greece)\nUTC+3: C (Arab Standard Time, Iraq, Bahrain, Kuwait, Saudi Arabia, Yemen, Qatar, as well as Moscow in Russia)\nUTC+4: D (Oman, the UAE)\nUTC+5: E (Pakistan, western Kazakhstan, Tajikistan, Uzbekistan, and Turkmenistan)\nUTC+6: F (eastern Kazakhstan, Bangladesh)\nUTC+7: G (Thailand)\nUTC+8: H (Beijing, China, Singapore)\nUTC+9: I (Tokyo, Japan)\nUTC+10: K (Brisbane, Australia)\nUTC+11: L (Sydney, Australia)\nUTC+12: M (Wellington, New Zealand)\nExamples\nExample 1:  represents the 5th day of the current month 11:00 (UTC).\nExample 2:  represents  9 July 2011 4:30 pm (MST).\nExample 3:  represents the current time of refresh:  (UTC)."]], "corpus": "wikipedia"}
 {"tstamp": 1722445108.5632, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722445108.2586, "finish": 1722445108.5632, "ip": "", "conv_id": "941220ab10f8447faa3721af54701b53", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "how to write time and date in military", "output": [["how to write time and date in military", "List of Dutch inventions and innovations\n\nAn artificial kidney is a machine and its related devices which clean blood for patients who have an acute or chronic failure of their kidneys. The first artificial kidney was developed by Dutchman Willem Johan Kolff. The procedure of cleaning the blood by this means is called dialysis, a type of renal replacement therapy that is used to provide an artificial replacement for lost kidney function due to renal failure. It is a life support treatment and does not treat disease.\nArtificial heart (1957)\nOn 12 December 1957, Kolff implanted an artificial heart into a dog at Cleveland Clinic. The dog lived for 90 minutes. In 1967, Dr. Kolff left Cleveland Clinic to start the Division of Artificial Organs at the University of Utah and pursue his work on the artificial heart. Under his supervision, a team of surgeons, chemists, physicists and bioengineers developed an artificial heart and made it ready for industrial production. To help manage his many endeavors, Dr. Kolff assigned project managers. Each project was named after its manager. Graduate student Robert Jarvik was the project manager for the artificial heart, which was subsequently renamed the Jarvik-7. Based on lengthy animal trials, this first artificial heart was successfully implanted into the thorax of patient Barney Clark in December 1982. Clark survived 112 days with the device.\nMilitary"]], "corpus": "wikipedia"}
+{"tstamp": 1722445186.6013, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722445184.2856, "finish": 1722445186.6013, "ip": "", "conv_id": "8938df256e7147eb857d62b6793e0ffa", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "who played ben stone son on law and order", "output": [["who played ben stone son on law and order", "Benjamin Stone (Law & Order)\n\nBenjamin \"Ben\" Stone is a fictional character portrayed by Michael Moriarty in the TV drama Law & Order. He was the Executive Assistant District Attorney for New York County until his resignation at the end of season four. He appeared in 88 episodes.\nCharacter overview\nStone works in the Manhattan District Attorney's office under Alfred Wentworth (Roy Thinnes, in the pilot episode \"Everybody's Favorite Bagman\") and Adam Schiff (Steven Hill). He was born in 1943 and raised in an Irish Catholic family. He was raised mostly by his Irish grandmother, his father having been an alcoholic. Stone is divorced and has a son, Peter Stone (Philip Winchester), who was the Deputy Chief of the Special Prosecutions Bureau in Chicago and was the Chief ADA of the Sex Crimes Bureau in New York,\nand a daughter, Pamela (Amy Korb)."]], "corpus": "wikipedia"}
+{"tstamp": 1722445186.6013, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722445184.2856, "finish": 1722445186.6013, "ip": "", "conv_id": "b8d461fb91e64be581a193852f9c7600", "model_name": "text-embedding-3-large", "prompt": "who played ben stone son on law and order", "output": [["who played ben stone son on law and order", "Benjamin Stone (Law & Order)\n\nBenjamin \"Ben\" Stone is a fictional character portrayed by Michael Moriarty in the TV drama Law & Order. He was the Executive Assistant District Attorney for New York County until his resignation at the end of season four. He appeared in 88 episodes.\nCharacter overview\nStone works in the Manhattan District Attorney's office under Alfred Wentworth (Roy Thinnes, in the pilot episode \"Everybody's Favorite Bagman\") and Adam Schiff (Steven Hill). He was born in 1943 and raised in an Irish Catholic family. He was raised mostly by his Irish grandmother, his father having been an alcoholic. Stone is divorced and has a son, Peter Stone (Philip Winchester), who was the Deputy Chief of the Special Prosecutions Bureau in Chicago and was the Chief ADA of the Sex Crimes Bureau in New York,\nand a daughter, Pamela (Amy Korb)."]], "corpus": "wikipedia"}