Scheduled Commit

data/retrieval_battle-f36c9f97-e795-4522-8eb7-7dee254b42c7.jsonl

@@ -21,3 +21,5 @@
 {"tstamp": 1723553346.6846, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "234a6ea329f64bf49a87b6196b967256", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "Что такое ловушка в геологии?", "0_output": [["Что такое ловушка в геологии?", "Pskov Oblast\n\nThe Charter of Pskov Oblast is the fundamental law of the region. The Pskov Oblast Assembly of Deputies is the province's standing legislative (representative) body. The Assembly exercises its authority by passing laws, resolutions, and other legal acts and by supervising the implementation and observance of the laws and other legal acts passed by it. The highest executive body is the Oblast Government, which includes territorial executive bodies such as district administrations, committees, and commissions that facilitate development and run the day to day matters of the province. The Oblast administration supports the activities of the Governor who is the highest official and acts as guarantor of the observance of the oblast Charter in accordance with the Constitution of Russia.\nLiya Milushkina, supporter of Alexey Navalny and former head of the regional Open Russia organisation, and her husband Artyom were convicted for sale of illegal drugs and sentenced to 10.5 and 11 years in prison respectively. They said that the drugs had been planted and the conviction was politically motivated.\nFirst secretaries of the Pskov Oblast CPSU Committee\nIn the period when they were the most important authority in the oblast (1944 to 1991), the following first secretaries were appointed,\n1944-1949 Leonty Antyufeyev\n1949-1951 Gennady Shubin\n1951-1961 Mikhail Kanunnikov\n1961-1971 Ivan Gustov\n1971-1987 Alexey Rybakov\n1987-1988 Yury Pogorelov\n1988-1990 Alexey Ilyin\n1990-1991 Vladimir Nikitin\nGovernors\nSince 1991, governors were sometimes appointed, and sometimes elected,\n1991-1992 Anatoly Dobryakov, head of the administration, appointed\n1992-1996 Vladislav Tumanov, head of the administration, appointed\n1996-2004 Yevgeny Mikhailov, governor, elected\n2004-2009 Mikhail Kuznetsov, governor, elected\n2009-2017 Andrey Turchak, governor, appointed, elected\n2017-present Mikhail Vedernikov, governor, elected"]], "0_corpus": "wikipedia", "1_conv_id": "9bb3041daf144f9d9c377e7c755a6c60", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Что такое ловушка в геологии?", "1_output": [["Что такое ловушка в геологии?", "Grigori Grabovoi\n\nMeeting with Baba Vanga According to Grabovoi, in October 1995 he met the Bulgarian \"clairvoyant\" Baba Vanga in the village of Rupite, Bulgaria, and Valentina Genkova, the editor of the Bulgarian national television service, acted as translator. According to Genkova, issues related to nuclear and ecological risks for the planet, the prolongation of human life, the possibility of not dying and the unification of religions were discussed at the meeting. Vanga expressed her opinion that \"Grigori Petrovich, who has phenomenal qualities, must necessarily continue to work with people and expand the areas of application of his abilities. He should work in Russia, from where he will spread his knowledge and art to all countries of the world.\"\nLyudmila Kim (a traditional healer from Moscow)  said in an interview with the Komsomolskaya Pravda newspaper on October 13, 2005, that she was present at a meeting between Grabovoi and Vanga. According to Kim, Vanga's views on Grabovoi's abilities were sharply critical, and Grabovoi was expelled from the meeting. On April 7, 2006, Komsomolskaya Pravda wrote about a meeting of a \"young sesibila from Russia\" in Bulgaria in 1995 in which Vanga imposed a test on Grabovoi, but she was dissatisfied with the self-proclaimed \"healer\" and literally kicked Grabovoi out, as many Bulgarian newspapers recounted. In July 2006, Latvian/Russian editor and journalist Andrei Levkin told Vzglyad newspaper that a documentary on the Central Television network showed the filming of the meeting of Vanga and Grabovoi, which ended in Vanga's  emotional reaction and expulsion of Grabovoi.\nGenkova protested against the illegal use on Russian television of her copyrighted material, the filming of a meeting between Vanga and Grabovoi. Genkova stated that the significance of Vanga's conversation with Grabovoi was distorted in the fragment of the filmed material which was used."]], "1_corpus": "wikipedia"}
 {"tstamp": 1723554803.6716, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "50496366db2f4d42862f14e56e6f9d5d", "0_model_name": "voyage-multilingual-2", "0_prompt": "safety equipment for workers to climb buildings", "0_output": [["safety equipment for workers to climb buildings", "Safety harness\n\nA window cleaner who can be working up high on buildings need to use harnesses to keep them from falling many stories if they slip. That is the most common safety harness. A theatrical fly crew member will need a harness because they are up above the theater floor and they need to be safe in case they slip as well. Construction workers might need a harness because they could be building on the higher floors and without the harness they could fall to the ground. Crane operators will be at a height that will cause great injury or death if they were to fall off, so they need something to ensure their safety as they are working. A lineman has to climb power lines and they need to be secured in place so they can work on the high voltage lines without moving around.  Harnesses are used when sailing to prevent the crew from being thrown overboard in case the seas are rough.\nClimbing\nA climbing harness is a device which allows a climber access to the safety of a rope.\nRock climbers use harnesses to belay each other, this is when they use a rope to connect to one another so if the climber falls they can catch them with the rope instead of falling all the way to the ground.\nGuide or support\nA jackstay harness is a substantial line between two points used to guide or support."]], "0_corpus": "wikipedia", "1_conv_id": "4333077e6fbf42bda785a38ed03dd25f", "1_model_name": "BM25", "1_prompt": "safety equipment for workers to climb buildings", "1_output": [["safety equipment for workers to climb buildings", "Safety harness\n\nAn unrelated use with a materially different arresting mechanism is bungee jumping. Though they share certain similar attributes, a safety harness is not to be confused with a climbing harness used for mountaineering, rock climbing, and climbing gyms. Specialized harnesses for animal rescue or transfer, as from a dock to a vessel, are also made.\nSafety harnesses have restraints that prevent the wearer from falling from a height. By wearing the belt or harness the risk of injury from a fall is greatly reduced. The harness allows the user to attach themselves to an object that is stationary, ensuring they will not hit the ground in the event of a possible fall. Falling from high areas is one of the most common causes of injuries in the workplace, so it is very important to make sure you are properly equipped when working up high.\nBefore safety harnesses were required by OSHA (The Occupational Safety and Health Administration), workers wore body belts to connect to fall protection systems. Workers had the belts fastened around the waist, resulting in the entire force being exerted on the abdomen and often causing significant injury.\nOSHA implementing this requirement really made sure the amount of casualties decreased from falling, as well as injuries caused from the old belts they used to wear. Safety harnesses are essential while working in high areas to prevent significant injury or death, and OSHA making these a requirement made everyone understand the importance of safety-harnesses"]], "1_corpus": "wikipedia"}
 {"tstamp": 1723556232.5336, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6542ad389f2b48718b035edbf87b1c5d", "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": "9671663f29ea4c53ad024d71f252b226", "1_model_name": "intfloat/multilingual-e5-large-instruct", "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": 1723561856.6442, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6f72e7adae16465db19d1f0c8dcef745", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Survey on integrating large language models into information retrieval systems", "0_output": [["Survey on integrating large language models into information retrieval systems", "Title: Adaptive Feedforward Control For Reset Feedback Control Systems -- Application in Precision Motion Control\n\nAbstract: This paper presents a novel adaptive feedforward controller design for reset control systems. The combination of feedforward and reset feedback control promises high performance as the feedforward guarantees reference tracking, while the non-linear feedback element rejects disturbances. To overcome inevitable model mismatches, the feedforward controller adapts to increase precision in reference tracking. Where linear existing adaptive feedforward controllers do not guarantee convergence in the presence of reset, this work presents a novel adaptive law based on converging and diverging regions of adaptation to achieve good tracking. Experimental results demonstrate the claimed advantage of the novel method."]], "0_corpus": "arxiv", "1_conv_id": "8eb7d293d9b241ad8a556e24cfd75d5c", "1_model_name": "text-embedding-3-large", "1_prompt": "Survey on integrating large language models into information retrieval systems", "1_output": [["Survey on integrating large language models into information retrieval systems", "Title: Large Language Models for Information Retrieval: A Survey\n\nAbstract: As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing complex contextual signals and semantic nuances, thereby reshaping the IR landscape, they still face challenges such as data scarcity, interpretability, and the generation of contextually plausible yet potentially inaccurate responses. This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity). Meanwhile, the emergence of large language models (LLMs), typified by ChatGPT and GPT-4, has revolutionized natural language processing due to their remarkable language understanding, generation, generalization, and reasoning abilities. Consequently, recent research has sought to leverage LLMs to improve IR systems. Given the rapid evolution of this research trajectory, it is necessary to consolidate existing methodologies and provide nuanced insights through a comprehensive overview. In this survey, we delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers. Additionally, we explore promising directions, such as search agents, within this expanding field."]], "1_corpus": "arxiv"}
+{"tstamp": 1723561909.4003, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "4907360cccbb477a9e2ded466e4d385d", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "who was the movie citizen kane based on", "0_output": [["who was the movie citizen kane based on", "Charles Foster Kane\n\nCharles Foster Kane is a fictional character who is the subject of Orson Welles' 1941 film Citizen Kane.  Welles played Kane (receiving an Academy Award nomination), with Buddy Swan playing Kane as a child. Welles also produced, co-wrote and directed the film, winning an Oscar for writing the film.\nInspiration\nThe general consensus is that publishing tycoon William Randolph Hearst is the primary inspiration behind Charles Foster Kane.\nIn the film, Kane is given the line \"You provide the prose poems; I'll provide the war,\" undeniably similar to \"You furnish the pictures, and I'll furnish the war,\" a quote widely attributed to Hearst. Also, an overhead shot of Hearst's ranch is shown in the film as Xanadu, the lavish estate where Kane resides.\nIn addition, Kane's unsuccessful attempt to make his second wife an opera star parallels Hearst's effort to make his mistress Marion Davies a serious dramatic movie actress despite critics' complaints that she was miscast and better in light comedy roles. The connection with Hearst is strengthened by the fact that Welles's co-writer, Herman J. Mankiewicz, was a frequent guest of Davies at Hearst Castle."]], "0_corpus": "wikipedia", "1_conv_id": "9340ddb1efd64ba6940979916277905c", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "who was the movie citizen kane based on", "1_output": [["who was the movie citizen kane based on", "Xanadu (Citizen Kane)\n\nAppearance in the film Built on an immense \"private mountain\" located on the \"deserts of the Gulf Coast\" in Florida, Xanadu is described as being the world's largest private estate; \"Cost: no man can say,\" according to the newsreel at the beginning of the film. The newsreel directly quotes from Samuel Taylor Coleridge's poem Kubla Khan, which tells of the title character's erection of a \"stately pleasure-dome\" in the city of Xanadu. The newsreel also states that Kane specifically conceived the estate for Susan Alexander, his second wife. However, Susan grew to hate Xanadu, calling it \"forty-nine thousand acres [76 square miles, 200 km²] of nothing but scenery and statues.\" The News on the March description of Xanadu—a pastiche of the style of The March of Time news digests then regularly seen in theaters—is as follows:\nThe estate also boasts a championship golf course and a Venetian-style canal with gondolas. The extensive zoo and aquarium were stocked with a menagerie of animals including monkeys, horses, giraffes, birds, octopuses, elephants and donkeys. The estate is enclosed by an iron fence that is entered through a gateway with a giant letter \"K\" above it. Central to the estate is Xanadu proper, the castle-like mansion that served as Kane's home and repository for his enormous collection of antiquities and objets d'art. Xanadu has a butler and at least a few dozen footmen and maidservants, who are shown at the end of the scene where Kane wrecks his wife's suite after she leaves him."]], "1_corpus": "wikipedia"}

data/retrieval_individual-f36c9f97-e795-4522-8eb7-7dee254b42c7.jsonl

@@ -96,3 +96,9 @@
 {"tstamp": 1723556247.3917, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723556245.2004, "finish": 1723556247.3917, "ip": "", "conv_id": "25a9f4c2df4f43a49788976ec2cb5c6d", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "A paper arguing that LLMs capture human-like aspects of meaning through conceptual role.", "output": [["A paper arguing that LLMs capture human-like aspects of meaning through conceptual role.", "Title: Meaning without reference in large language models\n\nAbstract: The widespread success of large language models (LLMs) has been met with skepticism that they possess anything like human concepts or meanings. Contrary to claims that LLMs possess no meaning whatsoever, we argue that they likely capture important aspects of meaning, and moreover work in a way that approximates a compelling account of human cognition in which meaning arises from conceptual role. Because conceptual role is defined by the relationships between internal representational states, meaning cannot be determined from a model's architecture, training data, or objective function, but only by examination of how its internal states relate to each other. This approach may clarify why and how LLMs are so successful and suggest how they can be made more human-like."]], "corpus": "arxiv"}
 {"tstamp": 1723559917.5437, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723559916.8638, "finish": 1723559917.5437, "ip": "", "conv_id": "8c2980c8e45147298499ed5d20c31c45", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "New technique for few-shot object detection that reduces object confusion through context integration.", "output": [["New technique for few-shot object detection that reduces object confusion through context integration.", "Title: Context-Transformer: Tackling Object Confusion for Few-Shot Detection\n\nAbstract: Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches."]], "corpus": "arxiv"}
 {"tstamp": 1723559917.5437, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723559916.8638, "finish": 1723559917.5437, "ip": "", "conv_id": "6377c93547f547a09cce60d5ebee969f", "model_name": "text-embedding-3-large", "prompt": "New technique for few-shot object detection that reduces object confusion through context integration.", "output": [["New technique for few-shot object detection that reduces object confusion through context integration.", "Title: Context-Transformer: Tackling Object Confusion for Few-Shot Detection\n\nAbstract: Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches."]], "corpus": "arxiv"}
+{"tstamp": 1723561849.228, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723561848.6789, "finish": 1723561849.228, "ip": "", "conv_id": "6f72e7adae16465db19d1f0c8dcef745", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Survey on integrating large language models into information retrieval systems", "output": [["Survey on integrating large language models into information retrieval systems", "Title: Adaptive Feedforward Control For Reset Feedback Control Systems -- Application in Precision Motion Control\n\nAbstract: This paper presents a novel adaptive feedforward controller design for reset control systems. The combination of feedforward and reset feedback control promises high performance as the feedforward guarantees reference tracking, while the non-linear feedback element rejects disturbances. To overcome inevitable model mismatches, the feedforward controller adapts to increase precision in reference tracking. Where linear existing adaptive feedforward controllers do not guarantee convergence in the presence of reset, this work presents a novel adaptive law based on converging and diverging regions of adaptation to achieve good tracking. Experimental results demonstrate the claimed advantage of the novel method."]], "corpus": "arxiv"}
+{"tstamp": 1723561849.228, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723561848.6789, "finish": 1723561849.228, "ip": "", "conv_id": "8eb7d293d9b241ad8a556e24cfd75d5c", "model_name": "text-embedding-3-large", "prompt": "Survey on integrating large language models into information retrieval systems", "output": [["Survey on integrating large language models into information retrieval systems", "Title: Large Language Models for Information Retrieval: A Survey\n\nAbstract: As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing complex contextual signals and semantic nuances, thereby reshaping the IR landscape, they still face challenges such as data scarcity, interpretability, and the generation of contextually plausible yet potentially inaccurate responses. This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity). Meanwhile, the emergence of large language models (LLMs), typified by ChatGPT and GPT-4, has revolutionized natural language processing due to their remarkable language understanding, generation, generalization, and reasoning abilities. Consequently, recent research has sought to leverage LLMs to improve IR systems. Given the rapid evolution of this research trajectory, it is necessary to consolidate existing methodologies and provide nuanced insights through a comprehensive overview. In this survey, we delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers. Additionally, we explore promising directions, such as search agents, within this expanding field."]], "corpus": "arxiv"}
+{"tstamp": 1723561864.0856, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723561861.8587, "finish": 1723561864.0856, "ip": "", "conv_id": "4907360cccbb477a9e2ded466e4d385d", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "who was the movie citizen kane based on", "output": [["who was the movie citizen kane based on", "Charles Foster Kane\n\nCharles Foster Kane is a fictional character who is the subject of Orson Welles' 1941 film Citizen Kane.  Welles played Kane (receiving an Academy Award nomination), with Buddy Swan playing Kane as a child. Welles also produced, co-wrote and directed the film, winning an Oscar for writing the film.\nInspiration\nThe general consensus is that publishing tycoon William Randolph Hearst is the primary inspiration behind Charles Foster Kane.\nIn the film, Kane is given the line \"You provide the prose poems; I'll provide the war,\" undeniably similar to \"You furnish the pictures, and I'll furnish the war,\" a quote widely attributed to Hearst. Also, an overhead shot of Hearst's ranch is shown in the film as Xanadu, the lavish estate where Kane resides.\nIn addition, Kane's unsuccessful attempt to make his second wife an opera star parallels Hearst's effort to make his mistress Marion Davies a serious dramatic movie actress despite critics' complaints that she was miscast and better in light comedy roles. The connection with Hearst is strengthened by the fact that Welles's co-writer, Herman J. Mankiewicz, was a frequent guest of Davies at Hearst Castle."]], "corpus": "wikipedia"}
+{"tstamp": 1723561864.0856, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1723561861.8587, "finish": 1723561864.0856, "ip": "", "conv_id": "9340ddb1efd64ba6940979916277905c", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "who was the movie citizen kane based on", "output": [["who was the movie citizen kane based on", "Xanadu (Citizen Kane)\n\nAppearance in the film Built on an immense \"private mountain\" located on the \"deserts of the Gulf Coast\" in Florida, Xanadu is described as being the world's largest private estate; \"Cost: no man can say,\" according to the newsreel at the beginning of the film. The newsreel directly quotes from Samuel Taylor Coleridge's poem Kubla Khan, which tells of the title character's erection of a \"stately pleasure-dome\" in the city of Xanadu. The newsreel also states that Kane specifically conceived the estate for Susan Alexander, his second wife. However, Susan grew to hate Xanadu, calling it \"forty-nine thousand acres [76 square miles, 200 km²] of nothing but scenery and statues.\" The News on the March description of Xanadu—a pastiche of the style of The March of Time news digests then regularly seen in theaters—is as follows:\nThe estate also boasts a championship golf course and a Venetian-style canal with gondolas. The extensive zoo and aquarium were stocked with a menagerie of animals including monkeys, horses, giraffes, birds, octopuses, elephants and donkeys. The estate is enclosed by an iron fence that is entered through a gateway with a giant letter \"K\" above it. Central to the estate is Xanadu proper, the castle-like mansion that served as Kane's home and repository for his enormous collection of antiquities and objets d'art. Xanadu has a butler and at least a few dozen footmen and maidservants, who are shown at the end of the scene where Kane wrecks his wife's suite after she leaves him."]], "corpus": "wikipedia"}
+{"tstamp": 1723561916.8226, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1723561914.6625, "finish": 1723561916.8226, "ip": "", "conv_id": "be42ce971c67420f8caa1aa168521086", "model_name": "GritLM/GritLM-7B", "prompt": "why ospf has area 0?", "output": [["why ospf has area 0?", "Serverfault Stackexchange\n\nQ: What is the network address (x.x.x.0) used for? It appears to be common practice to not use the first address in a subnet, that is the IP 192.168.0.0/24, or a more exotic example would be 172.20.20.64/29.\nThe ipcalc tool I frequently use follows the same practice:\n$ ipcalc -n -b 172.20.20.64/29\nAddress:   172.20.20.64         \nNetmask:   255.255.255.248 = 29 \nWildcard:  0.0.0.7              \n=>\nNetwork:   172.20.20.64/29      \nHostMin:   172.20.20.65         \nHostMax:   172.20.20.70         \nBroadcast: 172.20.20.71         \nHosts/Net: 6                     Class B, Private Internet\n\nBut why is that HostMin is not simply 64 in this case? The 64 address is a valid address, right? And whatever the answer, does the same apply to IPv6?\nPerhaps slightly related: it also appears possible to use a TCP port 0 and an UDP port 0. Are these valid or used anywhere?\n\nA: Early in the internet days, x.x.x.0 was used as the broadcast address for a network.  That was later changed to x.x.x.255.  I remember that there were options on SunOS to configure the broadcast either as .0 or .255 during that period.  So I guess for safety, .0 and .255 have been always a reserved numbers.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1723561916.8226, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723561914.6625, "finish": 1723561916.8226, "ip": "", "conv_id": "2b54195cf4c24491aadd00793db18412", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "why ospf has area 0?", "output": [["why ospf has area 0?", "Mathematica Stackexchange\n\nQ: Random point configuration from the Poincare disk model gives division by zero problem near boundary I am trying to select points uniformly at random from the Poincare disk model of hyperbolic geometry:\nshowpts[t_] := Module[{}, reg = Disk[{0, 0}, 0.99];\n  pts = RandomPointConfiguration[\n    InhomogeneousPoissonPointProcess[\n     Function[4/(t^2 (1 - ((#1)^2 + (#2)^2))^2)], 2], reg];\n  Show[RegionPlot[reg], ListPlot[pts]]]\nshowpts[1]\n\nBut this returns with a division by zero problem,\n\nIf I run this with a disk radius of 0.91 I get the same problem. I would have thought that would easily avoid the problem of the point process density being undefined at the boundary of the disk.\nThe code works with a disk radius of 0.9,\nshowpts[t_] := Module[{}, reg = Disk[{0, 0}, 0.9];\n  pts = RandomPointConfiguration[\n    InhomogeneousPoissonPointProcess[\n     Function[4/(t^2 (1 - ((#1)^2 + (#2)^2))^2)], 2], reg];\n  Show[RegionPlot[reg], ListPlot[pts]]]\nshowpts[1]\n\n\nIs this the cause of the problem?\n\nA: WorkingPrecision -> 80 seems work.\nt = 1;\n RandomPointConfiguration[\n InhomogeneousPoissonPointProcess[\n  Function[4/(t^2 (1 - ((#1)^2 + (#2)^2))^2)], 2], Disk[{0, 0}, .95], \n WorkingPrecision -> 80]\nShow[Graphics[Circle[]], ListPlot[%]]\n\n\n"]], "corpus": "stackexchange"}