Scheduled Commit

data/retrieval_battle-2cea24b1-39e9-480a-ba22-c617ea05c1fe.jsonl

@@ -48,3 +48,8 @@
 {"tstamp": 1724126034.0275, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1fffa14cd9944d98a6b0c4ce7570f50a", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Largest attendance at basketball game", "0_output": [["Largest attendance at basketball game", "The Basketball Tournament\n\nThe 2022 final was held on August 2, again in Dayton, between Blue Collar U (Buffalo) and Autism Army. Blue Collar U outscored Autism Army in every quarter and won the game, 89–67. The winning points came on a dunk by Montell McRae. C. J. Massinburg of Blue Collar U was named MVP.\nChampionship game records\nTeams are ordered by number of appearances.\nRelated venture\nOn October 11, 2022, tournament owner and organizer TBT Enterprises announced that it would extend the TBT concept to soccer, announcing The Soccer Tournament, a 7-on-7 summer tournament that began on schedule in 2023. Like TBT, TST has a winner-take-all prize of $1 million. TST started with 32 teams, with a group phase followed by a 16-team knockout tournament.\nMatches are played on a reduced-size field with slightly smaller goals than normal and consist of 20-minute halves. The Elam Ending has been adapted to a soccer setting; after the end of the second half, the remainder of the match consists of \"Target Score Time\", with the target score being set by adding one goal to the leading (or tied) team's score. The game ends once the target score is reached by either team. If the target is not reached after 5 minutes, one player from each team exits the field, with the process continuing every 5 minutes until each side is reduced to 2 players or the winning goal is scored, whichever comes first."]], "0_corpus": "wikipedia", "1_conv_id": "1b2c8305fcc9472bb40d52a201d62067", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Largest attendance at basketball game", "1_output": [["Largest attendance at basketball game", "List of sporting venues with a highest attendance of 100,000 or more\n\nThe following is an incomplete list of sports venues that currently have or once had a highest attendance of 100,000 people or more. Included are also those venues which have closed, been refurbished, or have been proposed.\nCurrent venues\nThis list is for those venues that are currently open for use and have a permanent structure (open venues such as for marathons are not considered). Their highest attendance may have occurred at a time when the configuration of venue was different, through the use of standing sections, or the use of infield areas. In the case of AT&T Stadium, the highest attendance was recorded for a basketball game, which used field-level seating not available for the venue's standard American football configuration. The largest sporting venue in the world, the Indianapolis Motor Speedway, has a permanent seating capacity for more than 257,000 people and infield seating that raises capacity to an approximate 400,000. Additionally, TPC Scottsdale during the Phoenix Open has the ability to hold 250,000 spectators in a single day, hosting 216,818 golf fans on February 3, 2018.\nClosed, proposed, and expanding venues\nThis list is for those venues that are currently closed, not in use, proposed, or are currently being expanded to 100,000 or more spectators."]], "1_corpus": "wikipedia"}
 {"tstamp": 1724138679.2543, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "d5f2a4e750ae4f5b8ea6cc667c618eff", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Who is that famous Austrian painter guy? ", "0_output": [["Who is that famous Austrian painter guy? ", "Gustav Klimt\n\nGustav Klimt (14 July 1862 – 6 February 1918) was an Austrian symbolist painter and one of the most prominent members of the Vienna Secession movement. Klimt is noted for his paintings, murals, sketches, and other objets d'art. Klimt's primary subject was the female body, and his works are marked by a frank eroticism. Amongst his figurative works, which include allegories and portraits, he painted landscapes. Among the artists of the Vienna Secession, Klimt was the most influenced by Japanese art and its methods.\nEarly in his career, he was a successful painter of architectural decorations in a conventional manner. As he began to develop a more personal style, his work was the subject of controversy that culminated when the paintings he completed around 1900 for the ceiling of the Great Hall of the University of Vienna were criticized as pornographic. He subsequently accepted no more public commissions, but achieved a new success with the paintings of his \"golden phase\", many of which include gold leaf. Klimt's work was an important influence on his younger peer Egon Schiele.\nSince the 1990s, he has been one of the artists whose paintings fetch top prices at auctions.\nBiography\nEarly life\nGustav Klimt was born in Baumgarten, near Vienna in the Austrian Empire, the second of seven children—three boys and four girls. His mother, Anna Klimt (née Finster), had an unrealized ambition to be a musical performer. His father, Ernst Klimt the Elder, formerly from Bohemia, was a gold engraver. All three of their sons displayed artistic talent early on. Klimt's younger brothers were Ernst Klimt and ."]], "0_corpus": "wikipedia", "1_conv_id": "47a792b052334b92b4096e5bdf3fb975", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Who is that famous Austrian painter guy? ", "1_output": [["Who is that famous Austrian painter guy? ", "Gustav Klimt\n\nGustav Klimt (14 July 1862 – 6 February 1918) was an Austrian symbolist painter and one of the most prominent members of the Vienna Secession movement. Klimt is noted for his paintings, murals, sketches, and other objets d'art. Klimt's primary subject was the female body, and his works are marked by a frank eroticism. Amongst his figurative works, which include allegories and portraits, he painted landscapes. Among the artists of the Vienna Secession, Klimt was the most influenced by Japanese art and its methods.\nEarly in his career, he was a successful painter of architectural decorations in a conventional manner. As he began to develop a more personal style, his work was the subject of controversy that culminated when the paintings he completed around 1900 for the ceiling of the Great Hall of the University of Vienna were criticized as pornographic. He subsequently accepted no more public commissions, but achieved a new success with the paintings of his \"golden phase\", many of which include gold leaf. Klimt's work was an important influence on his younger peer Egon Schiele.\nSince the 1990s, he has been one of the artists whose paintings fetch top prices at auctions.\nBiography\nEarly life\nGustav Klimt was born in Baumgarten, near Vienna in the Austrian Empire, the second of seven children—three boys and four girls. His mother, Anna Klimt (née Finster), had an unrealized ambition to be a musical performer. His father, Ernst Klimt the Elder, formerly from Bohemia, was a gold engraver. All three of their sons displayed artistic talent early on. Klimt's younger brothers were Ernst Klimt and ."]], "1_corpus": "wikipedia"}
 {"tstamp": 1724141134.0093, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d70a7f5c65e445cda73484ec4d902b7c", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "where does the big sur begin and end", "0_output": [["where does the big sur begin and end", "Big Sur\n\nBig Sur () is a rugged and mountainous section of the Central Coast of the U.S. state of California, between Carmel Highlands and San Simeon, where the Santa Lucia Mountains rise abruptly from the Pacific Ocean. It is frequently praised for its dramatic scenery. Big Sur has been called the \"longest and most scenic stretch of undeveloped coastline in the contiguous United States\", a sublime \"national treasure that demands extraordinary procedures to protect it from development\", and \"one of the most beautiful coastlines anywhere in the world, an isolated stretch of road, mythic in reputation\". The views, redwood forests, hiking, beaches, and other recreational opportunities have made Big Sur a popular destination for visitors from across the world. With 4.5 to 7 million visitors annually, it is among the top tourist destinations in the United States, comparable to Yosemite National Park, but with considerably fewer services, and less parking, roads, and related infrastructure."]], "0_corpus": "wikipedia", "1_conv_id": "81734a53605e4483a369da22be9b4923", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "where does the big sur begin and end", "1_output": [["where does the big sur begin and end", "Big Sur\n\nRestricted public transportation Public transportation is available to and from Monterey on Monterey–Salinas Transit. The summer schedule operates from Memorial Day to Labor Day three times a day, while the winter schedule only offers bus service on weekends. The route is subject to interruption due to wind and severe inclement weather.\nLimited vehicle services\nThere are only six gas stations along Highway 1 in Big Sur, from Ragged Point in the south to Carmel Highlands in the north. Three of them are in the north near Big Sur Valley. The gas station at the Big Sur River Inn and Restaurant offers a steep discount to local residents. The filling station in Gorda has one of the highest prices in the United States, as it is far from the electrical grid and part of the cost of auto fuel is used to support the operation of a diesel generator. All of them only operate during regular business hours and none of them supply diesel fuel. There are three Tesla recharging stations near Posts.\nLack of restrooms\nThere are only 16 public restrooms along the entire coast to accommodate the almost 5 million annual visitors. The number of visitors far exceeds the available restrooms, and most restrooms are not available in locations where tourists frequently visit.  Businesses report that the large number of visitors using their bathroom has overwhelmed their septic systems."]], "1_corpus": "wikipedia"}
+{"tstamp": 1724147162.3895, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "dac8741ee39e48d9a36f2ce6989ae96b", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. "]], "0_corpus": "wikipedia", "1_conv_id": "13079d64cb37499483293b81f18451d5", "1_model_name": "text-embedding-004", "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"}
+{"tstamp": 1724147206.4126, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "892e669a6bc34e9f94931b36cfeb01cd", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "what is the difference between a vpn and a private network?", "0_output": [["what is the difference between a vpn and a private network?", "Stackoverflow Stackexchange\n\nQ: Objective-C get a class property from string I've heard a number of similar questions for other languages, but I'm looking for a specific scenario.\nMy app has a Core Data model called \"Record\", which has a number of columns/properties like \"date, column1 and column2\". To keep the programming clean so I can adapt my app to multiple scenarios, input fields are mapped to a Core Data property inside a plist (so for example, I have a string variable called \"dataToGet\" with a value of 'column1'.\nHow can I retrieve the property \"column1\" from the Record class by using the dataToGet variable?\n\nA: The Key Value Coding mechanism allows you to interact with a class's properties using string representations of the property names. So, for example, if your Record class has a property called column1, you can access that property as follows:\nNSString* dataToGet = @\"column1\";\nid value = [myRecord valueForKey:dataToGet];\n\nYou can adapt that principle to your specific needs.\n"]], "0_corpus": "stackexchange", "1_conv_id": "8d0eef1f42dc4d7ca7b675a4cb2e4e76", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "what is the difference between a vpn and a private network?", "1_output": [["what is the difference between a vpn and a private network?", "Superuser Stackexchange\n\nQ: Extranet vs VPN What's the difference between an extranet and a VPN, and what common uses does each one serve? What technology stacks are often use to implement each?\n\nA: They are names for very different concepts.\nA VPN is a \"virtual private network\", a piece of software that creates an encrypted communication between two (potentially) far-away computers such that nobody in between can see the contents of the communication.\nAn extranet is something of a made-up term, referring to a web site that a company publishes for the benefit of its vendors, partners, or customers. The name \"extranet\" was derived from \"intranet\", which is a web site that's intended for use only within a company. That term itself was sort of perverted from \"Internet\", which is literally a network of networks (web sites came around long after the Internet per se was created).\nSo an extranet is really a web site with a specific purpose. It's not a technology for setting up communications between computers like a VPN is.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1724147246.3395, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "770183d0341d414c805cc19ed04f25f1", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "what is the difference between nominal and minimum?", "0_output": [["what is the difference between nominal and minimum?", "English Stackexchange\n\nQ: What is the difference between \"minimum\" and \"minimal\"? I am not a native speaker, but for me\n \"the minimum angle\" and \"the optimal solution\"\nsound correct, but only because I hear and read them more often.\nWhy are \"the minimal angle\" and \"the optimum solution\" not used as often or wrong?  Are there rules?\n\nA: Minimum is fairly absolute and solid, and refers to the smallest number or amount possible.\n\nMinimum: the least or smallest amount or quantity possible,\nattainable, or required.\n\nMinimal is a little more flexible, where it refers to being the smallest amount or degree in non-absolute terms.\n\nMinimal: of a minimum amount, quantity, or degree.\n\n\"Of a minimum amount\", without specifying the minimum amount.\nAn example of both might be:\n\nJane has minimal interest in going to the movies; mostly because it seems the ticket prices rise at a minimum of $1.50 every time.\nI keep the amount of furniture in my room at a minimum because I like my room to be minimal. I'm a minimalistic person.\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "b3f195571ab340f5a6aba9b00f47d682", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "what is the difference between nominal and minimum?", "1_output": [["what is the difference between nominal and minimum?", "Electronics Stackexchange\n\nQ: What does NOM stand for in a datasheet and what does it mean? The question is in reference to: http://www.ti.com/lit/ds/symlink/sn74ls00.pdf\n\nA: NOM = Nominal, that's the value you normally can expect, and what the device is designed to.  \nNote that nominal values are often not the best to calculate with. If you want to go worst case you'll have to calculate with Minimum or Maximum, depending on the parameter and the application.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1724147306.6021, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "289a6fa2c341488b878cbdd72a776881", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "is asgard a real planet?", "0_output": [["is asgard a real planet?", "Scifi Stackexchange\n\nQ: Why is it called Middle-earth? The planet is called Arda.\nSo why do they call it \"Middle-earth\" if there is no planet called Earth to have a middle of?\n\nA: \"Middle-earth\" is a modern version of the word \"Midgard\" which in Norse mythology is the world that humans live on (there are eight others). Tolkien was known to derive some of the mythology of Middle-earth from Norse mythology (including parts of the Ainulindalë, the creation scene at the beginning of the Silmarillion).\n"]], "0_corpus": "stackexchange", "1_conv_id": "db25d91c18d2409ab674107856390073", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "is asgard a real planet?", "1_output": [["is asgard a real planet?", "Scifi Stackexchange\n\nQ: What’s the official in-universe classification of Thor and other Asgardians in the MCU? What’s the official in-universe classification of Asgardians in the MCU?\nBy official, I mean what category does S.H.I.E.L.D. put them into, i.e. “Alien,” “Human” or do they just designate them as “Gods?”\n\nA: S.H.I.E.L.D. likely classifies Asgardians as aliens, judging by this dialogue from an early episode of Agents of S.H.I.E.L.D..\n\nSKYE: So, Asgardians are aliens from another planet that visited us thousands of years ago?\nAGENT COULSON: Or more.\nSKYE: And because we couldn't understand aliens, we thought they were gods?\nAGENT COULSON: That's where our Norse mythology comes from.\nSKYE: [sighs] That's too crazy. Do you think other deities are aliens, too? Vishnu for sure, right?\nAgents of S.H.I.E.L.D. - S01E08 - \"The Well\"\n\nOdin probably would've agreed with this classification, since he clearly stated that MCU Asgardians aren't gods.\n\nLOKI: I went down to Midgard to rule the people of Earth as a benevolent god. Just like you.\nODIN: We are not gods. We are born, we live, we die. Just as humans do.\nLOKI: Give or take 5000 years.\nThor: The Dark World (2013)\n\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1724147323.7292, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "b1622a49d84d41de81be59458d957680", "0_model_name": "BM25", "0_prompt": "How to combine GPT-2 and BERT for better question generation and answering?", "0_output": [["How to combine GPT-2 and BERT for better question generation and answering?", "Title: Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds\n\nAbstract: Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime."]], "0_corpus": "arxiv", "1_conv_id": "a1b61891a5fc41888a6df192888fa1c6", "1_model_name": "text-embedding-3-large", "1_prompt": "How to combine GPT-2 and BERT for better question generation and answering?", "1_output": [["How to combine GPT-2 and BERT for better question generation and answering?", "Title: Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds\n\nAbstract: Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime."]], "1_corpus": "arxiv"}

data/retrieval_individual-2cea24b1-39e9-480a-ba22-c617ea05c1fe.jsonl

@@ -169,3 +169,15 @@
 {"tstamp": 1724142270.3508, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1724142268.2234, "finish": 1724142270.3508, "ip": "", "conv_id": "710c018891ac4cef89aea2b815e09cec", "model_name": "intfloat/multilingual-e5-large-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: InstructCoder: Instruction Tuning Large Language Models for Code Editing\n\nAbstract: Code editing encompasses a variety of pragmatic tasks that developers deal with daily. Despite its relevance and practical usefulness, automatic code editing remains an underexplored area in the evolution of deep learning models, partly due to data scarcity. In this work, we explore the use of Large Language Models (LLMs) to edit code based on user instructions. Evaluated on a novel human-written execution-based benchmark dubbed EditEval, we found current models often struggle to fulfill the instructions. In light of this, we contribute InstructCoder, the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing, containing high-diversity code-editing tasks such as comment insertion, code optimization, and code refactoring. It consists of over 114,000 instruction-input-output triplets and covers multiple distinct code editing scenarios. The collection process starts with filtered commit data sourced from GitHub Python repositories as seeds. Subsequently, the dataset is systematically expanded through an iterative process, where both seed and generated tasks are used to prompt ChatGPT for more data. Our findings reveal that open-source LLMs fine-tuned on InstructCoder can significantly enhance the accuracy of code edits, exhibiting superior code-editing performance matching advanced proprietary LLMs. The datasets and the source code are publicly available at https://github.com/qishenghu/CodeInstruct."]], "corpus": "arxiv"}
 {"tstamp": 1724142400.9728, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1724142398.8246, "finish": 1724142400.9728, "ip": "", "conv_id": "c2a4ffa0ef9d402a9ec714bd3fae6f73", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Welcher Test wurde entwickelt, um festzustellen, ob Roboter denken können?", "output": [["Welcher Test wurde entwickelt, um festzustellen, ob Roboter denken können?", "Computing Machinery and Intelligence\n\n\"Computing Machinery and Intelligence\" is a seminal paper written by Alan Turing on the topic of artificial intelligence. The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public.\nTuring's paper considers the question \"Can machines think?\" Turing says that since the words \"think\" and \"machine\" cannot be clearly defined we should \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" To do this, he must first find a simple and unambiguous idea to replace the word \"think\", second he must explain exactly which \"machines\" he is considering, and finally, armed with these tools, he formulates a new question, related to the first, that he believes he can answer in the affirmative."]], "corpus": "wikipedia"}
 {"tstamp": 1724142400.9728, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1724142398.8246, "finish": 1724142400.9728, "ip": "", "conv_id": "9e47fb94bb034697b89336cf4d5f295d", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Welcher Test wurde entwickelt, um festzustellen, ob Roboter denken können?", "output": [["Welcher Test wurde entwickelt, um festzustellen, ob Roboter denken können?", "Kleihauer–Betke test\n\nMethod Thin smears are prepared from capillary blood or venous blood collected into anticoagulants such as heparine, oxalate, citrate, or EDTA. Smears are air dried between 10–60 minutes, fixed in 80 vol% ethanol for 5 min at 20-22 °C, rinsed with tap water, and air dried. Films are then immersed in the citrate-phosphate buffer for 5 minutes at 37 °C and gently agitated for about 3 minutes. Slides are rinsed with tap water, dried, and stained with Ehrlich's acid hematoxylin for 3 min, rinsed with water, and dried again. They are counterstained with erythrosine for 3 min. After a final rinse, films are dried and examined under light microscopy.\nResults of the original method\nHemoglobin F cells are densely stained with erythrosine, Hemoglobin A cells appear as ghost cells, while intermediate cells are stained more or less pink. Reticulocytes containing Hemoglobin A may appear as intermediate cells and/or may show intracellular granulation. Inclusion bodies (Heinz bodies, precipitated α-chains or β-chains) are visible in eluted cells as compact inclusions of different size. Hemoglobin A is eluted regardless of whether it is oxyhemoglobin, methemoglobin, cyanmethemoglobin, reduced hemoglobin, or carboxyhemoglobin.\nQuantitation of Hemoglobin F Cells\nMethods developed by Schneider and Ludwig and Bartsch' are recommended. For determination of the intracellular distribution of Hemoglobin F, the semi-quantitative method of Shepard, Weatherall, and Conley' may be employed.\nNormal Values\nNormal values for Hemoglobin F cells in adults as published originally by Kleihauer were below 0.01%; in full-term newborns they are above 90%.\nUses\nFetal–maternal hemorrhage severity estimation\nTo determine if a positive test for FMH indicates the likely cause of fetal death, the percent of total fetal blood volume lost should be calculated, making appropriate adjustments based on the following known relationships:\nthe size of a fetal red blood cell is 1.22 times that of an adult red blood cell;"]], "corpus": "wikipedia"}
+{"tstamp": 1724147150.1606, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1724147149.819, "finish": 1724147150.1606, "ip": "", "conv_id": "dac8741ee39e48d9a36f2ce6989ae96b", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. "]], "corpus": "wikipedia"}
+{"tstamp": 1724147150.1606, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1724147149.819, "finish": 1724147150.1606, "ip": "", "conv_id": "13079d64cb37499483293b81f18451d5", "model_name": "text-embedding-004", "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": 1724147184.6346, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1724147182.4782, "finish": 1724147184.6346, "ip": "", "conv_id": "892e669a6bc34e9f94931b36cfeb01cd", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "what is the difference between a vpn and a private network?", "output": [["what is the difference between a vpn and a private network?", "Stackoverflow Stackexchange\n\nQ: Objective-C get a class property from string I've heard a number of similar questions for other languages, but I'm looking for a specific scenario.\nMy app has a Core Data model called \"Record\", which has a number of columns/properties like \"date, column1 and column2\". To keep the programming clean so I can adapt my app to multiple scenarios, input fields are mapped to a Core Data property inside a plist (so for example, I have a string variable called \"dataToGet\" with a value of 'column1'.\nHow can I retrieve the property \"column1\" from the Record class by using the dataToGet variable?\n\nA: The Key Value Coding mechanism allows you to interact with a class's properties using string representations of the property names. So, for example, if your Record class has a property called column1, you can access that property as follows:\nNSString* dataToGet = @\"column1\";\nid value = [myRecord valueForKey:dataToGet];\n\nYou can adapt that principle to your specific needs.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1724147184.6346, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1724147182.4782, "finish": 1724147184.6346, "ip": "", "conv_id": "8d0eef1f42dc4d7ca7b675a4cb2e4e76", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "what is the difference between a vpn and a private network?", "output": [["what is the difference between a vpn and a private network?", "Superuser Stackexchange\n\nQ: Extranet vs VPN What's the difference between an extranet and a VPN, and what common uses does each one serve? What technology stacks are often use to implement each?\n\nA: They are names for very different concepts.\nA VPN is a \"virtual private network\", a piece of software that creates an encrypted communication between two (potentially) far-away computers such that nobody in between can see the contents of the communication.\nAn extranet is something of a made-up term, referring to a web site that a company publishes for the benefit of its vendors, partners, or customers. The name \"extranet\" was derived from \"intranet\", which is a web site that's intended for use only within a company. That term itself was sort of perverted from \"Internet\", which is literally a network of networks (web sites came around long after the Internet per se was created).\nSo an extranet is really a web site with a specific purpose. It's not a technology for setting up communications between computers like a VPN is.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1724147224.0823, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1724147221.8491, "finish": 1724147224.0823, "ip": "", "conv_id": "770183d0341d414c805cc19ed04f25f1", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "what is the difference between nominal and minimum?", "output": [["what is the difference between nominal and minimum?", "English Stackexchange\n\nQ: What is the difference between \"minimum\" and \"minimal\"? I am not a native speaker, but for me\n \"the minimum angle\" and \"the optimal solution\"\nsound correct, but only because I hear and read them more often.\nWhy are \"the minimal angle\" and \"the optimum solution\" not used as often or wrong?  Are there rules?\n\nA: Minimum is fairly absolute and solid, and refers to the smallest number or amount possible.\n\nMinimum: the least or smallest amount or quantity possible,\nattainable, or required.\n\nMinimal is a little more flexible, where it refers to being the smallest amount or degree in non-absolute terms.\n\nMinimal: of a minimum amount, quantity, or degree.\n\n\"Of a minimum amount\", without specifying the minimum amount.\nAn example of both might be:\n\nJane has minimal interest in going to the movies; mostly because it seems the ticket prices rise at a minimum of $1.50 every time.\nI keep the amount of furniture in my room at a minimum because I like my room to be minimal. I'm a minimalistic person.\n\n"]], "corpus": "stackexchange"}
+{"tstamp": 1724147224.0823, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1724147221.8491, "finish": 1724147224.0823, "ip": "", "conv_id": "b3f195571ab340f5a6aba9b00f47d682", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "what is the difference between nominal and minimum?", "output": [["what is the difference between nominal and minimum?", "Electronics Stackexchange\n\nQ: What does NOM stand for in a datasheet and what does it mean? The question is in reference to: http://www.ti.com/lit/ds/symlink/sn74ls00.pdf\n\nA: NOM = Nominal, that's the value you normally can expect, and what the device is designed to.  \nNote that nominal values are often not the best to calculate with. If you want to go worst case you'll have to calculate with Minimum or Maximum, depending on the parameter and the application.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1724147279.1875, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1724147278.9522, "finish": 1724147279.1875, "ip": "", "conv_id": "289a6fa2c341488b878cbdd72a776881", "model_name": "GritLM/GritLM-7B", "prompt": "is asgard a real planet?", "output": [["is asgard a real planet?", "Scifi Stackexchange\n\nQ: Why is it called Middle-earth? The planet is called Arda.\nSo why do they call it \"Middle-earth\" if there is no planet called Earth to have a middle of?\n\nA: \"Middle-earth\" is a modern version of the word \"Midgard\" which in Norse mythology is the world that humans live on (there are eight others). Tolkien was known to derive some of the mythology of Middle-earth from Norse mythology (including parts of the Ainulindalë, the creation scene at the beginning of the Silmarillion).\n"]], "corpus": "stackexchange"}
+{"tstamp": 1724147279.1875, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1724147278.9522, "finish": 1724147279.1875, "ip": "", "conv_id": "db25d91c18d2409ab674107856390073", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "is asgard a real planet?", "output": [["is asgard a real planet?", "Scifi Stackexchange\n\nQ: What’s the official in-universe classification of Thor and other Asgardians in the MCU? What’s the official in-universe classification of Asgardians in the MCU?\nBy official, I mean what category does S.H.I.E.L.D. put them into, i.e. “Alien,” “Human” or do they just designate them as “Gods?”\n\nA: S.H.I.E.L.D. likely classifies Asgardians as aliens, judging by this dialogue from an early episode of Agents of S.H.I.E.L.D..\n\nSKYE: So, Asgardians are aliens from another planet that visited us thousands of years ago?\nAGENT COULSON: Or more.\nSKYE: And because we couldn't understand aliens, we thought they were gods?\nAGENT COULSON: That's where our Norse mythology comes from.\nSKYE: [sighs] That's too crazy. Do you think other deities are aliens, too? Vishnu for sure, right?\nAgents of S.H.I.E.L.D. - S01E08 - \"The Well\"\n\nOdin probably would've agreed with this classification, since he clearly stated that MCU Asgardians aren't gods.\n\nLOKI: I went down to Midgard to rule the people of Earth as a benevolent god. Just like you.\nODIN: We are not gods. We are born, we live, we die. Just as humans do.\nLOKI: Give or take 5000 years.\nThor: The Dark World (2013)\n\n"]], "corpus": "stackexchange"}
+{"tstamp": 1724147316.5565, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1724147315.7701, "finish": 1724147316.5565, "ip": "", "conv_id": "b1622a49d84d41de81be59458d957680", "model_name": "BM25", "prompt": "How to combine GPT-2 and BERT for better question generation and answering?", "output": [["How to combine GPT-2 and BERT for better question generation and answering?", "Title: Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds\n\nAbstract: Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime."]], "corpus": "arxiv"}
+{"tstamp": 1724147316.5565, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1724147315.7701, "finish": 1724147316.5565, "ip": "", "conv_id": "a1b61891a5fc41888a6df192888fa1c6", "model_name": "text-embedding-3-large", "prompt": "How to combine GPT-2 and BERT for better question generation and answering?", "output": [["How to combine GPT-2 and BERT for better question generation and answering?", "Title: Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds\n\nAbstract: Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime."]], "corpus": "arxiv"}
+{"tstamp": 1724147350.4213, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1724147348.2289, "finish": 1724147350.4213, "ip": "", "conv_id": "ee7d7009cb09443086721d839839d30b", "model_name": "intfloat/e5-mistral-7b-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 use full brained humans as batteries? \nPossible Duplicate:\nIs the basic premise of humans as a power source in The Matrix reasonable?\nWhy Did the Machines Even Bother With a Matrix? \n\nThe machines in The Matrix series use humans as batteries. That's OK. But why did the machines not alter their livestock such that the matrix itself would be unnecessary?  \nConsider the recently proposed headless chicken farm. Something similar for the human batteries would seem to be an ideal solution for logical machines. No longer would they need to spend resources maintaining a virtual reality for their batteries. The energy, physical hardware and programs running the matrix could then be put to who knows what other uses.\n\nA: The explanation for the use of human brains would seem to be the fact that our complex thoughts lead to higher level of brain activity, thus resulting in exponentially more synapses firing (i.e. more electrical activity to harvest).\nThat makes human brains far more efficient than the brains of animals, even with the overhead of maintaining the matrix.  Plus, that movie would have really sucked... dumb cows and chickens revolt against the machines!  ;)\n"]], "corpus": "stackexchange"}
+{"tstamp": 1724147350.4213, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1724147348.2289, "finish": 1724147350.4213, "ip": "", "conv_id": "b1e3a8689e72427ea133d86987f91a26", "model_name": "BAAI/bge-large-en-v1.5", "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: Do the Machines in \"The Matrix\" Adhere to the Three Laws of Robotics? In the \"real world\" of the Matrix, do the machines follow Asimov's Three Rules? Were they even programmed with them?\nEDIT: I realize that killing people goes against the 1st law, but by keeping people in the Matrix (which the machines view as a symbiosis) rather than simply killing off these \"annoying\" creatures, I wonder if the killings can be seen as \"preserving the greater good\" or something along those lines; and thus justify violating the laws.\n\nA: \nA robot may not injure a human being or, through inaction, allow a\n  human being to come to harm.\nA robot must obey the orders given to it by human beings, except where\n  such orders would conflict with the First Law.\nA robot must protect its own existence as long as such protection does\n  not conflict with the First or Second Laws.\n\nNo, no, and no.\n"]], "corpus": "stackexchange"}