{"tstamp": 1722304978.8845, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722304977.8763, "finish": 1722304978.8845, "ip": "", "conv_id": "559be19d148045bd97deb6c2be8ad7ee", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Research on modifying sentence sentiment without altering the core content through a two-step masking and infilling approach.", "output": [["Research on modifying sentence sentiment without altering the core content through a two-step masking and infilling approach.", "Title: \"Mask and Infill\" : Applying Masked Language Model to Sentiment Transfer\n\nAbstract: This paper focuses on the task of sentiment transfer on non-parallel text, which modifies sentiment attributes (e.g., positive or negative) of sentences while preserving their attribute-independent content. Due to the limited capability of RNNbased encoder-decoder structure to capture deep and long-range dependencies among words, previous works can hardly generate satisfactory sentences from scratch. When humans convert the sentiment attribute of a sentence, a simple but effective approach is to only replace the original sentimental tokens in the sentence with target sentimental expressions, instead of building a new sentence from scratch. Such a process is very similar to the task of Text Infilling or Cloze, which could be handled by a deep bidirectional Masked Language Model (e.g. BERT). So we propose a two step approach \"Mask and Infill\". In the mask step, we separate style from content by masking the positions of sentimental tokens. In the infill step, we retrofit MLM to Attribute Conditional MLM, to infill the masked positions by predicting words or phrases conditioned on the context1 and target sentiment. We evaluate our model on two review datasets with quantitative, qualitative, and human evaluations. Experimental results demonstrate that our models improve state-of-the-art performance."]], "corpus": "arxiv"}
{"tstamp": 1722304978.8845, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722304977.8763, "finish": 1722304978.8845, "ip": "", "conv_id": "71e05d6cfbba477b9531770801bfdb37", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Research on modifying sentence sentiment without altering the core content through a two-step masking and infilling approach.", "output": [["Research on modifying sentence sentiment without altering the core content through a two-step masking and infilling approach.", "Title: Phonon Assisted Multimagnon Optical Absorption and Long Lived Two-Magnon States in Undoped Lamellar Copper Oxides\n\nAbstract: We calculate the effective charge for multimagnon infrared (IR) absorption assisted by phonons in the parent insulating compounds of cuprate superconductors and the spectra for two-magnon absorption using interacting spin-wave theory. Recent measured bands in the mid IR [Perkins et al. Phys. Rev. Lett. {\\bf 71} 1621 (1993)] are interpreted as involving one phonon plus a two-magnon virtual bound state, and one phonon plus higher multimagnon absorption processes. The virtual bound state consists of a narrow resonance occurring when the magnon pair has total momentum close to $(\\pi,0)$."]], "corpus": "arxiv"}
{"tstamp": 1722304986.0474, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722304985.1247, "finish": 1722304986.0474, "ip": "", "conv_id": "8307fc5bf26b463e864d9ff494a6ca1a", "model_name": "GritLM/GritLM-7B", "prompt": "who sings baby it's cold outside original", "output": [["who sings baby it's cold outside original", "Baby, It's Cold Outside\n\nDinah Shore and Buddy Clark with Ted Dale and his orchestra; recorded on March 17 and released by Columbia Records (peaked at No. 3 on Billboard Records Most Played By Disk Jockeys chart, at No. 4 on Billboard Best-Selling Popular Retail Records chart, and No. 6 on Billboard Most-Played Juke Box Records chart in mid-1949)\nMargaret Whiting and Johnny Mercer with Paul Weston and his orchestra; recorded on March 18 and released by Capitol Records (peaked at No. 3 on Billboard Records Most Played By Disk Jockeys chart, at No. 4 on Billboard Best-Selling Popular Retail Records chart [lasting 19 weeks on the chart], and No. 8 on Billboard Most-Played Juke Box Records chart in mid-1949)\nOther recordings\nAs of 2020, there are over 400 recordings of the song. The following list is incomplete.\nCharts\nDean Martin version\nRay Charles and Betty Carter version\nTom Jones and Cerys Matthews version\nElla Fitzgerald and Louis Jordan version\nGlee Cast version\nWillie Nelson and Norah Jones version\nShe & Him version\nLady Antebellum / Lady A version\nKelly Clarkson and Ronnie Dunn version\nIdina Menzel and Michael Bublé version\nBrett Eldredge and Meghan Trainor version\nJohn Legend and Kelly Clarkson version\nYear-end charts\nIdina Menzel and Michael Bublé version\nBrett Eldredge and Meghan Trainor version\nCertifications\nDean Martin version\nIdina Menzel and Michael Bublé version\nBrett Eldredge and Meghan Trainor version"]], "corpus": "wikipedia"}
{"tstamp": 1722304986.0474, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722304985.1247, "finish": 1722304986.0474, "ip": "", "conv_id": "4a6085badadb42d6880032af43e5dd2f", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "who sings baby it's cold outside original", "output": [["who sings baby it's cold outside original", "Baby, It's Cold Outside\n\n\"Baby, It's Cold Outside\" is a popular song written by Frank Loesser in 1944 and popularized in the 1949 film Neptune's Daughter. While the lyrics make no mention of a holiday, it is commonly regarded as a Christmas song owing to its winter theme.  The song was released in eight recordings in 1949—including well-known versions by Ella Fitzgerald and Louis Jordan, and by Dean Martin and Marilyn Maxwell—and has been covered numerous times since.\nHistory\nIn 1944, Loesser wrote \"Baby, It's Cold Outside\" to sing with his wife, Lynn Garland, at their housewarming party in New York City at the Navarro Hotel. They sang the song to indicate to guests that it was time to leave. Garland has written that after the first performance, \"We became instant parlor room stars. We got invited to all the best parties for years on the basis of 'Baby.' It was our ticket to caviar and truffles. Parties were built around our being the closing act.\" In 1948, after years of performing the song, Loesser sold it to MGM for the 1949 romantic comedy Neptune's Daughter. Garland was furious: \"I felt as betrayed as if I'd caught him in bed with another woman.\""]], "corpus": "wikipedia"}
{"tstamp": 1722304990.98, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722304990.7411, "finish": 1722304990.98, "ip": "", "conv_id": "c33f55221d004dba9557c88d0b09ce3d", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "are doughy cookies safe to eat?", "output": [["are doughy cookies safe to eat?", "Stackoverflow Stackexchange\n\nQ: pharo creating a global variable like transcript I want to create a variable named MameGap which is accessible from every where.\nI can print some words to transcript for ex. from a method of a class. I want to do this for MameGap too.\nI tried something like this \nMameGap:= MyClass new.\nSmalltalk at: #myMap put: MameGap.\n\nI want to access MameGap like this\ndoSomething: aVar\n   |x|\n   x:= MameGap getInt.\n   ^x*3\n\n\nA: You have to do:\nSmalltalk at: #MameGap put: MyClass new\n\nalso you can put there just a class object, like \n Smalltalk at: #MameGap put: MyClass\n\nand sen to it class-side messages\n"]], "corpus": "stackexchange"}
{"tstamp": 1722304990.98, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722304990.7411, "finish": 1722304990.98, "ip": "", "conv_id": "15e46770ed5144848b667dc921b96344", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "are doughy cookies safe to eat?", "output": [["are doughy cookies safe to eat?", "Stackoverflow Stackexchange\n\nQ: Results from query as argument for WHERE statement in MySQL I have two tables: orders and orderProducts. They both have a column called 'order_id'.\norders has a column named 'date_created'\nordersProducts has a column named 'SKU'\nI want to SELECT SKUs in within a date range.\nMy query so far is:\nSELECT `SKU`\nFROM `orderProducts`\nINNER JOIN orders\nON orderproducts.order_id = orders.order_id\nWHERE orders.order_id in (SELECT id FROM orders WHERE date_created BETWEEN '2014-10-01' AND '2015-03-31' ORDER BY date_created DESC)\n\nThe query runs but it returns nothings. What am I missing here?\n\nA: Try putting date condition in the where clause, there is no need for the subquery:\nselect op.`SKU`\nfrom `orderProducts` op\njoin `orders` o using(`order_id`)\nwhere o.`date_created` between '2014-10-01' and '2015-03-31'\n\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305000.0153, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722304999.8039, "finish": 1722305000.0153, "ip": "", "conv_id": "100375864a8944319e64d77e419c756d", "model_name": "GritLM/GritLM-7B", "prompt": "what emperor took over france after the reign of terror", "output": [["what emperor took over france after the reign of terror", "History of Europe\n\nOn 20 September 1792 the National Convention abolished the monarchy and declared France a republic. Due to the emergency of war, the National Convention created the Committee of Public Safety to act as the country's executive. Under Maximilien de Robespierre, the committee initiated the Reign of Terror, during which up to 40,000 people were executed in Paris, mainly nobles and those convicted by the Revolutionary Tribunal, often on the flimsiest of evidence. Internal tensions at Paris drove the Committee towards increasing assertions of radicalism and increasing suspicions. A few months into this phase, more and more prominent revolutionaries were being sent to the guillotine by Robespierre and his faction, for example Madame Roland and Georges Danton. Elsewhere in the country, counter-revolutionary insurrections were brutally suppressed. The regime was overthrown in the coup of 9 Thermidor (27 July 1794) and Robespierre was executed. The regime which followed ended the Terror and relaxed Robespierre's more extreme policies.\nNapoleon\nNapoleon Bonaparte was France's most successful general in the Revolutionary wars. In 1799 on 18 Brumaire (9 November) he overthrew the government, replacing it with the Consulate, which he dominated. He gained popularity in France by restoring the Church, keeping taxes low, centralizing power in Paris, and winning glory on the battlefield. In 1804 he crowned himself Emperor. In 1805, Napoleon planned to invade Britain, but a renewed British alliance with Russia and Austria (Third Coalition), forced him to turn his attention towards the continent, while at the same time the French fleet was demolished by the British at the Battle of Trafalgar, ending any plan to invade Britain. On 2 December 1805, Napoleon defeated a numerically superior Austro-Russian army at Austerlitz, forcing Austria's withdrawal from the coalition (see Treaty of Pressburg) and dissolving the Holy Roman Empire. In 1806, a Fourth Coalition was set up. On 14 October Napoleon defeated the Prussians at the Battle of Jena-Auerstedt, marched through Germany and defeated the Russians on 14 June 1807 at Friedland. The Treaties of Tilsit divided Europe between France and Russia and created the Duchy of Warsaw."]], "corpus": "wikipedia"}
{"tstamp": 1722305000.0153, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722304999.8039, "finish": 1722305000.0153, "ip": "", "conv_id": "bad28d9bdb47406f9cfd72c620633507", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "what emperor took over france after the reign of terror", "output": [["what emperor took over france after the reign of terror", "Emperor of the French\n\nNapoleon's reign lasted until 22 June 1815, when he was defeated at the Battle of Waterloo, exiled, and imprisoned on the island of Saint Helena, where he died on 5 May 1821. His reign was interrupted by the Bourbon Restoration of 1814 and his exile to Elba, from where he escaped less than a year later to reclaim the throne, reigning as Emperor for another 111 days before his final defeat and exile.\nLess than a year after the 1851 French coup d'état by Napoleon's nephew Louis-Napoléon Bonaparte, which ended in the successful dissolution of the French National Assembly, the French Second Republic was transformed into the Second French Empire, established by a referendum on 7 November 1852. President Bonaparte, elected by the French people, officially became Napoleon III, Emperor of the French, from the symbolic and historic date of 2 December 1852. His rule would de facto end on 28 July 1870 - the power of the head of state was transferred to his wife Eugenie de Montijo who would rule as empress regent of France while Napoleon III left with his army. His reign would nominally continue until 4 September 1870, as he was officially deposed after his defeat and capture at the Battle of Sedan during the Franco-Prussian War. In March 1871, he would be released from Prussian custody and exiled to the United Kingdom, where he died on 9 January 1873."]], "corpus": "wikipedia"}
{"tstamp": 1722305078.6563, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305078.4572, "finish": 1722305078.6563, "ip": "", "conv_id": "053ee0d3184146fda38e9bbb641a0f03", "model_name": "GritLM/GritLM-7B", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
{"tstamp": 1722305178.7998, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305178.5602, "finish": 1722305178.7998, "ip": "", "conv_id": "a721ba1ec728453388b30d0efed8f994", "model_name": "GritLM/GritLM-7B", "prompt": "what is the difference between an electronic signal and an electromagnetic signal?", "output": [["what is the difference between an electronic signal and an electromagnetic signal?", "Gaming Stackexchange\n\nQ: What's the difference between semaphore and electric signals? In OpenTTD you can use electric signals and semaphore signals. In every instance I've ever seen people use electric signals to direct their trains. On the wiki the only mention of semaphores is \"don't hold down control otherwise you'll end up with a semaphore\". This suggests to me that semaphores are unwanted and have a different function to normal signals that is only applicable in complex circumstances. I couldn't find anything on the internet detailing their function. \nWhat is the functional difference between semaphore and electric signals?\n\nA: There is no functional difference between the two. By default the game will construct semaphores before a certain date defined in the Advanced Game Options > Construction section, to mimic the introduction of electrical signals in real life. Holding down Ctrl overrides that and builds semaphores instead. \n"]], "corpus": "stackexchange"}
{"tstamp": 1722305178.7998, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722305178.5602, "finish": 1722305178.7998, "ip": "", "conv_id": "03037905b8a749e399e111e32c19baee", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "what is the difference between an electronic signal and an electromagnetic signal?", "output": [["what is the difference between an electronic signal and an electromagnetic signal?", "Askubuntu Stackexchange\n\nQ: Trouble Installing libxp6 and multi-arch-support I am following this guide, but I have run into some difficulties at step 11. When I type\nsudo apt-get install ./libxp6_1.0.2-2_amd64.deb\n\nI get\nReading package lists... Done\nBuilding dependency tree       \nReading state information... Done\nNote, selecting 'libxp6' instead of './libxp6_1.0.2-2_amd64.deb'\nSome packages could not be installed. This may mean that you have\nrequested an impossible situation or if you are using the unstable\ndistribution that some required packages have not yet been created\nor been moved out of Incoming.\nThe following information may help to resolve the situation:\n\nThe following packages have unmet dependencies:\n libxp6 : PreDepends: multiarch-support but it is not installable\nE: Unable to correct problems, you have held broken packages.\n\nlibxp6 and multiarch-support are what seem to be giving me difficulties. Searching on the web has given me a little, but not enough to install. Even the Ubuntu package website isn't helping.\nAny help would be appreciated.\n\nA: Then you have to install multiarch-support package from 18.04 LTS first with\ncd ~/Downloads\nwget http://archive.ubuntu.com/ubuntu/pool/main/g/glibc/multiarch-support_2.27-3ubuntu1_amd64.deb\nsudo apt-get install ./multiarch-support_2.27-3ubuntu1_amd64.deb\n\nand continue to follow the guide.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305223.7026, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305223.4442, "finish": 1722305223.7026, "ip": "", "conv_id": "9f4dfa402ea64c74bfec3768569b6d00", "model_name": "GritLM/GritLM-7B", "prompt": "How can deep learning be used to design communication systems as end-to-end reconstruction tasks?  ", "output": [["How can deep learning be used to design communication systems as end-to-end reconstruction tasks?  ", "Title: An Introduction to Deep Learning for the Physical Layer\n\nAbstract: We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. The paper is concluded with a discussion of open challenges and areas for future investigation."]], "corpus": "arxiv"}
{"tstamp": 1722305223.7026, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722305223.4442, "finish": 1722305223.7026, "ip": "", "conv_id": "fb3390a4534f41758663e225ee7ed9a6", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "How can deep learning be used to design communication systems as end-to-end reconstruction tasks?  ", "output": [["How can deep learning be used to design communication systems as end-to-end reconstruction tasks?  ", "Title: Design of Communication Systems using Deep Learning: A Variational Inference Perspective\n\nAbstract: Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the transmitter and decoder at the receiver and train them jointly by modeling transmit symbols as latent codes from the encoder. However, in communication systems, the receiver has to work with noise corrupted versions of transmit symbols. Traditional autoencoders are not designed to work with latent codes corrupted with noise. In this work, we provide a framework to design end to end communication systems which accounts for the existence of noise corrupted transmit symbols. The proposed method uses deep neural architecture. An objective function for optimizing these models is derived based on the concepts of variational inference. Further, domain knowledge such as channel type can be systematically integrated into the objective. Through numerical simulation, the proposed method is shown to consistently produce models with better packing density and achieving it faster in multiple popular channel models as compared to the previous works leveraging deep learning models."]], "corpus": "arxiv"}
{"tstamp": 1722305229.7963, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305229.6152, "finish": 1722305229.7963, "ip": "", "conv_id": "6cf9d4db20f8436c8c7f2ccaca29d2fa", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "what is the difference between cluster point and limit point?", "output": [["what is the difference between cluster point and limit point?", "Askubuntu Stackexchange\n\nQ: How to enable user sharing per instructions? I have this entry in .xsession-errors log\n\"Nautilus-Share-Message: Called \"net usershare info\" but it failed: 'net usershare' returned \nerror 255: net usershare: cannot open usershare directory /var/lib/samba/usershares.         \nError No such file or directory\nPlease ask your system administrator to enable user sharing.\n\nThis is in relation to my applets having to be reloaded after every boot. Just wondering how I would enable user sharing, and how it affects my applets ??\n\nA: I don't think this effects your applets at all, that's probably a different bug. You could just make the directory with sudo:\nsudo mkdir -p /var/lib/samba/usershares\n\nBut I don't really know what it will do.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305229.7963, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722305229.6152, "finish": 1722305229.7963, "ip": "", "conv_id": "38308d8cc72f4a24b8ddc6bafec79a96", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "what is the difference between cluster point and limit point?", "output": [["what is the difference between cluster point and limit point?", "Math Stackexchange\n\nQ: Limit and limit points What is the basic difference between limit and limit points, and if a sequence has one unique limit how it can have a lot of limit points \n\nA: A limit point is a generalization of a limit (each limit is a limit point but not vice versa). You can see this in the definition:\nLimit: $a$ is a limit of $(a_n)$, iff in each neighborhood of $a$ are almost all elements of $(a_n)$.\nLimit point: $a$ is a limit of $(a_n)$, iff in each neighborhood of $a$ are infinite elements of $(a_n)$.\nI highlighted the difference between both definitions.\nBecause only infinite elements need to be in any neighborhood of $a$. There can be infinite elements outside this neighborhood which can clustered to another limit point. This is the reason why there might be many limit points for a sequence. If $a$ is a limit only finite elements can be outside any given neighborhood which is not enough to go to another limit.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305252.6945, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722305252.201, "finish": 1722305252.6945, "ip": "", "conv_id": "d56443b4b3064e69a3fc99eabc8fbf8f", "model_name": "BM25", "prompt": "mass effect 1 how many skill points total?", "output": [["mass effect 1 how many skill points total?", "Gaming Stackexchange\n\nQ: How many skill points will I have at max level? What is the maximum level in Mass Effect 3?\nHow many skill points will Shepard have at max level? \nIf NPC companions earn a different number of skill points than Shepard, what's the number they'll have at max level?\n\nA: Since this question seems to have been deemed the Canonical by mods:\nThe maximum character level is 60. At this level, Shepard will have earned a total of 181 Skill Points.\nMaxing a single skill requires 21 points.\nThere are enough points in the game to max out a total of 8 skills, with a few left over.\nShepard, conveniently enough, has 8 skills, plus one slot for a Bonus Power.\nSo, you can come very close to maxing out all skills, but you can't quite make it to maxing out all 9. You can max out 8 of them, and have 13 points remaining for the 9th, which lets you get to rank 4. You'll have 2 points left over. Alternately, you could max out 7 skills, get two to rank 5, and have 3 points left over.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305252.6945, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722305252.201, "finish": 1722305252.6945, "ip": "", "conv_id": "2766c8817d564f3fa47a1aa8eeaa0b03", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "mass effect 1 how many skill points total?", "output": [["mass effect 1 how many skill points total?", "Gaming Stackexchange\n\nQ: How many skill points will I have at max level? What is the maximum level in Mass Effect 3?\nHow many skill points will Shepard have at max level? \nIf NPC companions earn a different number of skill points than Shepard, what's the number they'll have at max level?\n\nA: Since this question seems to have been deemed the Canonical by mods:\nThe maximum character level is 60. At this level, Shepard will have earned a total of 181 Skill Points.\nMaxing a single skill requires 21 points.\nThere are enough points in the game to max out a total of 8 skills, with a few left over.\nShepard, conveniently enough, has 8 skills, plus one slot for a Bonus Power.\nSo, you can come very close to maxing out all skills, but you can't quite make it to maxing out all 9. You can max out 8 of them, and have 13 points remaining for the 9th, which lets you get to rank 4. You'll have 2 points left over. Alternately, you could max out 7 skills, get two to rank 5, and have 3 points left over.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305639.8104, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722305639.6393, "finish": 1722305639.8104, "ip": "", "conv_id": "bc1a7d6500334478abf4839584bbeaec", "model_name": "GritLM/GritLM-7B", "prompt": "How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "output": [["How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "Title: Brain Network Transformer\n\nAbstract: Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data, we model brain networks as graphs with nodes of fixed size and order, which allows us to (1) use connection profiles as node features to provide natural and low-cost positional information and (2) learn pair-wise connection strengths among ROIs with efficient attention weights across individuals that are predictive towards downstream analysis tasks. Moreover, we propose an Orthonormal Clustering Readout operation based on self-supervised soft clustering and orthonormal projection. This design accounts for the underlying functional modules that determine similar behaviors among groups of ROIs, leading to distinguishable cluster-aware node embeddings and informative graph embeddings. Finally, we re-standardize the evaluation pipeline on the only one publicly available large-scale brain network dataset of ABIDE, to enable meaningful comparison of different models. Experiment results show clear improvements of our proposed Brain Network Transformer on both the public ABIDE and our restricted ABCD datasets. The implementation is available at https://github.com/Wayfear/BrainNetworkTransformer."]], "corpus": "arxiv"}
{"tstamp": 1722305639.8104, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305639.6393, "finish": 1722305639.8104, "ip": "", "conv_id": "0848dc0be9b94d688857e785d82e9f51", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "output": [["How does the Brain Network Transformer leverage connection profiles and orthonormal clustering to analyze mental disorders?", "Title: Noncompact, Gauge-Invariant Simulations of U(1), SU(2), and SU(3)\n\nAbstract: We have applied a new noncompact, gauge-invariant, Monte Carlo method to simulate the U(1), SU(2), and SU(3) gauge theories on 8^4 and 12^4 lattices. For U(1) the Creutz ratios of the Wilson loops agree with the exact results for beta > 0.5 after a renormalization of the charge. The SU(2) and SU(3) Creutz ratios robustly display quark confinement at beta = 0.5 and beta = 2, respectively. At much weaker coupling, the SU(2) and SU(3) Creutz ratios agree with perturbation theory after a renormalization of the coupling constant. For SU(3) without quarks, our lattice QCD parameter is Lambda_L = 130 +- 18$ MeV."]], "corpus": "arxiv"}
{"tstamp": 1722305896.3763, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722305894.1942, "finish": 1722305896.3763, "ip": "", "conv_id": "ebb67aaa6367404fada798faa16238be", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "can gravity waves travel faster than light?", "output": [["can gravity waves travel faster than light?", "Physics Stackexchange\n\nQ: Why can't the speed of gravitational waves be greater than the speed of light if the universe can expand faster than the speed of light? Since the expansion speed of the universe can be greater than the speed of light, why can't gravitational waves, which also uses space as the medium, travel faster than the speed of light?\n\nA: Gravitational waves are solutions to the linearized field equations\n$\\Box h_{\\mu \\nu} = 0,$\nwhere $\\Box$ is the d'Alembertian differential operator.\nThey are of the form\n\\begin{align}\nh_{\\mu \\nu}= \\text{Re}[H_{\\mu \\nu} e^{i k_{\\rho} x^{\\rho}}] , \n\\end{align}\nwhere $H_{\\mu \\nu}$ is just a complex symmetric matrix setting the polarization of the wave and $k_{\\rho}$ is its wave-vector.\nFor the above ansatz to satisfy the equations, we plug it into the equations to get the condition $k_{\\mu} k^{\\mu} =0$. This is just the statement that the wave-vector must be null, meaning the wave propagates at the speed of light.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722305896.3763, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722305894.1942, "finish": 1722305896.3763, "ip": "", "conv_id": "0594d3384acf4283a4656c03b325081a", "model_name": "BM25", "prompt": "can gravity waves travel faster than light?", "output": [["can gravity waves travel faster than light?", "Physics Stackexchange\n\nQ: Why can't fermions be affected by effective gravity in non-linear quantum electrodynamics? Quantum electrodynamics based upon Euler-Heisenberg or Born-Infeld Lagrangians predict photons\nto move according to an effective metric which is dependent on the background electromagnetic\nfield. In other words, photon trajectories are curved in presence of electromagnetic fields,\nmeaning that an effective gravity is acting upon. If part of fermion masses is allegedly of\nelectromagnetic origin, the question why their trajectories are not affected by this\neffective gravity naturally comes to mind.\n\nA: In the presence of a background electromagnetic field, electromagnetic fields travel along a deformed light cone which is smaller than the \"relativistic light cone\". However, charged fermions can still travel faster than electromagnetic waves as long as they are still slower than the \"relativistic speed of light\". They emit Cherenkov radiation while doing so. \n"]], "corpus": "stackexchange"}
{"tstamp": 1722306330.1591, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722306329.8317, "finish": 1722306330.1591, "ip": "", "conv_id": "5993407c26d04c54981a49217a38518d", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "when did scotland last qualify for world cup", "output": [["when did scotland last qualify for world cup", "Scotland national football team\n\nScotland lost just two of their first 43 international matches. It was not until a 2–0 home defeat by Ireland in 1903 that Scotland lost a match to a team other than England. This run of success meant that Scotland would have regularly topped the Elo ratings, which were calculated in 1998, between 1876 and 1904. Scotland won the British Home Championship outright on 24 occasions, and shared the title 17 times with at least one other team. A noteworthy victory for Scotland before the Second World War was the 5–1 victory over England in 1928, which led to that Scotland side being known as the \"Wembley Wizards\". Scotland played their first match outside the British Isles in 1929, beating Norway 7–3 in Bergen. Scotland continued to contest regular friendly matches against European opposition and enjoyed wins against Germany and France before losing to the Austrian \"Wunderteam\" and Italy in 1931.\nScotland, like the other Home Nations, did not enter the three FIFA World Cups held during the 1930s. This was because the four associations had been excluded from FIFA due to a disagreement regarding the status of amateur players. The four associations, including Scotland, returned to the FIFA fold after the Second World War. A match between a United Kingdom team and a \"Rest of the World\" team was played at Hampden Park in 1947 to celebrate this reconciliation.\n1950s: Entering the World Cup"]], "corpus": "wikipedia"}
{"tstamp": 1722306330.1591, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722306329.8317, "finish": 1722306330.1591, "ip": "", "conv_id": "940948f56e9c4f5297cb17f69935bd8b", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "when did scotland last qualify for world cup", "output": [["when did scotland last qualify for world cup", "Scotland at the FIFA World Cup\n\nThe World Cup consists of two parts, the qualification phase and the final phase (officially called the World Cup Finals). The qualification phase, which currently take place over the three years preceding the Finals, is used to determine which teams qualify for the Finals. The current format of the Finals involves 32 teams competing at venues within the host nation (or nations) over a period of about a month. The World Cup Finals is the most widely viewed sporting event in the world, with an estimated 715.1 million people watching the 2006 tournament final.\nHistory\n1930s\nScotland did not compete in the first three World Cup competitions, in 1930, 1934 and 1938. Because of a dispute with FIFA over \"broken-time\" payments to players, the SFA, with the football associations of England, Ireland and Wales, withdrew from FIFA in 1928, and did not rejoin as a permanent member until 1946.\n1950s\nThe readmission of the Scottish Football Association to FIFA in 1946 meant that Scotland were now eligible to enter the 1950 FIFA World Cup. FIFA advised that places would be awarded to the top two teams in the 1949–50 British Home Championship, but the SFA announced that Scotland would attend the finals only if Scotland won the competition. Scotland won their first two matches, but a 1–0 home defeat by England meant that the Scots finished as runners-up. The SFA stood by this proclamation, despite pleas to the contrary by the Scotland players, supported by England captain Billy Wright and the other England players. Sportswriter Brian Glanville later described the SFA as showing \"baffling insularity and pique\" in their refusal to send a team to the World Cup."]], "corpus": "wikipedia"}
{"tstamp": 1722306358.068, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722306357.4875, "finish": 1722306358.068, "ip": "", "conv_id": "be3fc968a3aa487ba8db65457d91fec3", "model_name": "text-embedding-3-large", "prompt": "Methods for tracing dataset provenance and legal implications in AI training data  ", "output": [["Methods for tracing dataset provenance and legal implications in AI training data  ", "Title: The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI\n\nAbstract: The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tools and standards to trace the lineage of these datasets, from their source, creators, series of license conditions, properties, and subsequent use. Our landscape analysis highlights the sharp divides in composition and focus of commercially open vs closed datasets, with closed datasets monopolizing important categories: lower resource languages, more creative tasks, richer topic variety, newer and more synthetic training data. This points to a deepening divide in the types of data that are made available under different license conditions, and heightened implications for jurisdictional legal interpretations of copyright and fair use. We also observe frequent miscategorization of licenses on widely used dataset hosting sites, with license omission of 70%+ and error rates of 50%+. This points to a crisis in misattribution and informed use of the most popular datasets driving many recent breakthroughs. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire audit, with an interactive UI, the Data Provenance Explorer, which allows practitioners to trace and filter on data provenance for the most popular open source finetuning data collections: www.dataprovenance.org."]], "corpus": "arxiv"}
{"tstamp": 1722306358.068, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722306357.4875, "finish": 1722306358.068, "ip": "", "conv_id": "ff92d927e7a1440f91ca6ddae1fb40a5", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Methods for tracing dataset provenance and legal implications in AI training data  ", "output": [["Methods for tracing dataset provenance and legal implications in AI training data  ", "Title: The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI\n\nAbstract: The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tools and standards to trace the lineage of these datasets, from their source, creators, series of license conditions, properties, and subsequent use. Our landscape analysis highlights the sharp divides in composition and focus of commercially open vs closed datasets, with closed datasets monopolizing important categories: lower resource languages, more creative tasks, richer topic variety, newer and more synthetic training data. This points to a deepening divide in the types of data that are made available under different license conditions, and heightened implications for jurisdictional legal interpretations of copyright and fair use. We also observe frequent miscategorization of licenses on widely used dataset hosting sites, with license omission of 70%+ and error rates of 50%+. This points to a crisis in misattribution and informed use of the most popular datasets driving many recent breakthroughs. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire audit, with an interactive UI, the Data Provenance Explorer, which allows practitioners to trace and filter on data provenance for the most popular open source finetuning data collections: www.dataprovenance.org."]], "corpus": "arxiv"}
{"tstamp": 1722306925.7354, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722306925.3084, "finish": 1722306925.7354, "ip": "", "conv_id": "f027bc84f35a4d748c67b44a2435bbe4", "model_name": "voyage-multilingual-2", "prompt": "What is a transformer?", "output": [["What is a transformer?", "Title: Formal Algorithms for Transformers\n\nAbstract: This document aims to be a self-contained, mathematically precise overview of transformer architectures and algorithms (*not* results). It covers what transformers are, how they are trained, what they are used for, their key architectural components, and a preview of the most prominent models. The reader is assumed to be familiar with basic ML terminology and simpler neural network architectures such as MLPs."]], "corpus": "arxiv"}
{"tstamp": 1722306971.5819, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722306971.4007, "finish": 1722306971.5819, "ip": "", "conv_id": "a55ff04a15fd4107ac10365fbb0eb9a2", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "where did the british empire control an entire continent", "output": [["where did the british empire control an entire continent", "British Empire\n\nThe British Empire comprised the dominions, colonies, protectorates, mandates, and other territories ruled or administered by the United Kingdom and its predecessor states. It began with the overseas possessions and trading posts established by England in the late 16th and early 17th centuries. At its height in the 19th and early 20th centuries, it was the largest empire in history and, for a century, was the foremost global power. By 1913, the British Empire held sway over 412 million people,  of the world population at the time, and by 1920, it covered ,  of the Earth's total land area. As a result, its constitutional, legal, linguistic, and cultural legacy is widespread. At the peak of its power, it was described as \"the empire on which the sun never sets\", as the sun was always shining on at least one of its territories.\nDuring the Age of Discovery in the 15th and 16th centuries, Portugal and Spain pioneered European exploration of the globe, and in the process established large overseas empires. Envious of the great wealth these empires generated, England, France, and the Netherlands began to establish colonies and trade networks of their own in the Americas and Asia. A series of wars in the 17th and 18th centuries with the Netherlands and France left England (Britain, following the 1707 Act of Union with Scotland) the dominant colonial power in North America. Britain became a major power in the Indian subcontinent after the East India Company's conquest of Mughal Bengal at the Battle of Plassey in 1757."]], "corpus": "wikipedia"}
{"tstamp": 1722306971.5819, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722306971.4007, "finish": 1722306971.5819, "ip": "", "conv_id": "a6afd35e6fa54368be503cb1c3cf43ea", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "where did the british empire control an entire continent", "output": [["where did the british empire control an entire continent", "Territorial evolution of the British Empire\n\nThe territorial evolution of the British Empire is considered to have begun with the foundation of the English colonial empire in the late 16th century.  Since then, many territories around the world have been under the control of the United Kingdom or its predecessor states.\nWhen the Kingdom of Great Britain was formed in 1707 by the union of the Kingdoms of Scotland and England, the latter country's colonial possessions passed to the new state. Similarly, when Great Britain was united with the Kingdom of Ireland in 1801 to form the United Kingdom, control over its colonial possessions passed to the latter state. Collectively, these territories are referred to as the British Empire. When much of Ireland gained independence in 1922 as the Irish Free State, the other territories of the empire remained under the control of the United Kingdom.\nFrom 1714 to 1837, the British throne was held by a series of kings who were also the rulers of the German state of Hanover.  However, this was purely a personal union, with Hanover maintaining its political independence otherwise, and so it is not usually considered to have formed part of the British Empire."]], "corpus": "wikipedia"}
{"tstamp": 1722306986.0121, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722306985.8309, "finish": 1722306986.0121, "ip": "", "conv_id": "734d33bae026481cab60cb1e123a36b4", "model_name": "GritLM/GritLM-7B", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Attention Is All You Need\n\nAbstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."]], "corpus": "arxiv"}
{"tstamp": 1722307003.9939, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722307003.6084, "finish": 1722307003.9939, "ip": "", "conv_id": "3c77700a297d4a26b9466b34301c929b", "model_name": "voyage-multilingual-2", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Visual Attention and its Intimate Links to Spatial Cognition\n\nAbstract: It is almost universal to regard attention as the facility that permits an agent, human or machine, to give priority processing resources to relevant stimuli while ignoring the irrelevant. The reality of how this might manifest itself throughout all the forms of perceptual and cognitive processes possessed by humans, however, is not as clear. Here we examine this reality with a broad perspective in order to highlight the myriad ways that attentional processes impact both perception and cognition. The paper concludes by showing two real world problems that exhibit sufficient complexity to illustrate the ways in which attention and cognition connect. These then point to new avenues of research that might illuminate the overall cognitive architecture of spatial cognition."]], "corpus": "arxiv"}
{"tstamp": 1722307006.8942, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722307006.267, "finish": 1722307006.8942, "ip": "", "conv_id": "fdbaddd493b84cc89357c4480e2d727d", "model_name": "voyage-multilingual-2", "prompt": "The effect of Long pimple ", "output": [["The effect of Long pimple ", "Pimple\n\nA pimple or zit is a kind of comedo that results from excess sebum and dead skin cells getting trapped in the pores of the skin. In its aggravated state, it may evolve into a pustule or papule. Pimples can be treated by acne medications, antibiotics, and anti-inflammatories prescribed by a physician, or various over the counter remedies purchased at a pharmacy.\nCauses\nSebaceous glands inside the pore of the skin produce sebum. When the outer layers of skin shed (a natural and continuous process, normally), dead skin and oily sebum left behind may bond together and form a blockage of the sebaceous gland at the base of the skin. This is most common when the skin becomes thicker at puberty. The sebaceous gland continues to produce sebum, which builds up behind the blockage, allowing bacteria to grow in the area, including the species Staphylococcus aureus and Cutibacterium acnes, which causes inflammation and infection. Other causes of pimples include family history, stress, fluctuations in hormone levels, hair and skincare products, medication side effects, and un-diagnosed or underlying medical conditions. Pimples can be part of the presentation of rosacea."]], "corpus": "wikipedia"}
{"tstamp": 1722307006.8942, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722307006.267, "finish": 1722307006.8942, "ip": "", "conv_id": "86047e9633f3430da6d888564b950f65", "model_name": "text-embedding-3-large", "prompt": "The effect of Long pimple ", "output": [["The effect of Long pimple ", "Table tennis grips and playing styles\n\nShort pips hitter This penhold style utilizes a short pips out rubber, usually on only one side of the racket with no backside rubber. Short pips hitters play over the table, hitting the ball very flatly as soon as it bounces off the table with the pips being largely unaffected by (or even negating) the majority of the opponent's spin. The advantage of hitting the ball flatly is that all of the player's power is transferred into the linear velocity of the ball, without expending any energy on creating spin. This means that the shots are very fast, and typically hard to block for the inexperienced player. However, the lack of a topspin Magnus effect means that the shots are harder to place on the table since they don't follow a downward arc trajectory, and the lack of angular momentum on the ball means that shots are less consistent and more sensitive to small amounts of wind or air pressure, often giving a knuckle ball effect.\nNotable penhold short pips hitters include 1996 Olympic Champion Liu Guoliang, 1985 and 1987 World Champion Jiang Jialiang, three-time Olympian Toshio Tasaki, Yang Ying, Kwak Bang-bang, Seok Eun-mi, He Zhi Wen, Lee Eun-hee, Wang Zeng Yi and Rory Cargill.\nShakehand playing styles"]], "corpus": "wikipedia"}
{"tstamp": 1722307064.6599, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722307064.0314, "finish": 1722307064.6599, "ip": "", "conv_id": "a10afac3a30a4e82b4163e080b19e9e9", "model_name": "text-embedding-3-large", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Is Attention All What You Need? -- An Empirical Investigation on Convolution-Based Active Memory and Self-Attention\n\nAbstract: The key to a Transformer model is the self-attention mechanism, which allows the model to analyze an entire sequence in a computationally efficient manner. Recent work has suggested the possibility that general attention mechanisms used by RNNs could be replaced by active-memory mechanisms. In this work, we evaluate whether various active-memory mechanisms could replace self-attention in a Transformer. Our experiments suggest that active-memory alone achieves comparable results to the self-attention mechanism for language modelling, but optimal results are mostly achieved by using both active-memory and self-attention mechanisms together. We also note that, for some specific algorithmic tasks, active-memory mechanisms alone outperform both self-attention and a combination of the two."]], "corpus": "arxiv"}
{"tstamp": 1722307104.0574, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722307103.5502, "finish": 1722307104.0574, "ip": "", "conv_id": "3fe38b7d37f6440f9394145fd67cb9a9", "model_name": "text-embedding-3-large", "prompt": "The effect of long Pimple ", "output": [["The effect of long Pimple ", "Table tennis grips and playing styles\n\nShort pips hitter This penhold style utilizes a short pips out rubber, usually on only one side of the racket with no backside rubber. Short pips hitters play over the table, hitting the ball very flatly as soon as it bounces off the table with the pips being largely unaffected by (or even negating) the majority of the opponent's spin. The advantage of hitting the ball flatly is that all of the player's power is transferred into the linear velocity of the ball, without expending any energy on creating spin. This means that the shots are very fast, and typically hard to block for the inexperienced player. However, the lack of a topspin Magnus effect means that the shots are harder to place on the table since they don't follow a downward arc trajectory, and the lack of angular momentum on the ball means that shots are less consistent and more sensitive to small amounts of wind or air pressure, often giving a knuckle ball effect.\nNotable penhold short pips hitters include 1996 Olympic Champion Liu Guoliang, 1985 and 1987 World Champion Jiang Jialiang, three-time Olympian Toshio Tasaki, Yang Ying, Kwak Bang-bang, Seok Eun-mi, He Zhi Wen, Lee Eun-hee, Wang Zeng Yi and Rory Cargill.\nShakehand playing styles"]], "corpus": "wikipedia"}
{"tstamp": 1722307112.9208, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722307112.5638, "finish": 1722307112.9208, "ip": "", "conv_id": "3754c3b0f9fd448eac01d2cebd94e498", "model_name": "embed-english-v3.0", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines\n\nAbstract: This paper critically analyses the \"attention economy\" within the framework of cognitive science and techno-political economics, as applied to both human and machine interactions. We explore how current business models, particularly in digital platform capitalism, harness user engagement by strategically shaping attentional patterns. These platforms utilize advanced AI and massive data analytics to enhance user engagement, creating a cycle of attention capture and data extraction. We review contemporary (neuro)cognitive theories of attention and platform engagement design techniques and criticize classical cognitivist and behaviourist theories for their inadequacies in addressing the potential harms of such engagement on user autonomy and wellbeing. 4E approaches to cognitive science, instead, emphasizing the embodied, extended, enactive, and ecological aspects of cognition, offer us an intrinsic normative standpoint and a more integrated understanding of how attentional patterns are actively constituted by adaptive digital environments. By examining the precarious nature of habit formation in digital contexts, we reveal the techno-economic underpinnings that threaten personal autonomy by disaggregating habits away from the individual, into an AI managed collection of behavioural patterns. Our current predicament suggests the necessity of a paradigm shift towards an ecology of attention. This shift aims to foster environments that respect and preserve human cognitive and social capacities, countering the exploitative tendencies of cognitive capitalism."]], "corpus": "arxiv"}
{"tstamp": 1722307122.7157, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722307120.4275, "finish": 1722307122.7157, "ip": "", "conv_id": "ea468cdfbe3a4dd680a9a7471bed7154", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "The effect of long pimple", "output": [["The effect of long pimple", "Table tennis grips and playing styles\n\nShort pips hitter This penhold style utilizes a short pips out rubber, usually on only one side of the racket with no backside rubber. Short pips hitters play over the table, hitting the ball very flatly as soon as it bounces off the table with the pips being largely unaffected by (or even negating) the majority of the opponent's spin. The advantage of hitting the ball flatly is that all of the player's power is transferred into the linear velocity of the ball, without expending any energy on creating spin. This means that the shots are very fast, and typically hard to block for the inexperienced player. However, the lack of a topspin Magnus effect means that the shots are harder to place on the table since they don't follow a downward arc trajectory, and the lack of angular momentum on the ball means that shots are less consistent and more sensitive to small amounts of wind or air pressure, often giving a knuckle ball effect.\nNotable penhold short pips hitters include 1996 Olympic Champion Liu Guoliang, 1985 and 1987 World Champion Jiang Jialiang, three-time Olympian Toshio Tasaki, Yang Ying, Kwak Bang-bang, Seok Eun-mi, He Zhi Wen, Lee Eun-hee, Wang Zeng Yi and Rory Cargill.\nShakehand playing styles"]], "corpus": "wikipedia"}
{"tstamp": 1722307143.5212, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722307143.3926, "finish": 1722307143.5212, "ip": "", "conv_id": "ef28c889daf44e7587446e62092e3d2a", "model_name": "GritLM/GritLM-7B", "prompt": "Is attention all I need?", "output": [["Is attention all I need?", "Title: Attention Is All You Need\n\nAbstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."]], "corpus": "arxiv"}
{"tstamp": 1722307566.3844, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722307566.1887, "finish": 1722307566.3844, "ip": "", "conv_id": "9deb3465ffd3458081570489c3810813", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "are opened or are open?", "output": [["are opened or are open?", "Ell Stackexchange\n\nQ: Are they the same in meaning, the open door and the opened door? What is the difference between \"the open door\" and \"the opened door\"? Are they the same in meaning? Please explain it. Thanks a lot.\n\nA: Open is an adjective. \nOpened is a past participle used as a modifier. \nThe difference in meaning is that open is in contrast to not open. The door  could have been open for ten years or a century. It could always be open. \nOpened expands to The \"having been opened\" door...\nThis refers to the action of the door having been opened. The door used to be shut, now it is open. It has been opened. It has not always been open. It could have been opened ten years ago or two  seconds ago, but at some point it was closed. \n"]], "corpus": "stackexchange"}
{"tstamp": 1722307566.3844, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722307566.1887, "finish": 1722307566.3844, "ip": "", "conv_id": "9913e8b8325c4550a0bfb3f5298189d2", "model_name": "BM25", "prompt": "are opened or are open?", "output": [["are opened or are open?", "Ell Stackexchange\n\nQ: Are they the same in meaning, the open door and the opened door? What is the difference between \"the open door\" and \"the opened door\"? Are they the same in meaning? Please explain it. Thanks a lot.\n\nA: Open is an adjective. \nOpened is a past participle used as a modifier. \nThe difference in meaning is that open is in contrast to not open. The door  could have been open for ten years or a century. It could always be open. \nOpened expands to The \"having been opened\" door...\nThis refers to the action of the door having been opened. The door used to be shut, now it is open. It has been opened. It has not always been open. It could have been opened ten years ago or two  seconds ago, but at some point it was closed. \n"]], "corpus": "stackexchange"}
{"tstamp": 1722309436.1545, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722309435.9519, "finish": 1722309436.1545, "ip": "", "conv_id": "38a0e89319e9427ea9e2b12967ebecca", "model_name": "BM25", "prompt": "are brake pads supposed to be loose?", "output": [["are brake pads supposed to be loose?", "Stackoverflow Stackexchange\n\nQ: In Scala, how do I mixin java interfaces into Scala code For example suppose I have\ninterface ICar {...}\nclass Car implements ICar {...}\n\nIn Scala I wish to do\nnew MyScalaClass with ICar\n\nBut use the java implementation of ICar i.e. Car. What is the syntax for doing this?\n\nA: You can use object aggregation, but encapsulating the aggregation in a trait. Suppose you have the following Java code:\ninterface ICar {\n  public void brake();\n}\npublic class Car implements ICar {\n  public void brake() { System.out.println(\"BRAKE !!!\"); }\n}\n\nThen you can define the following Scala trait:\ntrait HasCar { self: ICar =>\n  private val car = new Car\n  def brake() = car.brake()\n}\n\nAnd finally you can mix everything you need into your class:\n val c = new MyScalaClass extends ICar with HasCar\n c.brake // prints \"BRAKE !!!\"\n\n"]], "corpus": "stackexchange"}
{"tstamp": 1722309436.1545, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722309435.9519, "finish": 1722309436.1545, "ip": "", "conv_id": "9af9eff85d5f4a178cd5808f666b4e57", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "are brake pads supposed to be loose?", "output": [["are brake pads supposed to be loose?", "Gaming Stackexchange\n\nQ: Brake pedal trouble with Xbox 360 Racing Wheel I have bought a used Microsoft Xbox Racing Wheel and have recently started playing. I have noticed that every time I let go of the gas pedal completely, the braking lights turn on. And when the car eventually stops, it starts backing up in reverse at full speed.\nHas anyone else experienced this? It happens on any game. I also switched pedal sets as I have 2 sets. \n\nA: I had this problem with mine, and it turned out that I needed to change out my batteries.  There is a compartment on the steering wheel portion of the setup that opens up when you push a grey button, and that's where the batteries go.  I want to say that it's all the way over on the right side of the assembly, but I am not 100% sure, as I do not have it in front of me.  It's pretty easy to find, once you know it's up there, though. If you can, use the kind of batteries meant for digital cameras, etc; the wheel draws a bit more juice than a normal XB360 controller.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722309446.5042, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722309446.2777, "finish": 1722309446.5042, "ip": "", "conv_id": "bc8a08f1718846e58aa907a16ce9620b", "model_name": "GritLM/GritLM-7B", "prompt": "where did german immigrants settled in the 1800s", "output": [["where did german immigrants settled in the 1800s", "German Americans\n\nA particularly attractive destination was Milwaukee, which came to be known as \"the German Athens\". Radical Germans trained in politics in the old country dominated the city's Socialists. Skilled workers dominated many crafts, while entrepreneurs created the brewing industry; the most famous brands included Pabst, Schlitz, Miller, and Blatz.\nWhereas half of German immigrants settled in cities, the other half established farms in the Midwest. From Ohio to the Plains states, a heavy presence persists in rural areas into the 21st century.\nDeep South\nFew German immigrants settled in the Deep South, apart from New Orleans, the German Coast, and Texas.\nTexas\nTexas attracted many Germans who entered through Galveston and Indianola, both those who came to farm, and later immigrants who more rapidly took industrial jobs in cities such as Houston. As in Milwaukee, Germans in Houston built the brewing industry. By the 1920s, the first generation of college-educated German Americans were moving into the chemical and oil industries.\nTexas had about 20,000 German Americans in the 1850s. They did not form a uniform bloc, but were highly diverse and drew from geographic areas and all sectors of European society, except that very few aristocrats or upper middle class businessmen arrived. In this regard, Texas Germania was a microcosm of the Germania nationwide."]], "corpus": "wikipedia"}
{"tstamp": 1722309446.5042, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722309446.2777, "finish": 1722309446.5042, "ip": "", "conv_id": "b7cfab5df92f40cb97e749a1057688d3", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "where did german immigrants settled in the 1800s", "output": [["where did german immigrants settled in the 1800s", "Immigration to Germany\n\nImmigration to Germany, both in the country's modern borders and the many political entities that preceded it, has occurred throughout the country's history. Today, Germany is one of the most popular destinations for immigrants in the world, with well over 1 million people moving there each year since 2013. As of 2019, around 13.7 million people living in Germany, or about 17% of the population, are first-generation immigrants."]], "corpus": "wikipedia"}
{"tstamp": 1722310041.3916, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722310041.2042, "finish": 1722310041.3916, "ip": "", "conv_id": "0b27bd2d434548c2960eabd134fec901", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Lightweight and efficient self-attentive model improving performance of sequential recommenders.", "output": [["Lightweight and efficient self-attentive model improving performance of sequential recommenders.", "Title: Infinite Lexicographic Products of Triangular Algebras\n\nAbstract: Some new connections are given between linear orderings and triangular operator algebras. A lexicograhic product is defined for triangular operator algebras and the Jacobson radical of an infinite lexicographic product of upper triangular matrix algebras is determined."]], "corpus": "arxiv"}
{"tstamp": 1722310041.3916, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310041.2042, "finish": 1722310041.3916, "ip": "", "conv_id": "29c8e18ebfb3415483d9b644aadd230c", "model_name": "GritLM/GritLM-7B", "prompt": "Lightweight and efficient self-attentive model improving performance of sequential recommenders.", "output": [["Lightweight and efficient self-attentive model improving performance of sequential recommenders.", "Title: Lightweight Self-Attentive Sequential Recommendation\n\nAbstract: Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks. Given a sequence of interacted items, existing DNN-based sequential recommenders commonly embed each item into a unique vector to support subsequent computations of the user interest. However, due to the potentially large number of items, the over-parameterised item embedding matrix of a sequential recommender has become a memory bottleneck for efficient deployment in resource-constrained environments, e.g., smartphones and other edge devices. Furthermore, we observe that the widely-used multi-head self-attention, though being effective in modelling sequential dependencies among items, heavily relies on redundant attention units to fully capture both global and local item-item transition patterns within a sequence. In this paper, we introduce a novel lightweight self-attentive network (LSAN) for sequential recommendation. To aggressively compress the original embedding matrix, LSAN leverages the notion of compositional embeddings, where each item embedding is composed by merging a group of selected base embedding vectors derived from substantially smaller embedding matrices. Meanwhile, to account for the intrinsic dynamics of each item, we further propose a temporal context-aware embedding composition scheme. Besides, we develop an innovative twin-attention network that alleviates the redundancy of the traditional multi-head self-attention while retaining full capacity for capturing long- and short-term (i.e., global and local) item dependencies. Comprehensive experiments demonstrate that LSAN significantly advances the accuracy and memory efficiency of existing sequential recommenders."]], "corpus": "arxiv"}
{"tstamp": 1722310072.3579, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722310072.2064, "finish": 1722310072.3579, "ip": "", "conv_id": "723e06bd28704a6b90a00a505ecb7438", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "output": [["A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "Title: ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts\n\nAbstract: This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and the attention weights, which are shared between tasks in multi-task training, are updated, while the original LM and source prompts are intact. ATTEMPT is highly parameter-efficient (e.g., updates 2,300 times fewer parameters than full fine-tuning) while achieving high task performance using knowledge from high-resource tasks. Moreover, it is modular using pre-trained soft prompts, and can flexibly add or remove source prompts for effective knowledge transfer. Our experimental results across 21 diverse NLP datasets show that ATTEMPT significantly outperforms prompt tuning and outperforms or matches fully fine-tuned or other parameter-efficient tuning approaches that use over ten times more parameters. Finally, ATTEMPT outperforms previous work in few-shot learning settings."]], "corpus": "arxiv"}
{"tstamp": 1722310072.3579, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722310072.2064, "finish": 1722310072.3579, "ip": "", "conv_id": "6584a2b86688459ca86f0cbfc241df00", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "output": [["A new method called ATTEMPT utilizing attentional mixtures of soft prompts for knowledge transfer.", "Title: ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts\n\nAbstract: This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and the attention weights, which are shared between tasks in multi-task training, are updated, while the original LM and source prompts are intact. ATTEMPT is highly parameter-efficient (e.g., updates 2,300 times fewer parameters than full fine-tuning) while achieving high task performance using knowledge from high-resource tasks. Moreover, it is modular using pre-trained soft prompts, and can flexibly add or remove source prompts for effective knowledge transfer. Our experimental results across 21 diverse NLP datasets show that ATTEMPT significantly outperforms prompt tuning and outperforms or matches fully fine-tuned or other parameter-efficient tuning approaches that use over ten times more parameters. Finally, ATTEMPT outperforms previous work in few-shot learning settings."]], "corpus": "arxiv"}
{"tstamp": 1722310097.423, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722310094.8456, "finish": 1722310097.423, "ip": "", "conv_id": "daca567e0fff4693a219f1ccdcb2330c", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "who votes for nba defensive player of year", "output": [["who votes for nba defensive player of year", "NBA Defensive Player of the Year Award\n\nThe NBA Defensive Player of the Year Award is an annual National Basketball Association (NBA) award given since the 1982–83 NBA season to the best defensive player of the regular season. The winner is selected by a panel of 124 sportswriters and broadcasters throughout the United States and Canada, each of whom casts a vote for first, second and third place selections. Each first-place vote is worth five points, second-place votes are worth three points, and a third-place vote is worth one. The player with the highest point total, regardless of the number of first-place votes, wins the award. Since the 2022–23 NBA season, winners receive the Hakeem Olajuwon Trophy, named after the two-time defensive player of the year winner.\nDikembe Mutombo, Ben Wallace, and Rudy Gobert have each won the award a record four times. Dwight Howard has won the award three times, and is the only player ever to have won it in three consecutive seasons. Sidney Moncrief, Mark Eaton, Dennis Rodman, Hakeem Olajuwon, Alonzo Mourning and Kawhi Leonard have each won it twice. The most recent award recipient is Rudy Gobert of the Minnesota Timberwolves."]], "corpus": "wikipedia"}
{"tstamp": 1722310097.423, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722310094.8456, "finish": 1722310097.423, "ip": "", "conv_id": "2eb4d1f035c0457d8b0f19fc28c2af8b", "model_name": "text-embedding-3-large", "prompt": "who votes for nba defensive player of year", "output": [["who votes for nba defensive player of year", "NBA Defensive Player of the Year Award\n\nThe NBA Defensive Player of the Year Award is an annual National Basketball Association (NBA) award given since the 1982–83 NBA season to the best defensive player of the regular season. The winner is selected by a panel of 124 sportswriters and broadcasters throughout the United States and Canada, each of whom casts a vote for first, second and third place selections. Each first-place vote is worth five points, second-place votes are worth three points, and a third-place vote is worth one. The player with the highest point total, regardless of the number of first-place votes, wins the award. Since the 2022–23 NBA season, winners receive the Hakeem Olajuwon Trophy, named after the two-time defensive player of the year winner.\nDikembe Mutombo, Ben Wallace, and Rudy Gobert have each won the award a record four times. Dwight Howard has won the award three times, and is the only player ever to have won it in three consecutive seasons. Sidney Moncrief, Mark Eaton, Dennis Rodman, Hakeem Olajuwon, Alonzo Mourning and Kawhi Leonard have each won it twice. The most recent award recipient is Rudy Gobert of the Minnesota Timberwolves."]], "corpus": "wikipedia"}
{"tstamp": 1722310117.8599, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722310117.7326, "finish": 1722310117.8599, "ip": "", "conv_id": "fc308a6564e84da2b576fda8f5b0f500", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "output": [["Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "Title: Spatio-temporal dynamics and plastic flow of vortices in superconductors with periodic arrays of pinning sites\n\nAbstract: We present simulations of flux-gradient-driven superconducting rigid vortices interacting with square and triangular arrays of columnar pinning sites in an increasing external magnetic field. These simulations allow us to quantitatively relate spatio-temporal microscopic information of the vortex lattice with typically measured macroscopic quantities, such as the magnetization $M(H)$. The flux lattice does not become completely commensurate with the pinning sites throughout the sample at the magnetization matching peaks, but forms a commensurate lattice in a region close to the edge of the sample. Matching fields related to unstable vortex configurations do not produce peaks in $M(H)$. We observe a variety of evolving complex flux profiles, including flat terraces or plateaus separated by winding current-carrying strings and, near the peaks in $M(H)$, plateaus only in certain regions, which move through the sample as the field increases."]], "corpus": "arxiv"}
{"tstamp": 1722310117.8599, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722310117.7326, "finish": 1722310117.8599, "ip": "", "conv_id": "659b5d7e65414017a44941dc4860d179", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "output": [["Study on improving computational efficiency and reducing parameters in recommender systems using prompt tuning and pruning techniques", "Title: Local Hypercomplex Analyticity\n\nAbstract: The notion of analyticity is studied in the context of hypercomplex numbers. A critical review of the problems arising from the conventional approach is given. We describe a local analyticity condition which yields the desired type of hypercomplex solutions. The result is the definition of a generalized complex analyticity to hypercomplex space. 02.10.Tq/Vr, 02.30.-f/Dk, 02.90.+p"]], "corpus": "arxiv"}
{"tstamp": 1722310173.0824, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310172.872, "finish": 1722310173.0824, "ip": "", "conv_id": "356862c28de946ae8a434438c62a06e4", "model_name": "GritLM/GritLM-7B", "prompt": "diablo 2 is not working on windows 7?", "output": [["diablo 2 is not working on windows 7?", "Gaming Stackexchange\n\nQ: Playing Diablo 1 on Windows 7 I just found an old copy of Diablo (the first game) in a box and whacked it into my laptop I purchased just yesterday.\nAfter the quick install I hit play and found that the screen remained black (nothing was rendering). There was however sound.\nI downloaded the patch but still nothing.\nWith it being a new laptop, I figure I might be missing some required software like Direct X maybe? I'm not really sure.\nIf anyone has any suggestions or knows how to fix this issue, that would be really helpful.\n\nA: This may sound weird (and I have actually done this to play Diablo on my Win 7 laptop), but right click on your desktop and click on Screen Resolution. Leave that open and then run Diablo.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722310173.0824, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722310172.872, "finish": 1722310173.0824, "ip": "", "conv_id": "375b8963e38a4bd191128977a52d9b97", "model_name": "BM25", "prompt": "diablo 2 is not working on windows 7?", "output": [["diablo 2 is not working on windows 7?", "Gaming Stackexchange\n\nQ: How do I configure Diablo 2 in Wine to -skiptobnet and -w? I am using Ubuntu Gnome and Diablo 2 works ok with Wine. How do I add shortcut options to the game? I want the -skiptobnet and -w for windowed mode.\nPath is ~/.wine/drive_c/Program Files (x86)/Diablo II/Game.exe.\n\nA: I was able to get options to work using the windows .lnk file that was created in the wine start menu directory during installation.\nYou can launch Diablo II through terminal or create a new launcher using this command replacing PATH_TO_WINE and PATH_TO_SHORTCUT:\nenv WINEPREFIX=\"PATH_TO_WINE\" wine C:\\\\windows\\\\command\\\\start.exe PATH_TO_SHORTCUT -skiptobnet -w\n\nFor example:\nenv WINEPREFIX=\"/home/nonterrorist/.wine\" wine C:\\\\windows\\\\command\\\\start.exe /Unix /home/nonterrorist/.wine/dosdevices/c:/users/Public/Start\\ Menu/Programs/Diablo\\ II/Diablo\\ II\\ -\\ Lord\\ of\\ Destruction.lnk -skiptobnet -w\n\nNote in the example above, you will need to escape any spaces in the shortcut path using \\\n"]], "corpus": "stackexchange"}
{"tstamp": 1722310277.0391, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722310276.8952, "finish": 1722310277.0391, "ip": "", "conv_id": "9776843ed00d4cab89c0894cad832500", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "what are chapters in the bible called?", "output": [["what are chapters in the bible called?", "Stackoverflow Stackexchange\n\nQ: How to strip executables thoroughly I'd like to strip as much as I can - on Linux: an ELF. I only want in there the stuff I need to run it.\nI tried using strip:\nstrip --strip-all elf\n\nBut it doesn't seem to do a good job: nm still displays lots of stuff, and the binary is still big.\nWhat should I do?\n\nA: I would check out this great article that goes into depth on making an ELF executable as small as possible. Maybe it has info that could help!\nhttp://www.muppetlabs.com/~breadbox/software/tiny/teensy.html\n"]], "corpus": "stackexchange"}
{"tstamp": 1722310277.0391, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722310276.8952, "finish": 1722310277.0391, "ip": "", "conv_id": "87810785bece4d6dab306293a64980f2", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "what are chapters in the bible called?", "output": [["what are chapters in the bible called?", "English Stackexchange\n\nQ: Why are the divisions of the Bible called \"verses\"? In common parlance, a verse is a writing arranged with a metrical rhythm, typically having a rhyme. Most of the books of Bible contain prose which do not follow metrical rhythm or rhyme. But their sentences are called verses. My question therefore is: Why are the divisions of the Bible called verses?\n\nA: A number of questions  on Stack Exchange - English Language and Usage have commented on the words 'stanza' and 'verse' in relation to poetry and to scripture.\n'Verses' are also used in Shakespeare.\nMerriam Webster defines a 'verse' in three ways : metrical verse, a stanza or a biblical verse.\nIn any work which requires to be referenced and quoted as part of its function, stanzas or verses are numbered accordingly so that they can be cited.\n"]], "corpus": "stackexchange"}
{"tstamp": 1722310317.0996, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310316.7231, "finish": 1722310317.0996, "ip": "", "conv_id": "895db1e023ff4806920e918196f681a3", "model_name": "GritLM/GritLM-7B", "prompt": "who are the parents of the three fates", "output": [["who are the parents of the three fates", "Clotho\n\nAs one of the three fates her influence in Greek mythology was significant. Along with her sisters and Hermes, Clotho was given credit for creating the alphabet for their people. Even though Clotho and her sisters were worshiped as goddesses, their representation of fate is more central to their role in mythology. Thread represented human life and her decisions represented the fate of all people in society.\nOrigin\nAlthough there does not seem to be an epic tale in Classical Greek mythology in which the Fates are the main focus, they have played critical roles in the lives of deities and mortals. It is likely that the versions of myths about their primal origins were lost in the transitions from prehistoric Greek mythology to that of the Classical writings.\nAccording to Hesiod's Theogony, Clotho and her sisters (Atropos and Lachesis) were the daughters of Nyx (Night), without the assistance of a father. Later in the same work (ll. 901-906) they are said to have been born of Zeus and Themis. Clotho is mentioned in the tenth book of the Republic of Plato as the daughter of Necessity, as well.\nIn Roman mythology, it was believed that Clotho was the daughter of Uranus and Gaia.\nThe Ivory Shoulder\nAs one of the Three Fates, Clotho participated in creating the alphabet with Hermes, forced the goddess Aphrodite into making love with other gods, weakened the monster Typhon with poison fruit, persuaded Zeus to kill Asclepius with a bolt of lightning, and aided the deities in their war with the Giants by killing Agrius and Thoas with bronze clubs."]], "corpus": "wikipedia"}
{"tstamp": 1722310317.0996, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722310316.7231, "finish": 1722310317.0996, "ip": "", "conv_id": "190f215de1614b968c5d2a87d0375480", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "who are the parents of the three fates", "output": [["who are the parents of the three fates", "Fates\n\nThe Fates are a common motif in European polytheism, most frequently represented as a trio of goddesses. The Fates shape the destiny of each human, often expressed in textile metaphors such as spinning fibers into yarn, or weaving threads on a loom. The trio are generally conceived of as sisters and are often given the names Clotho, Lachesis, and Atropos, which are the names of the Moirai, the version of the Fates who appear in Greek mythology. These divine figures are often artistically depicted as beautiful maidens with consideration to their serious responsibility: the life of mortals. Poets, on the other hand, typically express the Fates as ugly and unwavering, representing the gravity of their role within the mythological and human worlds."]], "corpus": "wikipedia"}
{"tstamp": 1722310335.4722, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722310335.2095, "finish": 1722310335.4722, "ip": "", "conv_id": "256e5bb6399a4ba7b599c9ba9a402e95", "model_name": "embed-english-v3.0", "prompt": "Learning word representations to improve IR tasks like query expansion and classification", "output": [["Learning word representations to improve IR tasks like query expansion and classification", "Title: Relevance-based Word Embedding\n\nAbstract: Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe."]], "corpus": "arxiv"}
{"tstamp": 1722310335.4722, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722310335.2095, "finish": 1722310335.4722, "ip": "", "conv_id": "2f7dc574c8b94ae7a397af80c4cf05f5", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Learning word representations to improve IR tasks like query expansion and classification", "output": [["Learning word representations to improve IR tasks like query expansion and classification", "Title: Relevance-based Word Embedding\n\nAbstract: Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe."]], "corpus": "arxiv"}
{"tstamp": 1722310364.3913, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722310363.9154, "finish": 1722310364.3913, "ip": "", "conv_id": "8c01a7990c7d433c8f9c9a38ccfd45b3", "model_name": "voyage-multilingual-2", "prompt": "who starred in the film far from the madding crowd", "output": [["who starred in the film far from the madding crowd", "Far from the Madding Crowd (2015 film)\n\nPlot The film is set in 1870 in Britain. While working on her aunt's farm in Dorset, Bathsheba Everdene (Carey Mulligan) meets neighbouring farmer, Gabriel Oak (Matthias Schoenaerts). He eventually proposes, but the headstrong Bathsheba declines, saying she is too independent. One night, Gabriel's new sheepdog herds his entire flock off a steep cliff. Penniless, Gabriel leaves to search for work. In contrast, Bathsheba inherits an uncle's farm and leaves to run it.\nWhile at a fair trying to find employment, Gabriel sees recruiting officers. A girl there, Fanny Robbin, points out one soldier, Sergeant Frank Troy, her sweetheart. She suggests Gabriel seek employment at a farm in Weatherbury. Upon Gabriel's arrival, several buildings are on fire. He saves the barn from destruction. At dawn the next day, he meets the farm's new mistress, Bathsheba, who hires him as a shepherd.\nWhile in town trading her seed, Bathsheba sees her neighbour William Boldwood (Michael Sheen), a prosperous and mature bachelor. Bathsheba sends Boldwood a Valentine as a joke. He is both offended and intrigued. In the meantime, Fanny goes to the wrong church for her wedding; Troy, believing he's been jilted, is devastated. Boldwood comes to speak to Bathsheba, sees her handling sheep, and is charmed. He invites her to his home, where he proposes marriage. Bathsheba delays giving him a final answer, and Gabriel admonishes her for toying with Boldwood's affections. Stung by the criticism, she fires him. The next day, a crisis arises with the sheep that only Gabriel can manage. She successfully persuades him to return."]], "corpus": "wikipedia"}
{"tstamp": 1722310364.3913, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722310363.9154, "finish": 1722310364.3913, "ip": "", "conv_id": "2dafecf528e44c34a578dd2bad99f101", "model_name": "GritLM/GritLM-7B", "prompt": "who starred in the film far from the madding crowd", "output": [["who starred in the film far from the madding crowd", "Far from the Madding Crowd (2015 film)\n\nFar from the Madding Crowd is a 2015 British romantic drama film directed by Thomas Vinterberg and starring Carey Mulligan, Matthias Schoenaerts, Tom Sturridge, Michael Sheen, and Juno Temple. An adaptation by David Nicholls of the 1874 novel Far from the Madding Crowd by Thomas Hardy, it is the fourth film adaptation of the novel."]], "corpus": "wikipedia"}