Datasets:

mteb
/

arena-results

+{"tstamp": 1722305009.6263, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722305004.9177, "finish": 1722305009.6263, "ip": "", "conv_id": "dcf280bdf2f5420281638d2dd9e11974", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["diamond", "amethyst", "ruby", "sapphire", "opal", "cappuccino", "mocha", "latte", "oboe", "trumpet", "flute", "saxophone", "clarinet", "trombone", "bassoon", "parabolic", "plane", "concave", "black", "white", "green"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722305009.6263, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722305004.9177, "finish": 1722305009.6263, "ip": "", "conv_id": "7d7bb506678345b39e10ef230d7bf4f7", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["diamond", "amethyst", "ruby", "sapphire", "opal", "cappuccino", "mocha", "latte", "oboe", "trumpet", "flute", "saxophone", "clarinet", "trombone", "bassoon", "parabolic", "plane", "concave", "black", "white", "green"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722305185.1488, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722305185.0712, "finish": 1722305185.1488, "ip": "", "conv_id": "1ffb428de2744076962b8acf8ec4df89", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["pyramidal", "motor", "Purkinje", "interneuron", "O", "B", "composite", "cinder cone", "flashlight", "sleeping bag", "whiskey", "tequila"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722305185.1488, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305185.0712, "finish": 1722305185.1488, "ip": "", "conv_id": "99f802e985564f459a4c8f1d35672366", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["pyramidal", "motor", "Purkinje", "interneuron", "O", "B", "composite", "cinder cone", "flashlight", "sleeping bag", "whiskey", "tequila"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722305188.8759, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722305188.8027, "finish": 1722305188.8759, "ip": "", "conv_id": "1ffb428de2744076962b8acf8ec4df89", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["pyramidal", "motor", "Purkinje", "interneuron", "O", "B", "composite", "cinder cone", "flashlight", "sleeping bag", "whiskey", "tequila", "B12", "K", "B1", "sautéing", "grilling", "baking", "frying", "steaming"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722305188.8759, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722305188.8027, "finish": 1722305188.8759, "ip": "", "conv_id": "99f802e985564f459a4c8f1d35672366", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["pyramidal", "motor", "Purkinje", "interneuron", "O", "B", "composite", "cinder cone", "flashlight", "sleeping bag", "whiskey", "tequila", "B12", "K", "B1", "sautéing", "grilling", "baking", "frying", "steaming"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722305193.6001, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722305193.4617, "finish": 1722305193.6001, "ip": "", "conv_id": "435492f9510842b3b662664dab56845d", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["fascism", "conservatism", "socialism", "hindsight bias", "dunning-kruger effect", "availability bias", "shovel", "wheelbarrow", "watering can", "pruning shears", "rake", "hoe", "trowel", "canine", "molar", "premolar", "incisor", "water filter", "tent", "camping stove", "sleeping bag", "backpack", "compass"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722305193.6001, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722305193.4617, "finish": 1722305193.6001, "ip": "", "conv_id": "8d6cf721df4a4d5ebea51d7bb875effb", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["fascism", "conservatism", "socialism", "hindsight bias", "dunning-kruger effect", "availability bias", "shovel", "wheelbarrow", "watering can", "pruning shears", "rake", "hoe", "trowel", "canine", "molar", "premolar", "incisor", "water filter", "tent", "camping stove", "sleeping bag", "backpack", "compass"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-b9229914-47bc-4da8-a21b-89329fff8207.jsonl ADDED Viewed

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+{"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"}

data/sts_individual-b9229914-47bc-4da8-a21b-89329fff8207.jsonl ADDED Viewed

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