Scheduled Commit

data/retrieval_battle-fc2e4671-7e20-44ad-a373-899a077f1401.jsonl

@@ -16,3 +16,13 @@
 {"tstamp": 1726527021.4177, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "4b9ba34af416462b8c2fc16fd505a8de", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "tell me about signalling systems for class 1 railways", "0_output": [["tell me about signalling systems for class 1 railways", "Title: Railway Track Specific Traffic Signal Selection Using Deep Learning\n\nAbstract: With the railway transportation Industry moving actively towards automation, accurate location and inventory of wayside track assets like traffic signals, crossings, switches, mileposts, etc. is of extreme importance. With the new Positive Train Control (PTC) regulation coming into effect, many railway safety rules will be tied directly to location of assets like mileposts and signals. Newer speed regulations will be enforced based on location of the Train with respect to a wayside asset. Hence it is essential for the railroads to have an accurate database of the types and locations of these assets. This paper talks about a real-world use-case of detecting railway signals from a camera mounted on a moving locomotive and tracking their locations. The camera is engineered to withstand the environment factors on a moving train and provide a consistent steady image at around 30 frames per second. Using advanced image analysis and deep learning techniques, signals are detected in these camera images and a database of their locations is created. Railway signals differ a lot from road signals in terms of shapes and rules for placement with respect to track. Due to space constraint and traffic densities in urban areas signals are not placed on the same side of the track and multiple lines can run in parallel. Hence there is need to associate signal detected with the track on which the train runs. We present a method to associate the signals to the specific track they belong to using a video feed from the front facing camera mounted on the lead locomotive. A pipeline of track detection, region of interest selection, signal detection has been implemented which gives an overall accuracy of 94.7% on a route covering 150km with 247 signals."]], "0_corpus": "arxiv", "1_conv_id": "76418008e48941bfa2ab46f4f09babd0", "1_model_name": "text-embedding-3-large", "1_prompt": "tell me about signalling systems for class 1 railways", "1_output": [["tell me about signalling systems for class 1 railways", "Title: A Radio Based Intelligent Railway Grade Crossing System to Avoid Collision\n\nAbstract: Railway grade crossing is become the major headache for the transportation system. This paper describes an intelligent railway crossing control system for multiple tracks that features a controller which receives messages from incoming and outgoing trains by sensors. These messages contain detail information including the direction and identity of a train. Depending on those messages the controller device decides whenever the railroad crossing gate will close or open."]], "1_corpus": "arxiv"}
 {"tstamp": 1726530186.447, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d0f215e5afe441b2a22633e5469fce5d", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "who was the first president of the US", "0_output": [["who was the first president of the US", "List of United States presidential firsts\n\nFirst president to have pets at the White House; two grizzly bear cubs and a mockingbird.\nFirst president to found a university after being in office; the University of Virginia in 1819.\nFirst president to serve as rector of a university (University of Virginia).\nFirst president to deliver a State of the Union Address via writing; this practice continued until 1913.\nFirst president to die on the Fourth of July.\nFirst president to be outlived by another former president.\nFirst president to defeat his opponent in his opponent's birth state.\nJames Madison (1809–1817)\nFirst president to have served in the United States House of Representatives.\nFirst president to ask Congress for a Declaration of War.\nFirst president to serve as a wartime commander-in-chief.\nFirst president to have an Inaugural ball.\nFirst president to issue a pocket veto.\nFirst president to have a parent live throughout his presidency.\nFirst president to have a second cousin as a future president.\nJames Monroe (1817–1825)\nFirst president to have served in the United States Senate.\nFirst president to have a child marry at the White House.\nFirst president to ride on a steamboat.\nFirst president to have held over 50 years of elected public office positions by the end of his presidency"]], "0_corpus": "wikipedia", "1_conv_id": "3e99fd5c68e045a9b6824d2a69059f6f", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "who was the first president of the US", "1_output": [["who was the first president of the US", "Presidency of George Washington\n\nThe presidency of George Washington began on April 30, 1789, when Washington was inaugurated as the first president of the United States, and ended on March 4, 1797. Washington took office after the 1788–1789 presidential election, the nation's first quadrennial presidential election, in which he was elected unanimously by the Electoral College. Washington was re-elected unanimously in the 1792 presidential election and chose to retire after two terms. He was succeeded by his vice president, John Adams of the Federalist Party.\nWashington, who had established his preeminence among the new nation's Founding Fathers through his service as Commander-in-Chief of the Continental Army during the American Revolutionary War and as president of the 1787 constitutional convention, was widely expected to become the first president of the United States under the new Constitution, though he desired to retire from public life. In his first inaugural address, Washington expressed both his reluctance to accept the presidency and his inexperience with the duties of civil administration, though he proved an able leader."]], "1_corpus": "wikipedia"}
 {"tstamp": 1726530215.3082, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "f4a4e47c9d0f437eb1bfcadd2c80431f", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "what is euler's theorem", "0_output": [["what is euler's theorem", "Euler's theorem\n\nIn number theory, Euler's theorem (also known as the Fermat–Euler theorem or Euler's totient theorem) states that, if  and  are coprime positive integers, then  is congruent to   modulo , where   denotes Euler's totient function; that is"]], "0_corpus": "wikipedia", "1_conv_id": "7cace2aac72247ceb0a1d623f9ec2376", "1_model_name": "text-embedding-3-large", "1_prompt": "what is euler's theorem", "1_output": [["what is euler's theorem", "Euler's theorem\n\nIn number theory, Euler's theorem (also known as the Fermat–Euler theorem or Euler's totient theorem) states that, if  and  are coprime positive integers, then  is congruent to   modulo , where   denotes Euler's totient function; that is"]], "1_corpus": "wikipedia"}
+{"tstamp": 1726530241.8145, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6c152cf7e67b48cbb92c00ae197c4578", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "what does vae stand for", "0_output": [["what does vae stand for", "Title: Hierarchical Variational Autoencoder for Visual Counterfactuals\n\nAbstract: Conditional Variational Auto Encoders (VAE) are gathering significant attention as an Explainable Artificial Intelligence (XAI) tool. The codes in the latent space provide a theoretically sound way to produce counterfactuals, i.e. alterations resulting from an intervention on a targeted semantic feature. To be applied on real images more complex models are needed, such as Hierarchical CVAE. This comes with a challenge as the naive conditioning is no longer effective. In this paper we show how relaxing the effect of the posterior leads to successful counterfactuals and we introduce VAEX an Hierarchical VAE designed for this approach that can visually audit a classifier in applications."]], "0_corpus": "arxiv", "1_conv_id": "a8810bee31fb4922b39be471be5f5dde", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "what does vae stand for", "1_output": [["what does vae stand for", "Title: A Tutorial on VAEs: From Bayes' Rule to Lossless Compression\n\nAbstract: The Variational Auto-Encoder (VAE) is a simple, efficient, and popular deep maximum likelihood model. Though usage of VAEs is widespread, the derivation of the VAE is not as widely understood. In this tutorial, we will provide an overview of the VAE and a tour through various derivations and interpretations of the VAE objective. From a probabilistic standpoint, we will examine the VAE through the lens of Bayes' Rule, importance sampling, and the change-of-variables formula. From an information theoretic standpoint, we will examine the VAE through the lens of lossless compression and transmission through a noisy channel. We will then identify two common misconceptions over the VAE formulation and their practical consequences. Finally, we will visualize the capabilities and limitations of VAEs using a code example (with an accompanying Jupyter notebook) on toy 2D data."]], "1_corpus": "arxiv"}
+{"tstamp": 1726530274.4823, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "64bf2b5642fb450eb2bc6564e03fdc59", "0_model_name": "voyage-multilingual-2", "0_prompt": "how can i calculate the algorithmic complexity of my code", "0_output": [["how can i calculate the algorithmic complexity of my code", "Stackoverflow Stackexchange\n\nQ: Software/script to calculate the computational complexity of code? Does anyone know of any program/script to calculate the computational complexity of code (e.g. method function) automatically?\nIf not, is there a good way (e.g. a design pattern, algorithm, etc) that supports it?\nI'm not trying to do this in general.\nIn most cases, I know the input, the algorithm running it, and what constitutes a halt. I'm trying to compare 2 or more algorithms this way.\nE.g.\nalgo #1 - 2x^2 + 10x + 5\n\nalgo #2 - 5x^2 + 1x + 3\n\nBoth algorithms are O(N^2). But algo #2 is better in the short run, while algo #1 is better in the long run.\n\nA: While it is impossible to develop an algorithm that solves your problem generally, you can write an algorithm that will calculate the complexity of a piece of software for a few example inputs. \nThe only software I can find reference to is called Trend-Profiler. But, if you are more interested in the algorithm than the result, there is a paper here that describes the software and its algorithm.\n"]], "0_corpus": "stackexchange", "1_conv_id": "54a3ba58a6884abe92d309652cc47df9", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "how can i calculate the algorithmic complexity of my code", "1_output": [["how can i calculate the algorithmic complexity of my code", "Stackoverflow Stackexchange\n\nQ: Software/script to calculate the computational complexity of code? Does anyone know of any program/script to calculate the computational complexity of code (e.g. method function) automatically?\nIf not, is there a good way (e.g. a design pattern, algorithm, etc) that supports it?\nI'm not trying to do this in general.\nIn most cases, I know the input, the algorithm running it, and what constitutes a halt. I'm trying to compare 2 or more algorithms this way.\nE.g.\nalgo #1 - 2x^2 + 10x + 5\n\nalgo #2 - 5x^2 + 1x + 3\n\nBoth algorithms are O(N^2). But algo #2 is better in the short run, while algo #1 is better in the long run.\n\nA: While it is impossible to develop an algorithm that solves your problem generally, you can write an algorithm that will calculate the complexity of a piece of software for a few example inputs. \nThe only software I can find reference to is called Trend-Profiler. But, if you are more interested in the algorithm than the result, there is a paper here that describes the software and its algorithm.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1726530303.7371, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "d0f8f68e38f54a41a7493760f8fd70a2", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "chinchilla formula", "0_output": [["chinchilla formula", "Title: A formula for pi involving nested radicals\n\nAbstract: We present a new formula for pi involving nested radicals with rapid convergence. This formula is based on the arctangent function identity with argument $x=\\sqrt{2-{{a}_{k-1}}}/{{a}_{k}}$, where \\[ {{a}_{k}}=\\underbrace{\\sqrt{2+\\sqrt{2+\\sqrt{2+\\cdots +\\sqrt{2}}}}}_{k\\,\\,\\text{square}\\,\\,\\text{roots}} \\] is a nested radical consisting of $k$ square roots. The computational test we performed reveals that the proposed formula for pi provides a significant improvement in accuracy as the integer $k$ increases."]], "0_corpus": "arxiv", "1_conv_id": "aa3c1b0cdda046c5bb31fbaf51c9676a", "1_model_name": "text-embedding-3-large", "1_prompt": "chinchilla formula", "1_output": [["chinchilla formula", "Title: Effect of a mixture of amoxicillin and norfloxacin in piglets feeding on productive performance and clinical signs\n\nAbstract: An experiment was conducted to evaluate the effect of feeding post-weaned piglets different levels of a commercial amoxicillin plus norfloxacin formula (Respirend) on the performance, clinical evidence of sickness, and the cost:benefit ratio. 454 PIC x Camborough female and castrated male, weaned at 21 d of age, with an average initial weight of 6.19 kg, were used in this experiment. Treatment 1 consisted in feeding a basal diet supplemented with 1000 g of a commercial tilmicosin product (Pulmotil) per metric tonne of feed (control group, from 21 to 35 d of age) and Treatments 2), 3) and 4) consisted in feeding the basal diet supplement with 500, 300 y 100 g of Respirend per metric tonne of feed, respectively, from 21 to 42 d of age. The results did not show significant differences (P<0.05) in mortality nor in parenteral medicine administration cost in none of the periods. Nevertheless, 500 g of Respirend per metric tonne of feed improved significantly (P<0.05) the general health status in the period between 21 and 35 d of age. For the whole evaluation period (21 to 42 d of age), this level improved significantly (P<0.05) the final body weight, the average daily weight gain, the average daily feed intake, the feed:gain ratio and optimized the cost:benefit ratio."]], "1_corpus": "arxiv"}
+{"tstamp": 1726530331.6548, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "a255d45769e54fcebb317fd82572b217", "0_model_name": "embed-english-v3.0", "0_prompt": "attention", "0_output": [["attention", "Title: Copy this Sentence\n\nAbstract: Attention is an operation that selects some largest element from some set, where the notion of largest is defined elsewhere. Applying this operation to sequence to sequence mapping results in significant improvements to the task at hand. In this paper we provide the mathematical definition of attention and examine its application to sequence to sequence models. We highlight the exact correspondences between machine learning implementations of attention and our mathematical definition. We provide clear evidence of effectiveness of attention mechanisms evaluating models with varying degrees of attention on a very simple task: copying a sentence. We find that models that make greater use of attention perform much better on sequence to sequence mapping tasks, converge faster and are more stable."]], "0_corpus": "arxiv", "1_conv_id": "1186330127d4445dac89ab090dc7ea9d", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "attention", "1_output": [["attention", "Title: A low energy neutrino factory with non-magnetic detectors\n\nAbstract: We show that a very precise neutrino/anti-neutrino event separation is not mandatory to cover the physics program of a low energy neutrino factory and thus non-magnetized detectors like water Cerenkov or liquid Argon detectors can be used. We point out, that oscillation itself strongly enhances the signal to noise ratio of a wrong sign muon search, provided there is sufficiently accurate neutrino energy reconstruction. Further, we argue that apart from a magnetic field, other means to distinguish neutrino from anti-neutrino events (at least statistically) can be explored. Combined with the fact that non-magnetic detectors potentially can be made very big, we show that modest neutrino/anti-neutrino separations at the level of 50% to 90% are sufficient to obtain good sensitivity to CP violation and the neutrino mass hierarchy for $\\sin^22\\theta_{13}>10^{-3}$. These non-magnetized detectors have a rich physics program outside the context of a neutrino factory, including topics like supernova neutrinos and proton decay. Hence, our observation opens the possibility to use a multi-purpose detector also in a neutrino factory beam."]], "1_corpus": "arxiv"}
+{"tstamp": 1726530345.9422, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "f122225161aa4905a2c45533ac61bf06", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "attention is all you need", "0_output": [["attention is all you need", "Title: Concept based Attention\n\nAbstract: Attention endows animals an ability to concentrate on the most relevant information among a deluge of distractors at any given time, either through volitionally 'top-down' biasing, or driven by automatically 'bottom-up' saliency of stimuli, in favour of advantageous competition in neural modulations for information processing. Nevertheless, instead of being limited to perceive simple features, human and other advanced animals adaptively learn the world into categories and abstract concepts from experiences, imparting the world meanings. This thesis suggests that the high-level cognitive ability of human is more likely driven by attention basing on abstract perceptions, which is defined as concept based attention (CbA)."]], "0_corpus": "arxiv", "1_conv_id": "7043aaaf939349c799e89142b6651ceb", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "attention is all you need", "1_output": [["attention is all you 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."]], "1_corpus": "arxiv"}
+{"tstamp": 1726530363.2688, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "a9912ad0d56b4498a9c82d99f44e4d76", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "transformer", "0_output": [["transformer", "Title: A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks\n\nAbstract: Poisoning attacks have emerged as a significant security threat to machine learning algorithms. It has been demonstrated that adversaries who make small changes to the training set, such as adding specially crafted data points, can hurt the performance of the output model. Some of the stronger poisoning attacks require the full knowledge of the training data. This leaves open the possibility of achieving the same attack results using poisoning attacks that do not have the full knowledge of the clean training set. In this work, we initiate a theoretical study of the problem above. Specifically, for the case of feature selection with LASSO, we show that full-information adversaries (that craft poisoning examples based on the rest of the training data) are provably stronger than the optimal attacker that is oblivious to the training set yet has access to the distribution of the data. Our separation result shows that the two setting of data-aware and data-oblivious are fundamentally different and we cannot hope to always achieve the same attack or defense results in these scenarios."]], "0_corpus": "arxiv", "1_conv_id": "3ea6f36f643f4840a463b908b535d124", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "transformer", "1_output": [["transformer", "Title: Transformadores: Fundamentos teoricos y Aplicaciones\n\nAbstract: Transformers are a neural network architecture originally designed for natural language processing that it is now a mainstream tool for solving a wide variety of problems, including natural language processing, sound, image, reinforcement learning, and other problems with heterogeneous input data. Its distinctive feature is its self-attention system, based on attention to one's own sequence, which derives from the previously introduced attention system. This article provides the reader with the necessary context to understand the most recent research articles and presents the mathematical and algorithmic foundations of the elements that make up this type of network. The different components that make up this architecture and the variations that may exist are also studied, as well as some applications of the transformer models. This article is in Spanish to bring this scientific knowledge to the Spanish-speaking community."]], "1_corpus": "arxiv"}
+{"tstamp": 1726530434.1381, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "5b87141f46f04e1f9a5821d0e6b73ae2", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "whats the capital of california", "0_output": [["whats the capital of california", "Sacramento County, California\n\nSacramento County () is a county located in the U.S. state of California. As of the 2020 census, the population was 1,585,055. Its county seat is Sacramento, which has been the state capital of California since 1854.\nSacramento County is the central county of the Greater Sacramento metropolitan area. The county covers about  in the northern portion of the Central Valley, on into Gold Country. Sacramento County extends from the low delta lands between the Sacramento River and San Joaquin River, including Suisun Bay, north to about  beyond the State Capitol and east into the foothills of the Sierra Nevada Mountains. The southernmost portion of Sacramento County has direct access to San Francisco Bay. Since 2010, statewide droughts in California have further strained Sacramento County's water security."]], "0_corpus": "wikipedia", "1_conv_id": "fcb88aaa51f940c885d643b4823dd396", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "whats the capital of california", "1_output": [["whats the capital of california", "Sacramento, California\n\n( ; ) is the capital city of the U.S. state of California and the seat of government of Sacramento County. Located at the confluence of the Sacramento and American Rivers in Northern California's Sacramento Valley, Sacramento's 2020 population of 524,943 makes it the fourth-most populous city in Northern California, sixth-most populous city in the state, and the ninth-most populous state capital in the United States. Sacramento is the seat of the California Legislature and the Governor of California.\nSacramento is also the cultural and economic core of the Greater Sacramento area, which at the 2020 census had a population of 2,680,831, the fourth-largest metropolitan area in California.\nBefore the arrival of the Spanish, the area was inhabited by the Nisenan, Maidu, and other indigenous peoples of California. In 1808, Spanish cavalryman Gabriel Moraga surveyed and named the Río del Santísimo Sacramento (Sacramento River), after the Blessed Sacrament. In 1839, Juan Bautista Alvarado, Mexican governor of Alta California, granted the responsibility of colonizing the Sacramento Valley to Swiss-born Mexican citizen John Augustus Sutter, who subsequently established Sutter's Fort and the settlement at the Rancho Nueva Helvetia. Following the American Conquest of California and the 1848 Treaty of Guadalupe-Hidalgo, the waterfront developed by Sutter began to be developed and incorporated in 1850 as the City of Sacramento. In 1852, the city offered its county courthouse to the state of California to house the state legislature, resulting in the city becoming the permanent state capital in 1854 and ushering in the construction of a new state capitol building which was finished in 1874."]], "1_corpus": "wikipedia"}
+{"tstamp": 1726530458.1588, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "68bbf111a9b3404b8e6a0aea5c07e855", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "list of greek prime ministers", "0_output": [["list of greek prime ministers", "List of prime ministers of Greece\n\nThis is a list of the heads of government of the modern Greek state, from its establishment during the Greek Revolution to the present day. Although various official and semi-official appellations were used during the early decades of independent statehood, the title of prime minister has been the formal designation of the office at least since 1843. On dates, Greece officially adopted the Gregorian calendar on 16 February 1923 (which became 1 March). All dates prior to that, unless specifically denoted, are Old Style.\nColor key\nFirst Hellenic Republic (1822–1833)\nThe heads of government of the provisional Greek state during the Greek War of Independence, and the subsequent Hellenic State.\nKingdom of Greece – Wittelsbach dynasty (1833–1862)\nThe heads of government during the period of the Wittelsbach dynasty.\nKingdom of Greece – Glücksburg dynasty (1863–1924)\nThe heads of government during the first period of the Glücksburg dynasty.\nSecond Hellenic Republic (1924–1935)\nKingdom of Greece – Glücksburg dynasty restored (1935–1974)\nThe heads of government during the second period of the Glücksburg dynasty, including the rival governments during the Second World War and the Civil War, as well as the 1967–74 military regime.\nThird Hellenic Republic (1974–present)\nTimeline\n1820–1924"]], "0_corpus": "wikipedia", "1_conv_id": "4bb383ff7ff14d609b97bd333414fc02", "1_model_name": "BM25", "1_prompt": "list of greek prime ministers", "1_output": [["list of greek prime ministers", "Greece–Italy relations\n\nCooperation against Terrorism, Organised Crime, and Drug Trafficking (1986)\nHigh level visits\nJanuary 2006; state visit of the Greek President Karolos Papoulias to Rome.\nDecember 2006; Visit of the Italian Prime Minister Romano Prodi to Athens.\nMarch 2007; official visit of the Greek Foreign Minister Dora Bakoyannis to Rome.\nAugust 2007; meeting of the Greek Foreign Minister with her Italian counterpart Massimo D'Alema in Rome.\nSeptember 2008; state visit of the Italian President Giorgio Napolitano to Athens.\nAugust 2012; visit of the Greek Prime Minister Antonis Samaras to Rome.\nSeptember 2012; visit of the Greek President Karolos Papoulias to Italy.\nOctober 2013; meeting of the Greek Prime Minister Antonis Samaras with his Italian counterpart in Rome.\nJuly 2014; visit of the Greek Prime Minister Antonis Samaras to Italy.\nFebruary 2015; meeting of the Greek Prime Minister Alexis Tsipras with his Italian counterpart Matteo Renzi in Rome.\nTransportation\nThe Italian ports of Bari, Brindisi, Ancona, Venice and Trieste on the Adriatic Sea's Italian coast have daily passenger and freight ferries to the Greek ports of Corfu, Patra, Igoumenitsa and Kalamata, avoiding overland transit via the Balkan Peninsula.\nResident diplomatic missions\nGreece has an embassy in Rome and consulates-general in Milan and a consulate in Venice.\nItaly has an embassy in Athens."]], "1_corpus": "wikipedia"}
+{"tstamp": 1726530482.8063, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "40f25a0ab0d14ffd81ded2d5e07123d9", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "wikipedia", "0_output": [["wikipedia", "Wikipedia (disambiguation)\n\nWikipedia is a free, collaborative and multilingual Internet encyclopedia.\nWikipedia may also refer to:\nEnglish Wikipedia, Wikipedia's first and largest edition\n274301 Wikipedia, an asteroid\n\"Wikipedia\", a single by American singer Jean Deaux\n\"Wikipedia\", a single by Swedish rapper Jireel"]], "0_corpus": "wikipedia", "1_conv_id": "5d4f735f77de468e9d484f28a39a2d28", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "wikipedia", "1_output": [["wikipedia", "Wikipedia\n\nWikipedia is a free content online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and the wiki software MediaWiki. Wikipedia is the largest and most-read reference work in history, and is consistently ranked among the ten most visited websites; , it was ranked fifth by Semrush, and sixth by Similarweb. Founded by Jimmy Wales and Larry Sanger on January 15, 2001, Wikipedia has been hosted since 2003 by the Wikimedia Foundation, an American nonprofit organization funded mainly by donations from readers.\nInitially only available in English, editions of Wikipedia in more than 300 other languages have been developed. The English Wikipedia, with its  million articles, is the largest of the editions, which together comprise more than  articles and attract more than 1.5 billion unique device visits and 13 million edits per month (about 5edits per second on average)  . Roughly 25% of Wikipedia's traffic is from the United States, followed by Japan at 5.8%, the United Kingdom at 5.7%, Germany at 5%, Russia at 4.9%, and the remaining 54% split among other countries, according to Similarweb.\nWikipedia has been praised for its enablement of the democratization of knowledge, extent of coverage, unique structure, and culture. It has been criticized for exhibiting systemic bias, particularly gender bias against women and geographical bias against the Global South (Eurocentrism). While the reliability of Wikipedia was frequently criticized in the 2000s, it has improved over time, receiving greater praise from the late 2010s onward while becoming an important fact-checking site. Wikipedia has been censored by some national governments, ranging from specific pages to the entire site. Articles on breaking news are often accessed as sources for frequently updated information about those events.\nHistory"]], "1_corpus": "wikipedia"}
+{"tstamp": 1726530499.6911, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "e0607c58482f4cbf9ee31e6d4208601e", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "ελλαδα", "0_output": [["ελλαδα", "Greece\n\nGreece, officially the Hellenic Republic, is a country in Southeast Europe. Located on the southern tip of the Balkan peninsula, Greece shares land borders with Albania to the northwest, North Macedonia and Bulgaria to the north, and Turkey to the east. The Aegean Sea lies to the east of the mainland, the Ionian Sea to the west, and the Sea of Crete and the Mediterranean Sea to the south. Greece has the longest coastline on the Mediterranean Basin, featuring thousands of islands. The country comprises nine traditional geographic regions, and has a population of nearly 10.4 million. Athens is the nation's capital and largest city.\nGreece is considered the cradle of Western civilization, being the birthplace of democracy, Western philosophy, Western literature, historiography, political science, major scientific and mathematical principles, theatre, and the Olympic Games. From the eighth century BC, the Greeks were organised into various independent city-states known as poleis (singular polis) that spanned the Mediterranean and Black seas. Philip II of Macedon united most of present-day Greece in the fourth century BC, with his son Alexander the Great rapidly conquering much of the known ancient world from the eastern Mediterranean to northwestern India. The subsequent Hellenistic period saw the height of Greek culture and influence in antiquity. Greece was annexed by Rome in the second century BC, becoming an integral part of the Roman Empire and its continuation, the Byzantine Empire, which was predominately Greek in culture and language. The Greek Orthodox Church, which emerged in the first century AD, helped shape modern Greek identity and transmitted Greek traditions to the wider Orthodox world. After the Fourth Crusade in 1204, Latin possessions were established in parts of the Greek peninsula, but most of the area fell under Ottoman rule by the mid-15th century."]], "0_corpus": "wikipedia", "1_conv_id": "de0e396072f848498afe918e2d41bbc1", "1_model_name": "text-embedding-004", "1_prompt": "ελλαδα", "1_output": [["ελλαδα", "Ñ\n\nIn Microsoft Word,  can be typed by pressing Control-Shift-Tilde () and then an .\nOn Linux it can be created by pressing Ctrl+Shift+U and then typing '00d1' or '00f1', followed by space or Ctrl to end the character code input. This produces  or .\nAnother option (for any operating system) is to configure the system to use the US-International keyboard layout, with which  can be produced either by holding Alt Gr and then pressing N, or by typing the tilde () followed by .\nYet another option is to use a compose key (hardware-based or software-emulated). Pressing the compose key, then , and then  results in . A capital  can be substituted to produce , and in most cases the order of  and  can be reversed.\nUse in URLs\nmay be used in internationalized domain names, but it will have to be converted from Unicode to ASCII using Punycode during the registration process (i.e. from www.piñata.com to www.xn--piata-pta.com).\nIn URLs (except for the domain name),  may be replaced by %C3%91, and  by %C3%B1. This is not needed for newer browsers. The hex digits represent the UTF-8 encoding of  and . This feature allows almost any Unicode character to be encoded, and it is considered important to support languages other than English."]], "1_corpus": "wikipedia"}

data/retrieval_individual-fc2e4671-7e20-44ad-a373-899a077f1401.jsonl

@@ -102,3 +102,25 @@
 {"tstamp": 1726530177.9441, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1726530177.6295, "finish": 1726530177.9441, "ip": "", "conv_id": "3e99fd5c68e045a9b6824d2a69059f6f", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "who was the first president of the US", "output": [["who was the first president of the US", "Presidency of George Washington\n\nThe presidency of George Washington began on April 30, 1789, when Washington was inaugurated as the first president of the United States, and ended on March 4, 1797. Washington took office after the 1788–1789 presidential election, the nation's first quadrennial presidential election, in which he was elected unanimously by the Electoral College. Washington was re-elected unanimously in the 1792 presidential election and chose to retire after two terms. He was succeeded by his vice president, John Adams of the Federalist Party.\nWashington, who had established his preeminence among the new nation's Founding Fathers through his service as Commander-in-Chief of the Continental Army during the American Revolutionary War and as president of the 1787 constitutional convention, was widely expected to become the first president of the United States under the new Constitution, though he desired to retire from public life. In his first inaugural address, Washington expressed both his reluctance to accept the presidency and his inexperience with the duties of civil administration, though he proved an able leader."]], "corpus": "wikipedia"}
 {"tstamp": 1726530208.9191, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1726530208.3942, "finish": 1726530208.9191, "ip": "", "conv_id": "f4a4e47c9d0f437eb1bfcadd2c80431f", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "what is euler's theorem", "output": [["what is euler's theorem", "Euler's theorem\n\nIn number theory, Euler's theorem (also known as the Fermat–Euler theorem or Euler's totient theorem) states that, if  and  are coprime positive integers, then  is congruent to   modulo , where   denotes Euler's totient function; that is"]], "corpus": "wikipedia"}
 {"tstamp": 1726530208.9191, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1726530208.3942, "finish": 1726530208.9191, "ip": "", "conv_id": "7cace2aac72247ceb0a1d623f9ec2376", "model_name": "text-embedding-3-large", "prompt": "what is euler's theorem", "output": [["what is euler's theorem", "Euler's theorem\n\nIn number theory, Euler's theorem (also known as the Fermat–Euler theorem or Euler's totient theorem) states that, if  and  are coprime positive integers, then  is congruent to   modulo , where   denotes Euler's totient function; that is"]], "corpus": "wikipedia"}
+{"tstamp": 1726530235.1436, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1726530234.8995, "finish": 1726530235.1436, "ip": "", "conv_id": "6c152cf7e67b48cbb92c00ae197c4578", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "what does vae stand for", "output": [["what does vae stand for", "Title: Hierarchical Variational Autoencoder for Visual Counterfactuals\n\nAbstract: Conditional Variational Auto Encoders (VAE) are gathering significant attention as an Explainable Artificial Intelligence (XAI) tool. The codes in the latent space provide a theoretically sound way to produce counterfactuals, i.e. alterations resulting from an intervention on a targeted semantic feature. To be applied on real images more complex models are needed, such as Hierarchical CVAE. This comes with a challenge as the naive conditioning is no longer effective. In this paper we show how relaxing the effect of the posterior leads to successful counterfactuals and we introduce VAEX an Hierarchical VAE designed for this approach that can visually audit a classifier in applications."]], "corpus": "arxiv"}
+{"tstamp": 1726530235.1436, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1726530234.8995, "finish": 1726530235.1436, "ip": "", "conv_id": "a8810bee31fb4922b39be471be5f5dde", "model_name": "GritLM/GritLM-7B", "prompt": "what does vae stand for", "output": [["what does vae stand for", "Title: A Tutorial on VAEs: From Bayes' Rule to Lossless Compression\n\nAbstract: The Variational Auto-Encoder (VAE) is a simple, efficient, and popular deep maximum likelihood model. Though usage of VAEs is widespread, the derivation of the VAE is not as widely understood. In this tutorial, we will provide an overview of the VAE and a tour through various derivations and interpretations of the VAE objective. From a probabilistic standpoint, we will examine the VAE through the lens of Bayes' Rule, importance sampling, and the change-of-variables formula. From an information theoretic standpoint, we will examine the VAE through the lens of lossless compression and transmission through a noisy channel. We will then identify two common misconceptions over the VAE formulation and their practical consequences. Finally, we will visualize the capabilities and limitations of VAEs using a code example (with an accompanying Jupyter notebook) on toy 2D data."]], "corpus": "arxiv"}
+{"tstamp": 1726530265.485, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1726530265.0621, "finish": 1726530265.485, "ip": "", "conv_id": "64bf2b5642fb450eb2bc6564e03fdc59", "model_name": "voyage-multilingual-2", "prompt": "how can i calculate the algorithmic complexity of my code", "output": [["how can i calculate the algorithmic complexity of my code", "Stackoverflow Stackexchange\n\nQ: Software/script to calculate the computational complexity of code? Does anyone know of any program/script to calculate the computational complexity of code (e.g. method function) automatically?\nIf not, is there a good way (e.g. a design pattern, algorithm, etc) that supports it?\nI'm not trying to do this in general.\nIn most cases, I know the input, the algorithm running it, and what constitutes a halt. I'm trying to compare 2 or more algorithms this way.\nE.g.\nalgo #1 - 2x^2 + 10x + 5\n\nalgo #2 - 5x^2 + 1x + 3\n\nBoth algorithms are O(N^2). But algo #2 is better in the short run, while algo #1 is better in the long run.\n\nA: While it is impossible to develop an algorithm that solves your problem generally, you can write an algorithm that will calculate the complexity of a piece of software for a few example inputs. \nThe only software I can find reference to is called Trend-Profiler. But, if you are more interested in the algorithm than the result, there is a paper here that describes the software and its algorithm.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1726530265.485, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1726530265.0621, "finish": 1726530265.485, "ip": "", "conv_id": "54a3ba58a6884abe92d309652cc47df9", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "how can i calculate the algorithmic complexity of my code", "output": [["how can i calculate the algorithmic complexity of my code", "Stackoverflow Stackexchange\n\nQ: Software/script to calculate the computational complexity of code? Does anyone know of any program/script to calculate the computational complexity of code (e.g. method function) automatically?\nIf not, is there a good way (e.g. a design pattern, algorithm, etc) that supports it?\nI'm not trying to do this in general.\nIn most cases, I know the input, the algorithm running it, and what constitutes a halt. I'm trying to compare 2 or more algorithms this way.\nE.g.\nalgo #1 - 2x^2 + 10x + 5\n\nalgo #2 - 5x^2 + 1x + 3\n\nBoth algorithms are O(N^2). But algo #2 is better in the short run, while algo #1 is better in the long run.\n\nA: While it is impossible to develop an algorithm that solves your problem generally, you can write an algorithm that will calculate the complexity of a piece of software for a few example inputs. \nThe only software I can find reference to is called Trend-Profiler. But, if you are more interested in the algorithm than the result, there is a paper here that describes the software and its algorithm.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1726530299.5109, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1726530296.6225, "finish": 1726530299.5109, "ip": "", "conv_id": "d0f8f68e38f54a41a7493760f8fd70a2", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "chinchilla formula", "output": [["chinchilla formula", "Title: A formula for pi involving nested radicals\n\nAbstract: We present a new formula for pi involving nested radicals with rapid convergence. This formula is based on the arctangent function identity with argument $x=\\sqrt{2-{{a}_{k-1}}}/{{a}_{k}}$, where \\[ {{a}_{k}}=\\underbrace{\\sqrt{2+\\sqrt{2+\\sqrt{2+\\cdots +\\sqrt{2}}}}}_{k\\,\\,\\text{square}\\,\\,\\text{roots}} \\] is a nested radical consisting of $k$ square roots. The computational test we performed reveals that the proposed formula for pi provides a significant improvement in accuracy as the integer $k$ increases."]], "corpus": "arxiv"}
+{"tstamp": 1726530299.5109, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1726530296.6225, "finish": 1726530299.5109, "ip": "", "conv_id": "aa3c1b0cdda046c5bb31fbaf51c9676a", "model_name": "text-embedding-3-large", "prompt": "chinchilla formula", "output": [["chinchilla formula", "Title: Effect of a mixture of amoxicillin and norfloxacin in piglets feeding on productive performance and clinical signs\n\nAbstract: An experiment was conducted to evaluate the effect of feeding post-weaned piglets different levels of a commercial amoxicillin plus norfloxacin formula (Respirend) on the performance, clinical evidence of sickness, and the cost:benefit ratio. 454 PIC x Camborough female and castrated male, weaned at 21 d of age, with an average initial weight of 6.19 kg, were used in this experiment. Treatment 1 consisted in feeding a basal diet supplemented with 1000 g of a commercial tilmicosin product (Pulmotil) per metric tonne of feed (control group, from 21 to 35 d of age) and Treatments 2), 3) and 4) consisted in feeding the basal diet supplement with 500, 300 y 100 g of Respirend per metric tonne of feed, respectively, from 21 to 42 d of age. The results did not show significant differences (P<0.05) in mortality nor in parenteral medicine administration cost in none of the periods. Nevertheless, 500 g of Respirend per metric tonne of feed improved significantly (P<0.05) the general health status in the period between 21 and 35 d of age. For the whole evaluation period (21 to 42 d of age), this level improved significantly (P<0.05) the final body weight, the average daily weight gain, the average daily feed intake, the feed:gain ratio and optimized the cost:benefit ratio."]], "corpus": "arxiv"}
+{"tstamp": 1726530319.7886, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1726530319.5286, "finish": 1726530319.7886, "ip": "", "conv_id": "a255d45769e54fcebb317fd82572b217", "model_name": "embed-english-v3.0", "prompt": "attention", "output": [["attention", "Title: Copy this Sentence\n\nAbstract: Attention is an operation that selects some largest element from some set, where the notion of largest is defined elsewhere. Applying this operation to sequence to sequence mapping results in significant improvements to the task at hand. In this paper we provide the mathematical definition of attention and examine its application to sequence to sequence models. We highlight the exact correspondences between machine learning implementations of attention and our mathematical definition. We provide clear evidence of effectiveness of attention mechanisms evaluating models with varying degrees of attention on a very simple task: copying a sentence. We find that models that make greater use of attention perform much better on sequence to sequence mapping tasks, converge faster and are more stable."]], "corpus": "arxiv"}
+{"tstamp": 1726530319.7886, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1726530319.5286, "finish": 1726530319.7886, "ip": "", "conv_id": "1186330127d4445dac89ab090dc7ea9d", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "attention", "output": [["attention", "Title: A low energy neutrino factory with non-magnetic detectors\n\nAbstract: We show that a very precise neutrino/anti-neutrino event separation is not mandatory to cover the physics program of a low energy neutrino factory and thus non-magnetized detectors like water Cerenkov or liquid Argon detectors can be used. We point out, that oscillation itself strongly enhances the signal to noise ratio of a wrong sign muon search, provided there is sufficiently accurate neutrino energy reconstruction. Further, we argue that apart from a magnetic field, other means to distinguish neutrino from anti-neutrino events (at least statistically) can be explored. Combined with the fact that non-magnetic detectors potentially can be made very big, we show that modest neutrino/anti-neutrino separations at the level of 50% to 90% are sufficient to obtain good sensitivity to CP violation and the neutrino mass hierarchy for $\\sin^22\\theta_{13}>10^{-3}$. These non-magnetized detectors have a rich physics program outside the context of a neutrino factory, including topics like supernova neutrinos and proton decay. Hence, our observation opens the possibility to use a multi-purpose detector also in a neutrino factory beam."]], "corpus": "arxiv"}
+{"tstamp": 1726530343.1289, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1726530340.7809, "finish": 1726530343.1289, "ip": "", "conv_id": "f122225161aa4905a2c45533ac61bf06", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "attention is all you need", "output": [["attention is all you need", "Title: Concept based Attention\n\nAbstract: Attention endows animals an ability to concentrate on the most relevant information among a deluge of distractors at any given time, either through volitionally 'top-down' biasing, or driven by automatically 'bottom-up' saliency of stimuli, in favour of advantageous competition in neural modulations for information processing. Nevertheless, instead of being limited to perceive simple features, human and other advanced animals adaptively learn the world into categories and abstract concepts from experiences, imparting the world meanings. This thesis suggests that the high-level cognitive ability of human is more likely driven by attention basing on abstract perceptions, which is defined as concept based attention (CbA)."]], "corpus": "arxiv"}
+{"tstamp": 1726530343.1289, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1726530340.7809, "finish": 1726530343.1289, "ip": "", "conv_id": "7043aaaf939349c799e89142b6651ceb", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "attention is all you need", "output": [["attention is all you 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": 1726530357.8243, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1726530357.7122, "finish": 1726530357.8243, "ip": "", "conv_id": "a9912ad0d56b4498a9c82d99f44e4d76", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "transformer", "output": [["transformer", "Title: A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks\n\nAbstract: Poisoning attacks have emerged as a significant security threat to machine learning algorithms. It has been demonstrated that adversaries who make small changes to the training set, such as adding specially crafted data points, can hurt the performance of the output model. Some of the stronger poisoning attacks require the full knowledge of the training data. This leaves open the possibility of achieving the same attack results using poisoning attacks that do not have the full knowledge of the clean training set. In this work, we initiate a theoretical study of the problem above. Specifically, for the case of feature selection with LASSO, we show that full-information adversaries (that craft poisoning examples based on the rest of the training data) are provably stronger than the optimal attacker that is oblivious to the training set yet has access to the distribution of the data. Our separation result shows that the two setting of data-aware and data-oblivious are fundamentally different and we cannot hope to always achieve the same attack or defense results in these scenarios."]], "corpus": "arxiv"}
+{"tstamp": 1726530357.8243, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1726530357.7122, "finish": 1726530357.8243, "ip": "", "conv_id": "3ea6f36f643f4840a463b908b535d124", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "transformer", "output": [["transformer", "Title: Transformadores: Fundamentos teoricos y Aplicaciones\n\nAbstract: Transformers are a neural network architecture originally designed for natural language processing that it is now a mainstream tool for solving a wide variety of problems, including natural language processing, sound, image, reinforcement learning, and other problems with heterogeneous input data. Its distinctive feature is its self-attention system, based on attention to one's own sequence, which derives from the previously introduced attention system. This article provides the reader with the necessary context to understand the most recent research articles and presents the mathematical and algorithmic foundations of the elements that make up this type of network. The different components that make up this architecture and the variations that may exist are also studied, as well as some applications of the transformer models. This article is in Spanish to bring this scientific knowledge to the Spanish-speaking community."]], "corpus": "arxiv"}
+{"tstamp": 1726530418.7551, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726530416.6019, "finish": 1726530418.7551, "ip": "", "conv_id": "5b87141f46f04e1f9a5821d0e6b73ae2", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "whats the capital of california", "output": [["whats the capital of california", "Sacramento County, California\n\nSacramento County () is a county located in the U.S. state of California. As of the 2020 census, the population was 1,585,055. Its county seat is Sacramento, which has been the state capital of California since 1854.\nSacramento County is the central county of the Greater Sacramento metropolitan area. The county covers about  in the northern portion of the Central Valley, on into Gold Country. Sacramento County extends from the low delta lands between the Sacramento River and San Joaquin River, including Suisun Bay, north to about  beyond the State Capitol and east into the foothills of the Sierra Nevada Mountains. The southernmost portion of Sacramento County has direct access to San Francisco Bay. Since 2010, statewide droughts in California have further strained Sacramento County's water security."]], "corpus": "wikipedia"}
+{"tstamp": 1726530418.7551, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1726530416.6019, "finish": 1726530418.7551, "ip": "", "conv_id": "fcb88aaa51f940c885d643b4823dd396", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "whats the capital of california", "output": [["whats the capital of california", "Sacramento, California\n\n( ; ) is the capital city of the U.S. state of California and the seat of government of Sacramento County. Located at the confluence of the Sacramento and American Rivers in Northern California's Sacramento Valley, Sacramento's 2020 population of 524,943 makes it the fourth-most populous city in Northern California, sixth-most populous city in the state, and the ninth-most populous state capital in the United States. Sacramento is the seat of the California Legislature and the Governor of California.\nSacramento is also the cultural and economic core of the Greater Sacramento area, which at the 2020 census had a population of 2,680,831, the fourth-largest metropolitan area in California.\nBefore the arrival of the Spanish, the area was inhabited by the Nisenan, Maidu, and other indigenous peoples of California. In 1808, Spanish cavalryman Gabriel Moraga surveyed and named the Río del Santísimo Sacramento (Sacramento River), after the Blessed Sacrament. In 1839, Juan Bautista Alvarado, Mexican governor of Alta California, granted the responsibility of colonizing the Sacramento Valley to Swiss-born Mexican citizen John Augustus Sutter, who subsequently established Sutter's Fort and the settlement at the Rancho Nueva Helvetia. Following the American Conquest of California and the 1848 Treaty of Guadalupe-Hidalgo, the waterfront developed by Sutter began to be developed and incorporated in 1850 as the City of Sacramento. In 1852, the city offered its county courthouse to the state of California to house the state legislature, resulting in the city becoming the permanent state capital in 1854 and ushering in the construction of a new state capitol building which was finished in 1874."]], "corpus": "wikipedia"}
+{"tstamp": 1726530455.527, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1726530453.1748, "finish": 1726530455.527, "ip": "", "conv_id": "68bbf111a9b3404b8e6a0aea5c07e855", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "list of greek prime ministers", "output": [["list of greek prime ministers", "List of prime ministers of Greece\n\nThis is a list of the heads of government of the modern Greek state, from its establishment during the Greek Revolution to the present day. Although various official and semi-official appellations were used during the early decades of independent statehood, the title of prime minister has been the formal designation of the office at least since 1843. On dates, Greece officially adopted the Gregorian calendar on 16 February 1923 (which became 1 March). All dates prior to that, unless specifically denoted, are Old Style.\nColor key\nFirst Hellenic Republic (1822–1833)\nThe heads of government of the provisional Greek state during the Greek War of Independence, and the subsequent Hellenic State.\nKingdom of Greece – Wittelsbach dynasty (1833–1862)\nThe heads of government during the period of the Wittelsbach dynasty.\nKingdom of Greece – Glücksburg dynasty (1863–1924)\nThe heads of government during the first period of the Glücksburg dynasty.\nSecond Hellenic Republic (1924–1935)\nKingdom of Greece – Glücksburg dynasty restored (1935–1974)\nThe heads of government during the second period of the Glücksburg dynasty, including the rival governments during the Second World War and the Civil War, as well as the 1967–74 military regime.\nThird Hellenic Republic (1974–present)\nTimeline\n1820–1924"]], "corpus": "wikipedia"}
+{"tstamp": 1726530455.527, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1726530453.1748, "finish": 1726530455.527, "ip": "", "conv_id": "4bb383ff7ff14d609b97bd333414fc02", "model_name": "BM25", "prompt": "list of greek prime ministers", "output": [["list of greek prime ministers", "Greece–Italy relations\n\nCooperation against Terrorism, Organised Crime, and Drug Trafficking (1986)\nHigh level visits\nJanuary 2006; state visit of the Greek President Karolos Papoulias to Rome.\nDecember 2006; Visit of the Italian Prime Minister Romano Prodi to Athens.\nMarch 2007; official visit of the Greek Foreign Minister Dora Bakoyannis to Rome.\nAugust 2007; meeting of the Greek Foreign Minister with her Italian counterpart Massimo D'Alema in Rome.\nSeptember 2008; state visit of the Italian President Giorgio Napolitano to Athens.\nAugust 2012; visit of the Greek Prime Minister Antonis Samaras to Rome.\nSeptember 2012; visit of the Greek President Karolos Papoulias to Italy.\nOctober 2013; meeting of the Greek Prime Minister Antonis Samaras with his Italian counterpart in Rome.\nJuly 2014; visit of the Greek Prime Minister Antonis Samaras to Italy.\nFebruary 2015; meeting of the Greek Prime Minister Alexis Tsipras with his Italian counterpart Matteo Renzi in Rome.\nTransportation\nThe Italian ports of Bari, Brindisi, Ancona, Venice and Trieste on the Adriatic Sea's Italian coast have daily passenger and freight ferries to the Greek ports of Corfu, Patra, Igoumenitsa and Kalamata, avoiding overland transit via the Balkan Peninsula.\nResident diplomatic missions\nGreece has an embassy in Rome and consulates-general in Milan and a consulate in Venice.\nItaly has an embassy in Athens."]], "corpus": "wikipedia"}
+{"tstamp": 1726530478.0931, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1726530477.9417, "finish": 1726530478.0931, "ip": "", "conv_id": "40f25a0ab0d14ffd81ded2d5e07123d9", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "wikipedia", "output": [["wikipedia", "Wikipedia (disambiguation)\n\nWikipedia is a free, collaborative and multilingual Internet encyclopedia.\nWikipedia may also refer to:\nEnglish Wikipedia, Wikipedia's first and largest edition\n274301 Wikipedia, an asteroid\n\"Wikipedia\", a single by American singer Jean Deaux\n\"Wikipedia\", a single by Swedish rapper Jireel"]], "corpus": "wikipedia"}
+{"tstamp": 1726530478.0931, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726530477.9417, "finish": 1726530478.0931, "ip": "", "conv_id": "5d4f735f77de468e9d484f28a39a2d28", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "wikipedia", "output": [["wikipedia", "Wikipedia\n\nWikipedia is a free content online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and the wiki software MediaWiki. Wikipedia is the largest and most-read reference work in history, and is consistently ranked among the ten most visited websites; , it was ranked fifth by Semrush, and sixth by Similarweb. Founded by Jimmy Wales and Larry Sanger on January 15, 2001, Wikipedia has been hosted since 2003 by the Wikimedia Foundation, an American nonprofit organization funded mainly by donations from readers.\nInitially only available in English, editions of Wikipedia in more than 300 other languages have been developed. The English Wikipedia, with its  million articles, is the largest of the editions, which together comprise more than  articles and attract more than 1.5 billion unique device visits and 13 million edits per month (about 5edits per second on average)  . Roughly 25% of Wikipedia's traffic is from the United States, followed by Japan at 5.8%, the United Kingdom at 5.7%, Germany at 5%, Russia at 4.9%, and the remaining 54% split among other countries, according to Similarweb.\nWikipedia has been praised for its enablement of the democratization of knowledge, extent of coverage, unique structure, and culture. It has been criticized for exhibiting systemic bias, particularly gender bias against women and geographical bias against the Global South (Eurocentrism). While the reliability of Wikipedia was frequently criticized in the 2000s, it has improved over time, receiving greater praise from the late 2010s onward while becoming an important fact-checking site. Wikipedia has been censored by some national governments, ranging from specific pages to the entire site. Articles on breaking news are often accessed as sources for frequently updated information about those events.\nHistory"]], "corpus": "wikipedia"}
+{"tstamp": 1726530495.3519, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1726530495.0313, "finish": 1726530495.3519, "ip": "", "conv_id": "e0607c58482f4cbf9ee31e6d4208601e", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "ελλαδα", "output": [["ελλαδα", "Greece\n\nGreece, officially the Hellenic Republic, is a country in Southeast Europe. Located on the southern tip of the Balkan peninsula, Greece shares land borders with Albania to the northwest, North Macedonia and Bulgaria to the north, and Turkey to the east. The Aegean Sea lies to the east of the mainland, the Ionian Sea to the west, and the Sea of Crete and the Mediterranean Sea to the south. Greece has the longest coastline on the Mediterranean Basin, featuring thousands of islands. The country comprises nine traditional geographic regions, and has a population of nearly 10.4 million. Athens is the nation's capital and largest city.\nGreece is considered the cradle of Western civilization, being the birthplace of democracy, Western philosophy, Western literature, historiography, political science, major scientific and mathematical principles, theatre, and the Olympic Games. From the eighth century BC, the Greeks were organised into various independent city-states known as poleis (singular polis) that spanned the Mediterranean and Black seas. Philip II of Macedon united most of present-day Greece in the fourth century BC, with his son Alexander the Great rapidly conquering much of the known ancient world from the eastern Mediterranean to northwestern India. The subsequent Hellenistic period saw the height of Greek culture and influence in antiquity. Greece was annexed by Rome in the second century BC, becoming an integral part of the Roman Empire and its continuation, the Byzantine Empire, which was predominately Greek in culture and language. The Greek Orthodox Church, which emerged in the first century AD, helped shape modern Greek identity and transmitted Greek traditions to the wider Orthodox world. After the Fourth Crusade in 1204, Latin possessions were established in parts of the Greek peninsula, but most of the area fell under Ottoman rule by the mid-15th century."]], "corpus": "wikipedia"}
+{"tstamp": 1726530495.3519, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1726530495.0313, "finish": 1726530495.3519, "ip": "", "conv_id": "de0e396072f848498afe918e2d41bbc1", "model_name": "text-embedding-004", "prompt": "ελλαδα", "output": [["ελλαδα", "Ñ\n\nIn Microsoft Word,  can be typed by pressing Control-Shift-Tilde () and then an .\nOn Linux it can be created by pressing Ctrl+Shift+U and then typing '00d1' or '00f1', followed by space or Ctrl to end the character code input. This produces  or .\nAnother option (for any operating system) is to configure the system to use the US-International keyboard layout, with which  can be produced either by holding Alt Gr and then pressing N, or by typing the tilde () followed by .\nYet another option is to use a compose key (hardware-based or software-emulated). Pressing the compose key, then , and then  results in . A capital  can be substituted to produce , and in most cases the order of  and  can be reversed.\nUse in URLs\nmay be used in internationalized domain names, but it will have to be converted from Unicode to ASCII using Punycode during the registration process (i.e. from www.piñata.com to www.xn--piata-pta.com).\nIn URLs (except for the domain name),  may be replaced by %C3%91, and  by %C3%B1. This is not needed for newer browsers. The hex digits represent the UTF-8 encoding of  and . This feature allows almost any Unicode character to be encoded, and it is considered important to support languages other than English."]], "corpus": "wikipedia"}
+{"tstamp": 1726530516.1917, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1726530515.9239, "finish": 1726530516.1917, "ip": "", "conv_id": "62b852aa91cd4459bf91ba3407b32f39", "model_name": "text-embedding-004", "prompt": "τεχνητη νουμοσυνη", "output": [["τεχνητη νουμοσυνη", "Ñ\n\nIn Microsoft Word,  can be typed by pressing Control-Shift-Tilde () and then an .\nOn Linux it can be created by pressing Ctrl+Shift+U and then typing '00d1' or '00f1', followed by space or Ctrl to end the character code input. This produces  or .\nAnother option (for any operating system) is to configure the system to use the US-International keyboard layout, with which  can be produced either by holding Alt Gr and then pressing N, or by typing the tilde () followed by .\nYet another option is to use a compose key (hardware-based or software-emulated). Pressing the compose key, then , and then  results in . A capital  can be substituted to produce , and in most cases the order of  and  can be reversed.\nUse in URLs\nmay be used in internationalized domain names, but it will have to be converted from Unicode to ASCII using Punycode during the registration process (i.e. from www.piñata.com to www.xn--piata-pta.com).\nIn URLs (except for the domain name),  may be replaced by %C3%91, and  by %C3%B1. This is not needed for newer browsers. The hex digits represent the UTF-8 encoding of  and . This feature allows almost any Unicode character to be encoded, and it is considered important to support languages other than English."]], "corpus": "wikipedia"}
+{"tstamp": 1726530516.1917, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1726530515.9239, "finish": 1726530516.1917, "ip": "", "conv_id": "4c5302fcb7eb4166b33bd984a59b3d42", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "τεχνητη νουμοσυνη", "output": [["τεχνητη νουμοσυνη", "Ethics of technology\n\nDeepfakes Deepfake is a form of media in which one existing image or video is replaced or altered by someone else. Altering may include acting out fake content, false advertisement, hoaxes, and financial fraud. The technology of deepfakes may also use machine learning or artificial intelligence. Deepfakes propose an ethical dilemma due to how accessible they are as well as the implications on one's integrity it may cause to viewers. Deepfakes reconsider the challenge of trustworthiness of the visual experience and can create negative consequences. Deepfakes contribute to the problem of \"fake news\" by enabling both the more widespread fabrication or manipulation of media that may be deliberately used for the purposes of disinformation. There are four categories of deepfakes: deepfake porn, deepfake political campaigns, deepfake for commercial use, and creative deepfakes. Deepfakes have many harmful effects such as deception, intimidation, and reputational harm. Deception causes views to synthesize a form of reality that did not exist before and may think of it as real footage. The contents of the footage may be detrimental depending on what it is. Detrimental information may include fraudulent voter information, candidate information, money fraud, etc. Intimidation may occur by targeting a certain audience with harmful threats to generate fear. An example of intimidation may be deepfake revenge pornography which also ties into reputational harm."]], "corpus": "wikipedia"}