Scheduled Commit

data/retrieval_battle-89f7d27e-df7d-414d-a213-dcd1c0b2f2a2.jsonl

@@ -35,3 +35,6 @@
 {"tstamp": 1730116241.9496, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "820c50ada44f46d0bbea99a0990cfe35", "0_model_name": "text-embedding-3-large", "0_prompt": "Is Jesus God?", "0_output": [["Is Jesus God?", "Title: Does God So Love the Multiverse?\n\nAbstract: Monotheistic religions such as Judaism and Christianity affirm that God loves all humans and created them in His image. However, we have learned from Darwin that we were not created separately from other life on earth. Some Christians opposed Darwinian evolution because it undercut certain design arguments for the existence of God. Today there is the growing idea that the fine-tuned constants of physics might be explained by a multiverse with very many different sets of constants of physics. Some Christians oppose the multiverse for similarly undercutting other design arguments for the existence of God. However, undercutting one argument does not disprove its conclusion. Here I argue that multiverse ideas, though not automatically a solution to the problems of physics, deserve serious consideration and are not in conflict with Christian theology as I see it. Although this paper as a whole is {\\it addressed} primarily to Christians in cosmology and others interested in the relation between the multiverse and theism, it should be of {\\it interest} to a wider audience. Proper subsets of this paper are addressed to other Christians, to other theists, to other cosmologists, to other scientists, and to others interested in the multiverse and theism."]], "0_corpus": "arxiv", "1_conv_id": "4f15cbff2bac404b950f7f26906f6d59", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Is Jesus God?", "1_output": [["Is Jesus God?", "Title: Google as God? Opportunities and Risks of the Information Age\n\nAbstract: If God did not exist - people would invent one. The development of human civilization requires mechanisms promoting cooperation and social order. One of these mechanisms is based on the idea that everything we do is seen and judged by God - bad deeds will be punished, while good ones will be rewarded. The Information Age has now fueled the dream that God-like omniscience and omnipotence can be created by man."]], "1_corpus": "arxiv"}
 {"tstamp": 1730119633.6467, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "0d117cf178b24419a69529a4d18dd51a", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "What is Ehrenmüller from Kempten?", "0_output": [["What is Ehrenmüller from Kempten?", "Schwäbisch Gmünd\n\nTheodor Schwenk (1910–1986), anthroposophist, engineer and pioneering water researcher\nKarl Ramsayer (1911–1982), geodesist\nHartmut Esslinger (born 1944), industrial designer, worked for Apple Computer Co in the early 1980s.\nNorbert Barthle (born 1952), politician, (CDU).\nMichael Braungart (born 1958), chemist, co-founder of the Chemistry Section of Greenpeace International\nRichard Arnold (born 1959), politician, (CDU), local Lord Mayor (Oberbürgermeister) since 2009\nThe Arts\nHans Judenkönig (–1526), lutenist\nJerg Ratgeb (–1526), painter\nHans Baldung (1484/85–1545), painter, printer, engraver, draftsman, and stained glass artist.\nEmanuel Leutze (1816–1868), history painter.\nJohannes Scherr (1817–1886), cultural historian, writer, literary critic and politician.\nHermann Pleuer (1863–1911), impressionist and landscape artist\nAron Strobel (born 1958), lead guitarist for Münchener Freiheit\nSport\nUwe Messerschmidt (born 1962), track cyclist and road bicycle racer, silver medallist at the 1984 Summer Olympics\nSimon Tischer (born 1982), volleyball player\nAndreas Hofmann (born 1986), footballer, played over 290 games\nDaniel Hägele (born 1989), footballer, played over 390 games\nKai Häfner (born 1989), handball player and team bronze medallist at the 2016 Summer Olympics\nJulian Grupp (born 1991), footballer, played over 250 games\nCarina Vogt (born 1992), former ski jumper, gold medallist at the 2014 Winter Olympics\nNiko Kappel (born 1995), a paralympic athlete, gold medallist at the shot put at the 2016 Summer Paralympics"]], "0_corpus": "wikipedia", "1_conv_id": "12ee4f4238c44bb7b75bc8187e2f7c88", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "What is Ehrenmüller from Kempten?", "1_output": [["What is Ehrenmüller from Kempten?", "Kempten\n\nKempten (, (Swabian German: )) is the largest town of Allgäu, in Swabia, Bavaria, Germany. The population was about 68,000 in 2016. The area was possibly settled originally by Celts, but was later taken over by the Romans, who called the town Cambodunum. Kempten is the oldest urban settlement (town) in Germany.\nHistory\nPre-Roman\nThe Greek geographer Strabo mentions in 50 BC a town of the Celtic Estiones named Kambodunon. This is considered the oldest written reference of any German city. So far no archaeological evidence could be found that this Celtic settlement really existed.\nRoman era\nIn 15 BC Roman troops led by Nero Claudius Drusus and his brother Tiberius conquered and destroyed an existing Celtic settlement.  Later the settlement was named Cambodunum.\nIn the following years the city was rebuilt on a classical Roman city plan with baths, forum and temples. Initially in wood, the city was later rebuilt in stone after a devastating fire that destroyed almost the entire city in the year 69 AD. The city possibly served as provincial capital of Raetia during the first century before Augsburg took over this role. Extensive archaeological excavations at the end of the 19th century and again during the 1950s at what were then the outskirts of Kempten unearthed the extensive structural foundations."]], "1_corpus": "wikipedia"}
 {"tstamp": 1730136516.6568, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "13f3da3ea83641af9012a7f631d1373b", "0_model_name": "BM25", "0_prompt": "what is the difference between state and status?", "0_output": [["what is the difference between state and status?", "Stackoverflow Stackexchange\n\nQ: What's the difference between 'state' and 'status' in function name? readyState\nstatusText\nonreadystatechange\nwindow.status\nWhat's the difference between 'state' and 'status' in English ?\n\nA: Not much.  I would tend to say state refers to more detailed information, but that's subjective.\nFor these:\nXMLHttpRequest.readyState - one of 5 possible states for a request\nXMLHttpRequest.statusText - The text equivalent of the HTTP response status (e.g. \"200 OK\")\nXMLHttpRequest.onreadystatechange - function to call when readyState changes\nwindow.status - status bar text (typically bottom left of window)\n"]], "0_corpus": "stackexchange", "1_conv_id": "aa03be823c0f4c20a4cfe3ff6baba50d", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "what is the difference between state and status?", "1_output": [["what is the difference between state and status?", "English Stackexchange\n\nQ: \"Status\" vs. \"state\" Can anyone explain what the difference between status and state is when I talk about the condition or situation of an object?\nHere's what I got from Longman English Dictionary.\n\nstatus: a situation at a particular time, especially in an argument,\n  discussion etc.\nstate: the physical or mental condition that someone or something is\n  in\n\nFor example, how do you interpret these two sentences:\n\n  \n*\n  \n*What is the current status of this project?\n  \n*What is the current state of this project?\n\n\nA: A hotel room might have a status of standard, de-luxe or honeymoon-suite.\nThat same room may have a state of being dishevelled or clean.\nA project progresses through a series of predefined stages. Its status tells you where it is in that series. Its state might be in disarray or on target regardless of status.\nIn considering this I asked myself two questions: What is the status of X? What sort of state is X in?\nIn reality, I suspect there is considerable overlap in the usage of state and status.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1730148684.0296, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "bd8c31026bce4901bc2bbb76d35debf3", "0_model_name": "voyage-multilingual-2", "0_prompt": "Apple founder", "0_output": [["Apple founder", "Steve Jobs\n\nSteven Paul Jobs (February 24, 1955 – October 5, 2011) was an American businessman, inventor, and investor best known for co-founding the technology company Apple Inc. Jobs was also the founder of NeXT and chairman and majority shareholder of Pixar. He was a pioneer of the personal computer revolution of the 1970s and 1980s, along with his early business partner and fellow Apple co-founder Steve Wozniak."]], "0_corpus": "wikipedia", "1_conv_id": "8d8a0e77a8f84f149a06295ad11d5318", "1_model_name": "embed-english-v3.0", "1_prompt": "Apple founder", "1_output": [["Apple founder", "Steve Jobs\n\nSteven Paul Jobs (February 24, 1955 – October 5, 2011) was an American businessman, inventor, and investor best known for co-founding the technology company Apple Inc. Jobs was also the founder of NeXT and chairman and majority shareholder of Pixar. He was a pioneer of the personal computer revolution of the 1970s and 1980s, along with his early business partner and fellow Apple co-founder Steve Wozniak."]], "1_corpus": "wikipedia"}
+{"tstamp": 1730148718.8999, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "651230bb92b34a33a66ad1a455ec3715", "0_model_name": "voyage-multilingual-2", "0_prompt": "where do secondary xylem and phloem cells arise from", "0_output": [["where do secondary xylem and phloem cells arise from", "Plant cell\n\nXylem Xylem is a complex vascular tissue composed of water-conducting tracheids or vessel elements, together with fibres and parenchyma cells. Tracheids are elongated cells with lignified secondary thickening of the cell walls, specialised for conduction of water, and first appeared in plants during their transition to land in the Silurian period more than 425 million years ago (see Cooksonia). The possession of xylem tracheids defines the vascular plants or Tracheophytes. Tracheids are pointed, elongated xylem cells, the simplest of which have continuous primary cell walls and lignified secondary wall thickenings in the form of rings, hoops, or reticulate networks. More complex tracheids with valve-like perforations called bordered pits characterise the gymnosperms. The ferns and other pteridophytes and the gymnosperms have only xylem tracheids, while the flowering plants also have xylem vessels. Vessel elements are hollow xylem cells without end walls that are aligned end-to-end so as to form long continuous tubes. The bryophytes lack true xylem tissue, but their sporophytes have a water-conducting tissue known as the hydrome that is composed of elongated cells of simpler construction.\nPhloem\nPhloem is a specialised tissue for food transport in higher plants, mainly transporting sucrose along pressure gradients generated by osmosis, a process called translocation. Phloem is a complex tissue, consisting of two main cell types, the sieve tubes and the intimately associated companion cells, together with parenchyma cells, phloem fibres and sclereids. Sieve tubes are joined end-to-end with perforated end-plates between known as sieve plates, which allow transport of photosynthate between the sieve elements. The sieve tube elements lack nuclei and ribosomes, and their metabolism and functions are regulated by the adjacent nucleate companion cells. The companion cells, connected to the sieve tubes via plasmodesmata, are responsible for loading the phloem with sugars. The bryophytes lack phloem, but moss sporophytes have a simpler tissue with analogous function known as the leptome."]], "0_corpus": "wikipedia", "1_conv_id": "cf1ecc854a13490f98e0dc915d461731", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "where do secondary xylem and phloem cells arise from", "1_output": [["where do secondary xylem and phloem cells arise from", "Vascular tissue\n\nBetween the xylem and phloem is a meristem called the vascular cambium. This tissue divides off cells that will become additional xylem and phloem. This growth increases the girth of the plant, rather than its length. As long as the vascular cambium continues to produce new cells, the plant will continue to grow more stout. In trees and other plants that develop wood, the vascular cambium allows the expansion of vascular tissue that produces woody growth. Because this growth ruptures the epidermis of the stem, woody plants also have a cork cambium that develops among the phloem. The cork cambium gives rise to thickened cork cells to protect the surface of the plant and reduce water loss.  Both the production of wood and the production of cork are forms of secondary growth.\nIn leaves, the vascular bundles are located among the spongy mesophyll. The xylem is oriented toward the adaxial surface of the leaf (usually the upper side), and phloem is oriented toward the abaxial surface of the leaf.  This is why aphids are typically found on the undersides of the leaves rather than on the top, since the phloem transports sugars manufactured by the plant and they are closer to the lower surface."]], "1_corpus": "wikipedia"}
+{"tstamp": 1730148760.0672, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "65ddd6c7f9d0464f9590670cd3796077", "0_model_name": "BM25", "0_prompt": "what is the difference between ms and me degree?", "0_output": [["what is the difference between ms and me degree?", "Math Stackexchange\n\nQ: Can I find the degree of an expression? Question: what is the degree of the following expression? $$\\frac{x^2+1}{6x-2}$$\nIf the above question has a degree, please tell me the difference between the degree of a polynomial and the degree of an expression?\n\nA: By one definition of degree, the degree of a rational function is the difference in the degrees between the numerator and the denominator. Then the degree here is $1$. This means that as $|x|\\to\\infty$ this behaves like a multiple of $x$, specifically $\\frac x6$.\n"]], "0_corpus": "stackexchange", "1_conv_id": "b31cec7c21f548b5a0ce7a7acc64bd28", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "what is the difference between ms and me degree?", "1_output": [["what is the difference between ms and me degree?", "Superuser Stackexchange\n\nQ: Difference between Intel MEI and Intel ME I have a 8th series Intel CPU and I want to Install the drivers for it but there are some programs similar to each other. one of them is Intel ME 1.5 MB and 5 MB and MEI. I searched for them and it appears that (5MB) version of ME has more features than 1.5 MB so its setup file is smaller (comparing to 90MB size of ME 5MB). so I installed the 5MB version and now I have Intel Security Assist and Intel Management Engine Components and Intel Anti Theft Technology. do I need to install MEI (Management Engine Interface) too? what's the advantage of installing them both? Thanks. \n\nA: To be honest, you don't NEED to install the MEI, but if you do, it has the ability to give you extended stats about Intel components. So as such, it is advisable.\n"]], "1_corpus": "stackexchange"}

data/retrieval_individual-89f7d27e-df7d-414d-a213-dcd1c0b2f2a2.jsonl

@@ -154,3 +154,10 @@
 {"tstamp": 1730136545.8336, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1730136545.5071, "finish": 1730136545.8336, "ip": "", "conv_id": "4e51f2fe17db46d69457a55c94d8c996", "model_name": "voyage-multilingual-2", "prompt": "A method of using pretrained language models without finetuning.", "output": [["A method of using pretrained language models without finetuning.", "Title: Masking as an Efficient Alternative to Finetuning for Pretrained Language Models\n\nAbstract: We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning. Extensive evaluations of masking BERT and RoBERTa on a series of NLP tasks show that our masking scheme yields performance comparable to finetuning, yet has a much smaller memory footprint when several tasks need to be inferred simultaneously. Through intrinsic evaluations, we show that representations computed by masked language models encode information necessary for solving downstream tasks. Analyzing the loss landscape, we show that masking and finetuning produce models that reside in minima that can be connected by a line segment with nearly constant test accuracy. This confirms that masking can be utilized as an efficient alternative to finetuning."]], "corpus": "arxiv"}
 {"tstamp": 1730144318.8751, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1730144312.4897, "finish": 1730144318.8751, "ip": "", "conv_id": "e13838f3fdf24ca8b126eaeb6c1fbe8b", "model_name": "BM25", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Michael E. Fisher - teacher, mentor, colleague and friend: a (very) personal account\n\nAbstract: The only rational way of educating is to be an example. If one cant help it, a warning example. Albert Einstein. I had the good fortune and privilege of having Michael Fisher as my teacher, supervisor, mentor and friend. During my years as a scientist, teacher and supervisor of about one hundred students and post docs I found myself innumerable times realizing that I am following or at least trying to follow Michaels example. These pages are my attempt to convey recollections of my association with Michael, focusing on how he served as an example for me."]], "corpus": "arxiv"}
 {"tstamp": 1730144318.8751, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1730144312.4897, "finish": 1730144318.8751, "ip": "", "conv_id": "4faffd868296404ebd0c36bebcf1ac8a", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Laws for Neural Language Models\n\nAbstract: We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence."]], "corpus": "arxiv"}
+{"tstamp": 1730148670.0161, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1730148667.5486, "finish": 1730148670.0161, "ip": "", "conv_id": "bd8c31026bce4901bc2bbb76d35debf3", "model_name": "voyage-multilingual-2", "prompt": "Apple founder", "output": [["Apple founder", "Steve Jobs\n\nSteven Paul Jobs (February 24, 1955 – October 5, 2011) was an American businessman, inventor, and investor best known for co-founding the technology company Apple Inc. Jobs was also the founder of NeXT and chairman and majority shareholder of Pixar. He was a pioneer of the personal computer revolution of the 1970s and 1980s, along with his early business partner and fellow Apple co-founder Steve Wozniak."]], "corpus": "wikipedia"}
+{"tstamp": 1730148670.0161, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1730148667.5486, "finish": 1730148670.0161, "ip": "", "conv_id": "8d8a0e77a8f84f149a06295ad11d5318", "model_name": "embed-english-v3.0", "prompt": "Apple founder", "output": [["Apple founder", "Steve Jobs\n\nSteven Paul Jobs (February 24, 1955 – October 5, 2011) was an American businessman, inventor, and investor best known for co-founding the technology company Apple Inc. Jobs was also the founder of NeXT and chairman and majority shareholder of Pixar. He was a pioneer of the personal computer revolution of the 1970s and 1980s, along with his early business partner and fellow Apple co-founder Steve Wozniak."]], "corpus": "wikipedia"}
+{"tstamp": 1730148698.287, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1730148697.8534, "finish": 1730148698.287, "ip": "", "conv_id": "651230bb92b34a33a66ad1a455ec3715", "model_name": "voyage-multilingual-2", "prompt": "where do secondary xylem and phloem cells arise from", "output": [["where do secondary xylem and phloem cells arise from", "Plant cell\n\nXylem Xylem is a complex vascular tissue composed of water-conducting tracheids or vessel elements, together with fibres and parenchyma cells. Tracheids are elongated cells with lignified secondary thickening of the cell walls, specialised for conduction of water, and first appeared in plants during their transition to land in the Silurian period more than 425 million years ago (see Cooksonia). The possession of xylem tracheids defines the vascular plants or Tracheophytes. Tracheids are pointed, elongated xylem cells, the simplest of which have continuous primary cell walls and lignified secondary wall thickenings in the form of rings, hoops, or reticulate networks. More complex tracheids with valve-like perforations called bordered pits characterise the gymnosperms. The ferns and other pteridophytes and the gymnosperms have only xylem tracheids, while the flowering plants also have xylem vessels. Vessel elements are hollow xylem cells without end walls that are aligned end-to-end so as to form long continuous tubes. The bryophytes lack true xylem tissue, but their sporophytes have a water-conducting tissue known as the hydrome that is composed of elongated cells of simpler construction.\nPhloem\nPhloem is a specialised tissue for food transport in higher plants, mainly transporting sucrose along pressure gradients generated by osmosis, a process called translocation. Phloem is a complex tissue, consisting of two main cell types, the sieve tubes and the intimately associated companion cells, together with parenchyma cells, phloem fibres and sclereids. Sieve tubes are joined end-to-end with perforated end-plates between known as sieve plates, which allow transport of photosynthate between the sieve elements. The sieve tube elements lack nuclei and ribosomes, and their metabolism and functions are regulated by the adjacent nucleate companion cells. The companion cells, connected to the sieve tubes via plasmodesmata, are responsible for loading the phloem with sugars. The bryophytes lack phloem, but moss sporophytes have a simpler tissue with analogous function known as the leptome."]], "corpus": "wikipedia"}
+{"tstamp": 1730148698.287, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1730148697.8534, "finish": 1730148698.287, "ip": "", "conv_id": "cf1ecc854a13490f98e0dc915d461731", "model_name": "GritLM/GritLM-7B", "prompt": "where do secondary xylem and phloem cells arise from", "output": [["where do secondary xylem and phloem cells arise from", "Vascular tissue\n\nBetween the xylem and phloem is a meristem called the vascular cambium. This tissue divides off cells that will become additional xylem and phloem. This growth increases the girth of the plant, rather than its length. As long as the vascular cambium continues to produce new cells, the plant will continue to grow more stout. In trees and other plants that develop wood, the vascular cambium allows the expansion of vascular tissue that produces woody growth. Because this growth ruptures the epidermis of the stem, woody plants also have a cork cambium that develops among the phloem. The cork cambium gives rise to thickened cork cells to protect the surface of the plant and reduce water loss.  Both the production of wood and the production of cork are forms of secondary growth.\nIn leaves, the vascular bundles are located among the spongy mesophyll. The xylem is oriented toward the adaxial surface of the leaf (usually the upper side), and phloem is oriented toward the abaxial surface of the leaf.  This is why aphids are typically found on the undersides of the leaves rather than on the top, since the phloem transports sugars manufactured by the plant and they are closer to the lower surface."]], "corpus": "wikipedia"}
+{"tstamp": 1730148737.5041, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1730148736.4914, "finish": 1730148737.5041, "ip": "", "conv_id": "65ddd6c7f9d0464f9590670cd3796077", "model_name": "BM25", "prompt": "what is the difference between ms and me degree?", "output": [["what is the difference between ms and me degree?", "Math Stackexchange\n\nQ: Can I find the degree of an expression? Question: what is the degree of the following expression? $$\\frac{x^2+1}{6x-2}$$\nIf the above question has a degree, please tell me the difference between the degree of a polynomial and the degree of an expression?\n\nA: By one definition of degree, the degree of a rational function is the difference in the degrees between the numerator and the denominator. Then the degree here is $1$. This means that as $|x|\\to\\infty$ this behaves like a multiple of $x$, specifically $\\frac x6$.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1730148737.5041, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1730148736.4914, "finish": 1730148737.5041, "ip": "", "conv_id": "b31cec7c21f548b5a0ce7a7acc64bd28", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "what is the difference between ms and me degree?", "output": [["what is the difference between ms and me degree?", "Superuser Stackexchange\n\nQ: Difference between Intel MEI and Intel ME I have a 8th series Intel CPU and I want to Install the drivers for it but there are some programs similar to each other. one of them is Intel ME 1.5 MB and 5 MB and MEI. I searched for them and it appears that (5MB) version of ME has more features than 1.5 MB so its setup file is smaller (comparing to 90MB size of ME 5MB). so I installed the 5MB version and now I have Intel Security Assist and Intel Management Engine Components and Intel Anti Theft Technology. do I need to install MEI (Management Engine Interface) too? what's the advantage of installing them both? Thanks. \n\nA: To be honest, you don't NEED to install the MEI, but if you do, it has the ability to give you extended stats about Intel components. So as such, it is advisable.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1730148798.1902, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1730148797.9914, "finish": 1730148798.1902, "ip": "", "conv_id": "e95e1b72ce7e4e919e3d71475296df75", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A novel method for document ranking using a sequence-to-sequence model instead of the typical encoder-only method.", "output": [["A novel method for document ranking using a sequence-to-sequence model instead of the typical encoder-only method.", "Title: Document Ranking with a Pretrained Sequence-to-Sequence Model\n\nAbstract: This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking. Our approach is fundamentally different from a commonly-adopted classification-based formulation of ranking, based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-to-sequence model can be trained to generate relevance labels as \"target words\", and how the underlying logits of these target words can be interpreted as relevance probabilities for ranking. On the popular MS MARCO passage ranking task, experimental results show that our approach is at least on par with previous classification-based models and can surpass them with larger, more-recent models. On the test collection from the TREC 2004 Robust Track, we demonstrate a zero-shot transfer-based approach that outperforms previous state-of-the-art models requiring in-dataset cross-validation. Furthermore, we find that our approach significantly outperforms an encoder-only model in a data-poor regime (i.e., with few training examples). We investigate this observation further by varying target words to probe the model's use of latent knowledge."]], "corpus": "arxiv"}

data/retrieval_single_choice-89f7d27e-df7d-414d-a213-dcd1c0b2f2a2.jsonl

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+{"tstamp": 1730148806.8588, "task_type": "retrieval", "type": "upvote", "models": "Alibaba-NLP/gte-Qwen2-7B-instruct", "ip": "", "conv_id": "e95e1b72ce7e4e919e3d71475296df75", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A novel method for document ranking using a sequence-to-sequence model instead of the typical encoder-only method.", "output": [["A novel method for document ranking using a sequence-to-sequence model instead of the typical encoder-only method.", "Title: Document Ranking with a Pretrained Sequence-to-Sequence Model\n\nAbstract: This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking. Our approach is fundamentally different from a commonly-adopted classification-based formulation of ranking, based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-to-sequence model can be trained to generate relevance labels as \"target words\", and how the underlying logits of these target words can be interpreted as relevance probabilities for ranking. On the popular MS MARCO passage ranking task, experimental results show that our approach is at least on par with previous classification-based models and can surpass them with larger, more-recent models. On the test collection from the TREC 2004 Robust Track, we demonstrate a zero-shot transfer-based approach that outperforms previous state-of-the-art models requiring in-dataset cross-validation. Furthermore, we find that our approach significantly outperforms an encoder-only model in a data-poor regime (i.e., with few training examples). We investigate this observation further by varying target words to probe the model's use of latent knowledge."]], "corpus": "arxiv"}