Scheduled Commit

data/retrieval_battle-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -48,3 +48,5 @@
 {"tstamp": 1726864296.1547, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "95b45bf978d0470c8b51c07805aa08d1", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "Dimension reduction", "0_output": [["Dimension reduction", "Dimensionality reduction\n\nDimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with). Dimensionality reduction is common in fields that deal with large numbers of observations and/or large numbers of variables, such as signal processing, speech recognition, neuroinformatics, and bioinformatics.\nMethods are commonly divided into linear and nonlinear approaches. Approaches can also be divided into feature selection and feature extraction. Dimensionality reduction can be used for noise reduction, data visualization, cluster analysis, or as an intermediate step to facilitate other analyses.\nFeature selection\nFeature selection approaches try to find a subset of the input variables (also called features or attributes). The three strategies are: the filter strategy (e.g. information gain), the wrapper strategy (e.g. search guided by accuracy), and the embedded strategy (selected features are added or removed while building the model based on prediction errors).\nData analysis such as regression or classification can be done in the reduced space more accurately than in the original space."]], "0_corpus": "wikipedia", "1_conv_id": "33eec091c49041e699b3a5aa0cbb74f8", "1_model_name": "BM25", "1_prompt": "Dimension reduction", "1_output": [["Dimension reduction", "Dimensionality reduction\n\nPaCMAp PaCMAp (Pairwise Controlled Manifold Approximation) is a nonlinear dimensionality reduction method that can be used for visualization. A systematic evaluation of dimensionality reduction methods considering five components (preservation of local structure, preservation of global structure, sensitivity to parameter choices, sensitivity to preprocessing choices, and computational efficiency) revealed that PaCMAp is the technique that better preserves both global and local structures while being less sensitive to preprocessing choices.\nDimension reduction\nFor high-dimensional datasets (i.e. with number of dimensions more than 10), dimension reduction is usually performed prior to applying a K-nearest neighbors algorithm (k-NN) in order to avoid the effects of the curse of dimensionality.\nFeature extraction and dimension reduction can be combined in one step using principal component analysis (PCA), linear discriminant analysis (LDA), canonical correlation analysis (CCA), or non-negative matrix factorization (NMF) techniques as a pre-processing step followed by clustering by K-NN on feature vectors in reduced-dimension space. In machine learning this process is also called low-dimensional embedding.\nFor very-high-dimensional datasets (e.g. when performing similarity search on live video streams, DNA data or high-dimensional time series) running a fast approximate K-NN search using locality-sensitive hashing, random projection, \"sketches\", or other high-dimensional similarity search techniques from the VLDB conference toolbox might be the only feasible option.\nApplications\nA dimensionality reduction technique that is sometimes used in neuroscience is maximally informative dimensions, which finds a lower-dimensional representation of a dataset such that as much information as possible about the original data is preserved."]], "1_corpus": "wikipedia"}
 {"tstamp": 1726915656.3858, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "280eefc315fa4cde97ea348961228f50", "0_model_name": "voyage-multilingual-2", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "0_corpus": "stackexchange", "1_conv_id": "73e1fb0823854df58d11d23aca9b2451", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?\n\nA: Avoid tags that will inject content into the page:\n\n\n*\n\n*applet\n\n*script\n\n*style\n\n*link\n\n*iframe\n\n\nOther tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1726919547.7276, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5be8979bf65141eb925d40b2958d9007", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. "]], "0_corpus": "wikipedia", "1_conv_id": "086fdc19459f412c898919c67f64e7ea", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
+{"tstamp": 1726948701.5949, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "86dae06f57fa4405a7d58d4ae39ed5ee", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Meta programming framework improving multi-agent LLM collaborations in complex task handling", "0_output": [["Meta programming framework improving multi-agent LLM collaborations in complex task handling", "Title: MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework\n\nAbstract: Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. On collaborative software engineering benchmarks, MetaGPT generates more coherent solutions than previous chat-based multi-agent systems. Our project can be found at https://github.com/geekan/MetaGPT"]], "0_corpus": "arxiv", "1_conv_id": "5711733c8be44c76a7131eb8b2398403", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Meta programming framework improving multi-agent LLM collaborations in complex task handling", "1_output": [["Meta programming framework improving multi-agent LLM collaborations in complex task handling", "Title: MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework\n\nAbstract: Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. On collaborative software engineering benchmarks, MetaGPT generates more coherent solutions than previous chat-based multi-agent systems. Our project can be found at https://github.com/geekan/MetaGPT"]], "1_corpus": "arxiv"}
+{"tstamp": 1726948796.4012, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "b6495c1f18e64e7eb1b351c24864cc0c", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Как же грустно, что лето заканчивается... (Печаль)\nНе могу поверить, что он так со мной поступил! (Злоба)\nОн меня просто раздражает! (Враждебность)\nЧто-то мне не по себе сегодня... (Тревога)\nБоюсь, что завтра будет сложно... (Страх)\nНе могу перестать думать о том, как он смотрел на нее... (Зависть)\nМне так стыдно за то, что я сделал... (Раскаяние)\nМне совсем не нравится эта работа... (Неудовлетворение)\nВсе идет не так, как я хотел... (Пессимизм)\nУстал как собака после работы... (Усталость)\n", "0_output": [["Как же грустно, что лето заканчивается... (Печаль)\nНе могу поверить, что он так со мной поступил! (Злоба)\nОн меня просто раздражает! (Враждебность)\nЧто-то мне не по себе сегодня... (Тревога)\nБоюсь, что завтра будет сложно... (Страх)\nНе могу перестать думать о том, как он смотрел на нее... (Зависть)\nМне так стыдно за то, что я сделал... (Раскаяние)\nМне совсем не нравится эта работа... (Неудовлетворение)\nВсе идет не так, как я хотел... (Пессимизм)\nУстал как собака после работы... (Усталость)\n", "Ell Stackexchange\n\nQ: I 'still not' speak Russian or I 'still don't' speak Russian? Which of these sentences is the correct one? \n\nI still not speak Russian\nI still don't speak Russian?\n\nEven though normally I know that verbs that describe usual or routine things get the structure of present simple, in this case(?) I have doubt that when using \"still\" it is like a \"modal verb\", but I'm not sure. \n\nA: Negative phrases are most commonly formed with the auxiliary verb \"do\" in the negative: \"do not <verb>\".  Since 'speak' is the verb that you want to use, it needs to be preceded by \"do not\" (or \"don't\"), regardless of the other qualifiers like \"still\".\n\nI still don't speak Russian.\n\n(BTW, while it likely means you are unable to speak Russian, it can also mean that you are unwilling to speak it)\n\nThere are cases when a verb can be followed by \"not\" to make a negative, but those are few.  For instance, \"know\" can be followed by \"not\":\n\n\nI know not what to do.  \n\nbut it's mostly artistic, bookish way of saying \"I don't know...\"\n"]], "0_corpus": "stackexchange", "1_conv_id": "ef9ddfa9720b41ddab9be661e79622fa", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Как же грустно, что лето заканчивается... (Печаль)\nНе могу поверить, что он так со мной поступил! (Злоба)\nОн меня просто раздражает! (Враждебность)\nЧто-то мне не по себе сегодня... (Тревога)\nБоюсь, что завтра будет сложно... (Страх)\nНе могу перестать думать о том, как он смотрел на нее... (Зависть)\nМне так стыдно за то, что я сделал... (Раскаяние)\nМне совсем не нравится эта работа... (Неудовлетворение)\nВсе идет не так, как я хотел... (Пессимизм)\nУстал как собака после работы... (Усталость)\n", "1_output": [["Как же грустно, что лето заканчивается... (Печаль)\nНе могу поверить, что он так со мной поступил! (Злоба)\nОн меня просто раздражает! (Враждебность)\nЧто-то мне не по себе сегодня... (Тревога)\nБоюсь, что завтра будет сложно... (Страх)\nНе могу перестать думать о том, как он смотрел на нее... (Зависть)\nМне так стыдно за то, что я сделал... (Раскаяние)\nМне совсем не нравится эта работа... (Неудовлетворение)\nВсе идет не так, как я хотел... (Пессимизм)\nУстал как собака после работы... (Усталость)\n", "Stackoverflow Stackexchange\n\nQ: What is the difference between util.error and console.error in Node.js? What exactly is the difference between the util.error([...]) and console.error([...])?\nIn both of the methods it prints to stderr.\n\nA: Just in case anyone comes across this question again, util.error is DEPRECATED so stick to console.error!\n"]], "1_corpus": "stackexchange"}

data/retrieval_individual-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -204,3 +204,9 @@
 {"tstamp": 1726928577.644, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726928575.3786, "finish": 1726928577.644, "ip": "", "conv_id": "bd9fe7f502b2444fbd6e94db63472187", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "who led an attack that allowed for american victory at yorktown", "output": [["who led an attack that allowed for american victory at yorktown", "Siege of Yorktown\n\nThe siege of Yorktown, also known as the Battle of Yorktown and the surrender at Yorktown, began September 28, 1781, and ended on October 19, 1781, at exactly 10:30 am in Yorktown, Virginia. It was a decisive victory by a combined force of the American Continental Army troops led by General George Washington with support from the Marquis de Lafayette and French Army troops led by the Comte de Rochambeau and a French naval force commanded by the Comte de Grasse over the British Army commanded by British Lieutenant General Charles Cornwallis.\nThe siege of Yorktown was the last major land battle of the American Revolutionary War in North America, and led to the surrender of General Cornwallis and the capture of both him and his army. The Continental Army's victory at Yorktown prompted the British government to negotiate an end to the conflict.\nOverview\nIn 1780, about 5,500 French soldiers landed in Rhode Island to help their American allies fight the British troops controlling New York City. Following the arrival of dispatches from France that included the possibility of support from the French West Indies fleet of the Comte de Grasse, disagreements arose between Washington and Rochambeau on whether to ask de Grasse for assistance in besieging New York or in military operations against a British army in Virginia. On the advice of Rochambeau, de Grasse informed them of his intent to sail to the Chesapeake Bay, where Cornwallis had taken command of the army. Cornwallis, at first given confusing orders by his superior officer, Henry Clinton, was eventually ordered to build a defensible deep-water port, which he began to do in Yorktown. Cornwallis' movements in Virginia were shadowed by a Continental Army force led by Marquis de Lafayette."]], "corpus": "wikipedia"}
 {"tstamp": 1726946337.1455, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1726946334.8225, "finish": 1726946337.1455, "ip": "", "conv_id": "6d2162ddc00b4ae69535824e3726a9c2", "model_name": "GritLM/GritLM-7B", "prompt": "what does the law of multiple proportions state", "output": [["what does the law of multiple proportions state", "Law of multiple proportions\n\nIn chemistry, the law of multiple proportions states that in compounds which contain two particular chemical elements, the amount of Element A per measure of Element B will differ across these compounds by ratios of small whole numbers. For instance, the ratio of the hydrogen content in methane (CH4) and ethane (C2H6) per measure of carbon is 4:3. This law is also known as Dalton's Law, named after John Dalton, the chemist who first expressed it. The discovery of this pattern led Dalton to develop the modern theory of atoms, as it suggested that the elements combine with each other in multiples of a basic quantity."]], "corpus": "wikipedia"}
 {"tstamp": 1726946337.1455, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1726946334.8225, "finish": 1726946337.1455, "ip": "", "conv_id": "1dc3730c07a74dd4be27ab5a5e1af23f", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "what does the law of multiple proportions state", "output": [["what does the law of multiple proportions state", "Law of multiple proportions\n\nIn chemistry, the law of multiple proportions states that in compounds which contain two particular chemical elements, the amount of Element A per measure of Element B will differ across these compounds by ratios of small whole numbers. For instance, the ratio of the hydrogen content in methane (CH4) and ethane (C2H6) per measure of carbon is 4:3. This law is also known as Dalton's Law, named after John Dalton, the chemist who first expressed it. The discovery of this pattern led Dalton to develop the modern theory of atoms, as it suggested that the elements combine with each other in multiples of a basic quantity."]], "corpus": "wikipedia"}
+{"tstamp": 1726948693.8499, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1726948691.6273, "finish": 1726948693.8499, "ip": "", "conv_id": "86dae06f57fa4405a7d58d4ae39ed5ee", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Meta programming framework improving multi-agent LLM collaborations in complex task handling", "output": [["Meta programming framework improving multi-agent LLM collaborations in complex task handling", "Title: MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework\n\nAbstract: Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. On collaborative software engineering benchmarks, MetaGPT generates more coherent solutions than previous chat-based multi-agent systems. Our project can be found at https://github.com/geekan/MetaGPT"]], "corpus": "arxiv"}
+{"tstamp": 1726948693.8499, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1726948691.6273, "finish": 1726948693.8499, "ip": "", "conv_id": "5711733c8be44c76a7131eb8b2398403", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Meta programming framework improving multi-agent LLM collaborations in complex task handling", "output": [["Meta programming framework improving multi-agent LLM collaborations in complex task handling", "Title: MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework\n\nAbstract: Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. On collaborative software engineering benchmarks, MetaGPT generates more coherent solutions than previous chat-based multi-agent systems. Our project can be found at https://github.com/geekan/MetaGPT"]], "corpus": "arxiv"}
+{"tstamp": 1726948709.9155, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726948709.7129, "finish": 1726948709.9155, "ip": "", "conv_id": "2771bdeebfcb40f99150f40ae4658f7c", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "how does the xbox 360 controller work on the xbox one?", "output": [["how does the xbox 360 controller work on the xbox one?", "Gaming Stackexchange\n\nQ: Is the Xbox One controller compatible with Xbox 360? I simply do not have the finances available to test this out yet, so I am requesting help from someone who has the answer. I am aware of the original Xbox controller is not compatible with the 360, but does this hold true for the Xbox One controller on the 360?\n\nA: Xbox One controller WILL NOT work with the 360. I have both consoles and have tested. Microsoft stated that the controller shall not work on 360. Meaning Xbox One controller works only with Xbox One and 360 controller works with/only 360 console.\nBut PS4 Dualshock controller works on 360 not sure about Xbox One though.\nCited \n"]], "corpus": "stackexchange"}
+{"tstamp": 1726948709.9155, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1726948709.7129, "finish": 1726948709.9155, "ip": "", "conv_id": "da114b78db344f21bafa8fd24f0ef32b", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "how does the xbox 360 controller work on the xbox one?", "output": [["how does the xbox 360 controller work on the xbox one?", "Gaming Stackexchange\n\nQ: Is the Xbox One controller compatible with Xbox 360? I simply do not have the finances available to test this out yet, so I am requesting help from someone who has the answer. I am aware of the original Xbox controller is not compatible with the 360, but does this hold true for the Xbox One controller on the 360?\n\nA: Xbox One controller WILL NOT work with the 360. I have both consoles and have tested. Microsoft stated that the controller shall not work on 360. Meaning Xbox One controller works only with Xbox One and 360 controller works with/only 360 console.\nBut PS4 Dualshock controller works on 360 not sure about Xbox One though.\nCited \n"]], "corpus": "stackexchange"}
+{"tstamp": 1726948772.2361, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1726948770.0589, "finish": 1726948772.2361, "ip": "", "conv_id": "b6495c1f18e64e7eb1b351c24864cc0c", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Как же грустно, что лето заканчивается... (Печаль)\nНе могу поверить, что он так со мной поступил! (Злоба)\nОн меня просто раздражает! (Враждебность)\nЧто-то мне не по себе сегодня... (Тревога)\nБоюсь, что завтра будет сложно... (Страх)\nНе могу перестать думать о том, как он смотрел на нее... (Зависть)\nМне так стыдно за то, что я сделал... (Раскаяние)\nМне совсем не нравится эта работа... (Неудовлетворение)\nВсе идет не так, как я хотел... (Пессимизм)\nУстал как собака после работы... (Усталость)\n", "output": [["Как же грустно, что лето заканчивается... (Печаль)\nНе могу поверить, что он так со мной поступил! (Злоба)\nОн меня просто раздражает! (Враждебность)\nЧто-то мне не по себе сегодня... (Тревога)\nБоюсь, что завтра будет сложно... (Страх)\nНе могу перестать думать о том, как он смотрел на нее... (Зависть)\nМне так стыдно за то, что я сделал... (Раскаяние)\nМне совсем не нравится эта работа... (Неудовлетворение)\nВсе идет не так, как я хотел... (Пессимизм)\nУстал как собака после работы... (Усталость)\n", "Ell Stackexchange\n\nQ: I 'still not' speak Russian or I 'still don't' speak Russian? Which of these sentences is the correct one? \n\nI still not speak Russian\nI still don't speak Russian?\n\nEven though normally I know that verbs that describe usual or routine things get the structure of present simple, in this case(?) I have doubt that when using \"still\" it is like a \"modal verb\", but I'm not sure. \n\nA: Negative phrases are most commonly formed with the auxiliary verb \"do\" in the negative: \"do not <verb>\".  Since 'speak' is the verb that you want to use, it needs to be preceded by \"do not\" (or \"don't\"), regardless of the other qualifiers like \"still\".\n\nI still don't speak Russian.\n\n(BTW, while it likely means you are unable to speak Russian, it can also mean that you are unwilling to speak it)\n\nThere are cases when a verb can be followed by \"not\" to make a negative, but those are few.  For instance, \"know\" can be followed by \"not\":\n\n\nI know not what to do.  \n\nbut it's mostly artistic, bookish way of saying \"I don't know...\"\n"]], "corpus": "stackexchange"}
+{"tstamp": 1726948772.2361, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1726948770.0589, "finish": 1726948772.2361, "ip": "", "conv_id": "ef9ddfa9720b41ddab9be661e79622fa", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Как же грустно, что лето заканчивается... (Печаль)\nНе могу поверить, что он так со мной поступил! (Злоба)\nОн меня просто раздражает! (Враждебность)\nЧто-то мне не по себе сегодня... (Тревога)\nБоюсь, что завтра будет сложно... (Страх)\nНе могу перестать думать о том, как он смотрел на нее... (Зависть)\nМне так стыдно за то, что я сделал... (Раскаяние)\nМне совсем не нравится эта работа... (Неудовлетворение)\nВсе идет не так, как я хотел... (Пессимизм)\nУстал как собака после работы... (Усталость)\n", "output": [["Как же грустно, что лето заканчивается... (Печаль)\nНе могу поверить, что он так со мной поступил! (Злоба)\nОн меня просто раздражает! (Враждебность)\nЧто-то мне не по себе сегодня... (Тревога)\nБоюсь, что завтра будет сложно... (Страх)\nНе могу перестать думать о том, как он смотрел на нее... (Зависть)\nМне так стыдно за то, что я сделал... (Раскаяние)\nМне совсем не нравится эта работа... (Неудовлетворение)\nВсе идет не так, как я хотел... (Пессимизм)\nУстал как собака после работы... (Усталость)\n", "Stackoverflow Stackexchange\n\nQ: What is the difference between util.error and console.error in Node.js? What exactly is the difference between the util.error([...]) and console.error([...])?\nIn both of the methods it prints to stderr.\n\nA: Just in case anyone comes across this question again, util.error is DEPRECATED so stick to console.error!\n"]], "corpus": "stackexchange"}