Datasets:

mteb
/

arena-results

@@ -30,3 +30,4 @@
 {"tstamp": 1737015761.6362, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "616f466dd0e6408c8d8a09f53fcc6958", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "what is the main ideas of attention mechanism in transformer architecture?", "0_output": [["what is the main ideas of attention mechanism in transformer architecture?", "Transformer (deep learning architecture)\n\nScaled dot-product attention The transformer building blocks are scaled dot-product attention units. For each attention unit, the transformer model learns three weight matrices: the query weights , the key weights , and the value weights . For each token , the input token representation  is multiplied with each of the three weight matrices to produce a query vector , a key vector , and a value vector . Attention weights are calculated using the query and key vectors: the attention weight  from token  to token  is the dot product between  and . The attention weights are divided by the square root of the dimension of the key vectors, , which stabilizes gradients during training, and passed through a softmax which normalizes the weights. The fact that  and  are different matrices allows attention to be non-symmetric: if token  attends to token  (i.e.  is large), this does not necessarily mean that token  will attend to token  (i.e.  could be small). The output of the attention unit for token  is the weighted sum of the value vectors of all tokens, weighted by , the attention from token  to each token.\nThe attention calculation for all tokens can be expressed as one large matrix calculation using the softmax function, which is useful for training due to computational matrix operation optimizations that quickly compute matrix operations. The matrices ,  and  are defined as the matrices where the th rows are vectors , , and  respectively. Then we can represent the attention as\nwhere softmax is taken over the horizontal axis.\nMulti-head attention\nOne set of  matrices is called an attention head, and each layer in a transformer model has multiple attention heads. While each attention head attends to the tokens that are relevant to each token, multiple attention heads allow the model to do this for different definitions of \"relevance\". In addition, the influence field representing relevance can become progressively dilated in successive layers. Many transformer attention heads encode relevance relations that are meaningful to humans. For example, some attention heads can attend mostly to the next word, while others mainly attend from verbs to their direct objects. The computations for each attention head can be performed in parallel, which allows for fast processing. The outputs for the attention layer are concatenated to pass into the feed-forward neural network layers."]], "0_corpus": "wikipedia", "1_conv_id": "ab67691881d741f89fae04d2cb8de022", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "what is the main ideas of attention mechanism in transformer architecture?", "1_output": [["what is the main ideas of attention mechanism in transformer architecture?", "Transformer (deep learning architecture)\n\nA transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper \"Attention Is All You Need\". Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism allowing the signal for key tokens to be amplified and less important tokens to be diminished. The transformer paper, published in 2017, is based on the softmax-based attention mechanism  proposed by Bahdanau et. al. in 2014 for machine translation, and the Fast Weight Controller, similar to a transformer, proposed in 1992.\nTransformers have the advantage of having no recurrent units, and therefore require less training time than earlier recurrent neural architectures such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLM) on large (language) datasets, such as the Wikipedia corpus and Common Crawl.\nThis architecture is now used not only in natural language processing and computer vision, but also in audio, multi-modal processing and robotics. It has also led to the development of pre-trained systems, such as generative pre-trained transformers (GPTs) and BERT (Bidirectional Encoder Representations from Transformers)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1737037318.7492, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8780e4c0c0ea4602af9e593ef89d005b", "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\nA similar tale can be found in the near contemporary Indian Buddhist Jataka tales, but here the intricacy of the automation does not match that of Master Yan.Prior to the introduction of Buddhism in the Common Era, Chinese philosophers did not seriously consider the distinction between appearance and reality. The Liezi rebuts Buddhist philosophies and likens human creative powers to that of the Creator. The Indian Lokapannatti, a collection of cycles and lores produced in the 11th or 12th century AD, tells the story of how an army of automated soldiers (bhuta vahana yanta or \"Spirit movement machines\") were crafted to protect the relics of Buddha in a secret stupa. The plans for making such humanoid automatons were stolen from the kingdom of Rome, a generic term for the Greco-Roman-Byzantine culture. According to the Lokapannatti, the Yavanas (\"Greek-speakers\") used the automatons to carry out trade and farming, but also captured and executed criminals. Roman automation makers who left the kingdom were pursued and killed by the automatons. According to the Lokapannatti, the emperor Asoka hears the story of the secret stupa and sets out to find it. "]], "0_corpus": "wikipedia", "1_conv_id": "76bbff4c96e64c408df551c98ea752cf", "1_model_name": "intfloat/e5-mistral-7b-instruct", "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": 1737037744.6476, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "159fb734d1c94391b30e3117f1ac30ed", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "why do I need to have `if __name__ == '__main__' in python?", "0_output": [["why do I need to have `if __name__ == '__main__' in python?", "Stackoverflow Stackexchange\n\nQ: How to initialize two-dimensional arrays in Fortran In C you can easily initialize an array using the curly braces syntax, if I remember correctly:\nint* a = new int[] { 1, 2, 3, 4 };\n\nHow can you do the same in Fortran for two-dimensional arrays when you wish to initialize a matrix with specific test values for mathematical purposes? (Without having to doubly index every element on separate statements)\nThe array is either defined by\nreal, dimension(3, 3) :: a\n\nor\nreal, dimension(:), allocatable :: a\n\n\nA: Array initialization can be done in the array declaration statement itself, as shown below:\nprogram test\n real:: x(3) = (/1,2,3/)\n real:: y(3,3) = reshape((/1,2,3,4,5,6,7,8,9/), (/3,3/))\n integer:: i(3,2,2) = reshape((/1,2,3,4,5,6,7,8,9,10,11,12/), (/3,2,2/))\n\nend program test\n\nIt surprises me that\n real:: y(3,3) = (/(/1,2,3/),(/4,5,6/),(/7,8,9/)/)\n\nis not accepted by the compiler (tried g95, gfortran). It turns out that the shape of\n(/(/1,2,3/),(/4,5,6/),(/7,8,9/)/) is 9 and not 3 3!\n"]], "0_corpus": "stackexchange", "1_conv_id": "0ed83a02e4c34e8da543c18a59af2fb7", "1_model_name": "voyage-multilingual-2", "1_prompt": "why do I need to have `if __name__ == '__main__' in python?", "1_output": [["why do I need to have `if __name__ == '__main__' in python?", "Stackoverflow Stackexchange\n\nQ: Python program start Should I start a Python program with:\nif__name__ == '__main__':\nsome code...\n\nAnd if so, why? I saw it many times but don't have a clue about it.\n\nA: If your program is usable as a library but you also have a main program (e.g. to test the library), that construct lets others import the file as a library and not run your main program. If your program is named foo.py and you do \"import foo\" from another python file, __name__ evaluates to 'foo', but if you run \"python foo.py\" from the command line, __name__ evaluates to '__main__'.\nNote that you do need to insert a space between if and _, and indent the main program:\nif __name__ == '__main__':\n    main program here\n\n"]], "1_corpus": "stackexchange"}

 {"tstamp": 1737015761.6362, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "616f466dd0e6408c8d8a09f53fcc6958", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "what is the main ideas of attention mechanism in transformer architecture?", "0_output": [["what is the main ideas of attention mechanism in transformer architecture?", "Transformer (deep learning architecture)\n\nScaled dot-product attention The transformer building blocks are scaled dot-product attention units. For each attention unit, the transformer model learns three weight matrices: the query weights , the key weights , and the value weights . For each token , the input token representation  is multiplied with each of the three weight matrices to produce a query vector , a key vector , and a value vector . Attention weights are calculated using the query and key vectors: the attention weight  from token  to token  is the dot product between  and . The attention weights are divided by the square root of the dimension of the key vectors, , which stabilizes gradients during training, and passed through a softmax which normalizes the weights. The fact that  and  are different matrices allows attention to be non-symmetric: if token  attends to token  (i.e.  is large), this does not necessarily mean that token  will attend to token  (i.e.  could be small). The output of the attention unit for token  is the weighted sum of the value vectors of all tokens, weighted by , the attention from token  to each token.\nThe attention calculation for all tokens can be expressed as one large matrix calculation using the softmax function, which is useful for training due to computational matrix operation optimizations that quickly compute matrix operations. The matrices ,  and  are defined as the matrices where the th rows are vectors , , and  respectively. Then we can represent the attention as\nwhere softmax is taken over the horizontal axis.\nMulti-head attention\nOne set of  matrices is called an attention head, and each layer in a transformer model has multiple attention heads. While each attention head attends to the tokens that are relevant to each token, multiple attention heads allow the model to do this for different definitions of \"relevance\". In addition, the influence field representing relevance can become progressively dilated in successive layers. Many transformer attention heads encode relevance relations that are meaningful to humans. For example, some attention heads can attend mostly to the next word, while others mainly attend from verbs to their direct objects. The computations for each attention head can be performed in parallel, which allows for fast processing. The outputs for the attention layer are concatenated to pass into the feed-forward neural network layers."]], "0_corpus": "wikipedia", "1_conv_id": "ab67691881d741f89fae04d2cb8de022", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "what is the main ideas of attention mechanism in transformer architecture?", "1_output": [["what is the main ideas of attention mechanism in transformer architecture?", "Transformer (deep learning architecture)\n\nA transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper \"Attention Is All You Need\". Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism allowing the signal for key tokens to be amplified and less important tokens to be diminished. The transformer paper, published in 2017, is based on the softmax-based attention mechanism  proposed by Bahdanau et. al. in 2014 for machine translation, and the Fast Weight Controller, similar to a transformer, proposed in 1992.\nTransformers have the advantage of having no recurrent units, and therefore require less training time than earlier recurrent neural architectures such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLM) on large (language) datasets, such as the Wikipedia corpus and Common Crawl.\nThis architecture is now used not only in natural language processing and computer vision, but also in audio, multi-modal processing and robotics. It has also led to the development of pre-trained systems, such as generative pre-trained transformers (GPTs) and BERT (Bidirectional Encoder Representations from Transformers)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1737037318.7492, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8780e4c0c0ea4602af9e593ef89d005b", "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\nA similar tale can be found in the near contemporary Indian Buddhist Jataka tales, but here the intricacy of the automation does not match that of Master Yan.Prior to the introduction of Buddhism in the Common Era, Chinese philosophers did not seriously consider the distinction between appearance and reality. The Liezi rebuts Buddhist philosophies and likens human creative powers to that of the Creator. The Indian Lokapannatti, a collection of cycles and lores produced in the 11th or 12th century AD, tells the story of how an army of automated soldiers (bhuta vahana yanta or \"Spirit movement machines\") were crafted to protect the relics of Buddha in a secret stupa. The plans for making such humanoid automatons were stolen from the kingdom of Rome, a generic term for the Greco-Roman-Byzantine culture. According to the Lokapannatti, the Yavanas (\"Greek-speakers\") used the automatons to carry out trade and farming, but also captured and executed criminals. Roman automation makers who left the kingdom were pursued and killed by the automatons. According to the Lokapannatti, the emperor Asoka hears the story of the secret stupa and sets out to find it. "]], "0_corpus": "wikipedia", "1_conv_id": "76bbff4c96e64c408df551c98ea752cf", "1_model_name": "intfloat/e5-mistral-7b-instruct", "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": 1737037744.6476, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "159fb734d1c94391b30e3117f1ac30ed", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "why do I need to have `if __name__ == '__main__' in python?", "0_output": [["why do I need to have `if __name__ == '__main__' in python?", "Stackoverflow Stackexchange\n\nQ: How to initialize two-dimensional arrays in Fortran In C you can easily initialize an array using the curly braces syntax, if I remember correctly:\nint* a = new int[] { 1, 2, 3, 4 };\n\nHow can you do the same in Fortran for two-dimensional arrays when you wish to initialize a matrix with specific test values for mathematical purposes? (Without having to doubly index every element on separate statements)\nThe array is either defined by\nreal, dimension(3, 3) :: a\n\nor\nreal, dimension(:), allocatable :: a\n\n\nA: Array initialization can be done in the array declaration statement itself, as shown below:\nprogram test\n real:: x(3) = (/1,2,3/)\n real:: y(3,3) = reshape((/1,2,3,4,5,6,7,8,9/), (/3,3/))\n integer:: i(3,2,2) = reshape((/1,2,3,4,5,6,7,8,9,10,11,12/), (/3,2,2/))\n\nend program test\n\nIt surprises me that\n real:: y(3,3) = (/(/1,2,3/),(/4,5,6/),(/7,8,9/)/)\n\nis not accepted by the compiler (tried g95, gfortran). It turns out that the shape of\n(/(/1,2,3/),(/4,5,6/),(/7,8,9/)/) is 9 and not 3 3!\n"]], "0_corpus": "stackexchange", "1_conv_id": "0ed83a02e4c34e8da543c18a59af2fb7", "1_model_name": "voyage-multilingual-2", "1_prompt": "why do I need to have `if __name__ == '__main__' in python?", "1_output": [["why do I need to have `if __name__ == '__main__' in python?", "Stackoverflow Stackexchange\n\nQ: Python program start Should I start a Python program with:\nif__name__ == '__main__':\nsome code...\n\nAnd if so, why? I saw it many times but don't have a clue about it.\n\nA: If your program is usable as a library but you also have a main program (e.g. to test the library), that construct lets others import the file as a library and not run your main program. If your program is named foo.py and you do \"import foo\" from another python file, __name__ evaluates to 'foo', but if you run \"python foo.py\" from the command line, __name__ evaluates to '__main__'.\nNote that you do need to insert a space between if and _, and indent the main program:\nif __name__ == '__main__':\n    main program here\n\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1737037783.8529, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "a8124f6a8d0c42c3bca43560f0726e5e", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "What is the eight fold path?", "0_output": [["What is the eight fold path?", "Noble Eightfold Path\n\nThe Noble Eightfold Path () or Eight Right Paths () is an early summary of the path of Buddhist practices leading to liberation from samsara, the painful cycle of rebirth, in the form of nirvana.\nThe Eightfold Path consists of eight practices: right view, right resolve, right speech, right conduct, right livelihood, right effort, right mindfulness, and right samadhi ('meditative absorption or union'; alternatively, equanimous meditative awareness)."]], "0_corpus": "wikipedia", "1_conv_id": "154387d22a3d4c79a85f3c907058d334", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "What is the eight fold path?", "1_output": [["What is the eight fold path?", "Six Paths\n\nThe Six Paths in Buddhist cosmology are the six worlds where sentient beings are reincarnated based on their karma, which is linked to their actions in previous lives. These paths are depicted in the Bhavacakra (\"wheel of existence\"). The six paths are:\nthe world of gods or celestial beings (deva) ;\nthe world of warlike demigods (asura) ;\nthe world of human beings (manushya) ;\nthe world of animals (tiryagyoni) ;\nthe world of hungry ghosts (preta) ;\nthe world of Hell (naraka)."]], "1_corpus": "wikipedia"}

data/retrieval_individual-dc61b353-c2a4-4719-875a-064e75e0f6f7.jsonl CHANGED Viewed

@@ -123,3 +123,5 @@
 {"tstamp": 1737037300.4587, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1737037298.2548, "finish": 1737037300.4587, "ip": "", "conv_id": "76bbff4c96e64c408df551c98ea752cf", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe 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)."]], "corpus": "wikipedia"}
 {"tstamp": 1737037702.5351, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1737037702.2303, "finish": 1737037702.5351, "ip": "", "conv_id": "159fb734d1c94391b30e3117f1ac30ed", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "why do I need to have `if __name__ == '__main__' in python?", "output": [["why do I need to have `if __name__ == '__main__' in python?", "Stackoverflow Stackexchange\n\nQ: How to initialize two-dimensional arrays in Fortran In C you can easily initialize an array using the curly braces syntax, if I remember correctly:\nint* a = new int[] { 1, 2, 3, 4 };\n\nHow can you do the same in Fortran for two-dimensional arrays when you wish to initialize a matrix with specific test values for mathematical purposes? (Without having to doubly index every element on separate statements)\nThe array is either defined by\nreal, dimension(3, 3) :: a\n\nor\nreal, dimension(:), allocatable :: a\n\n\nA: Array initialization can be done in the array declaration statement itself, as shown below:\nprogram test\n real:: x(3) = (/1,2,3/)\n real:: y(3,3) = reshape((/1,2,3,4,5,6,7,8,9/), (/3,3/))\n integer:: i(3,2,2) = reshape((/1,2,3,4,5,6,7,8,9,10,11,12/), (/3,2,2/))\n\nend program test\n\nIt surprises me that\n real:: y(3,3) = (/(/1,2,3/),(/4,5,6/),(/7,8,9/)/)\n\nis not accepted by the compiler (tried g95, gfortran). It turns out that the shape of\n(/(/1,2,3/),(/4,5,6/),(/7,8,9/)/) is 9 and not 3 3!\n"]], "corpus": "stackexchange"}
 {"tstamp": 1737037702.5351, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1737037702.2303, "finish": 1737037702.5351, "ip": "", "conv_id": "0ed83a02e4c34e8da543c18a59af2fb7", "model_name": "voyage-multilingual-2", "prompt": "why do I need to have `if __name__ == '__main__' in python?", "output": [["why do I need to have `if __name__ == '__main__' in python?", "Stackoverflow Stackexchange\n\nQ: Python program start Should I start a Python program with:\nif__name__ == '__main__':\nsome code...\n\nAnd if so, why? I saw it many times but don't have a clue about it.\n\nA: If your program is usable as a library but you also have a main program (e.g. to test the library), that construct lets others import the file as a library and not run your main program. If your program is named foo.py and you do \"import foo\" from another python file, __name__ evaluates to 'foo', but if you run \"python foo.py\" from the command line, __name__ evaluates to '__main__'.\nNote that you do need to insert a space between if and _, and indent the main program:\nif __name__ == '__main__':\n    main program here\n\n"]], "corpus": "stackexchange"}

 {"tstamp": 1737037300.4587, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1737037298.2548, "finish": 1737037300.4587, "ip": "", "conv_id": "76bbff4c96e64c408df551c98ea752cf", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe 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)."]], "corpus": "wikipedia"}
 {"tstamp": 1737037702.5351, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1737037702.2303, "finish": 1737037702.5351, "ip": "", "conv_id": "159fb734d1c94391b30e3117f1ac30ed", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "why do I need to have `if __name__ == '__main__' in python?", "output": [["why do I need to have `if __name__ == '__main__' in python?", "Stackoverflow Stackexchange\n\nQ: How to initialize two-dimensional arrays in Fortran In C you can easily initialize an array using the curly braces syntax, if I remember correctly:\nint* a = new int[] { 1, 2, 3, 4 };\n\nHow can you do the same in Fortran for two-dimensional arrays when you wish to initialize a matrix with specific test values for mathematical purposes? (Without having to doubly index every element on separate statements)\nThe array is either defined by\nreal, dimension(3, 3) :: a\n\nor\nreal, dimension(:), allocatable :: a\n\n\nA: Array initialization can be done in the array declaration statement itself, as shown below:\nprogram test\n real:: x(3) = (/1,2,3/)\n real:: y(3,3) = reshape((/1,2,3,4,5,6,7,8,9/), (/3,3/))\n integer:: i(3,2,2) = reshape((/1,2,3,4,5,6,7,8,9,10,11,12/), (/3,2,2/))\n\nend program test\n\nIt surprises me that\n real:: y(3,3) = (/(/1,2,3/),(/4,5,6/),(/7,8,9/)/)\n\nis not accepted by the compiler (tried g95, gfortran). It turns out that the shape of\n(/(/1,2,3/),(/4,5,6/),(/7,8,9/)/) is 9 and not 3 3!\n"]], "corpus": "stackexchange"}
 {"tstamp": 1737037702.5351, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1737037702.2303, "finish": 1737037702.5351, "ip": "", "conv_id": "0ed83a02e4c34e8da543c18a59af2fb7", "model_name": "voyage-multilingual-2", "prompt": "why do I need to have `if __name__ == '__main__' in python?", "output": [["why do I need to have `if __name__ == '__main__' in python?", "Stackoverflow Stackexchange\n\nQ: Python program start Should I start a Python program with:\nif__name__ == '__main__':\nsome code...\n\nAnd if so, why? I saw it many times but don't have a clue about it.\n\nA: If your program is usable as a library but you also have a main program (e.g. to test the library), that construct lets others import the file as a library and not run your main program. If your program is named foo.py and you do \"import foo\" from another python file, __name__ evaluates to 'foo', but if you run \"python foo.py\" from the command line, __name__ evaluates to '__main__'.\nNote that you do need to insert a space between if and _, and indent the main program:\nif __name__ == '__main__':\n    main program here\n\n"]], "corpus": "stackexchange"}
+{"tstamp": 1737037767.554, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1737037767.3095, "finish": 1737037767.554, "ip": "", "conv_id": "a8124f6a8d0c42c3bca43560f0726e5e", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What is the eight fold path?", "output": [["What is the eight fold path?", "Noble Eightfold Path\n\nThe Noble Eightfold Path () or Eight Right Paths () is an early summary of the path of Buddhist practices leading to liberation from samsara, the painful cycle of rebirth, in the form of nirvana.\nThe Eightfold Path consists of eight practices: right view, right resolve, right speech, right conduct, right livelihood, right effort, right mindfulness, and right samadhi ('meditative absorption or union'; alternatively, equanimous meditative awareness)."]], "corpus": "wikipedia"}
+{"tstamp": 1737037767.554, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1737037767.3095, "finish": 1737037767.554, "ip": "", "conv_id": "154387d22a3d4c79a85f3c907058d334", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "What is the eight fold path?", "output": [["What is the eight fold path?", "Six Paths\n\nThe Six Paths in Buddhist cosmology are the six worlds where sentient beings are reincarnated based on their karma, which is linked to their actions in previous lives. These paths are depicted in the Bhavacakra (\"wheel of existence\"). The six paths are:\nthe world of gods or celestial beings (deva) ;\nthe world of warlike demigods (asura) ;\nthe world of human beings (manushya) ;\nthe world of animals (tiryagyoni) ;\nthe world of hungry ghosts (preta) ;\nthe world of Hell (naraka)."]], "corpus": "wikipedia"}