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 {"tstamp": 1739479296.6999, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1739479296.6084, "finish": 1739479296.6999, "ip": "", "conv_id": "b92926b48ebc4eab8d5aeaa9a4c81d15", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: Gauge fixing, BRS invariance and Ward identities for randomly stirred flows\n\nAbstract: The Galilean invariance of the Navier-Stokes equation is shown to be akin to a global gauge symmetry familiar from quantum field theory. This symmetry leads to a multiple counting of infinitely many inertial reference frames in the path integral approach to randomly stirred fluids. This problem is solved by fixing the gauge, i.e., singling out one reference frame. The gauge fixed theory has an underlying Becchi-Rouet-Stora (BRS) symmetry which leads to the Ward identity relating the exact inverse response and vertex functions. This identification of Galilean invariance as a gauge symmetry is explored in detail, for different gauge choices and by performing a rigorous examination of a discretized version of the theory. The Navier-Stokes equation is also invariant under arbitrary rectilinear frame accelerations, known as extended Galilean invariance (EGI). We gauge fix this extended symmetry and derive the generalized Ward identity that follows from the BRS invariance of the gauge-fixed theory. This new Ward identity reduces to the standard one in the limit of zero acceleration. This gauge-fixing approach unambiguously shows that Galilean invariance and EGI constrain only the zero mode of the vertex but none of the higher wavenumber modes."]], "corpus": "arxiv"}
 {"tstamp": 1739480897.9709, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1739480895.7817, "finish": 1739480897.9709, "ip": "", "conv_id": "71ad4569bd884ed19d0e6e944c3be8d4", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "what does it mean when someone says sure?", "output": [["what does it mean when someone says sure?", "Ell Stackexchange\n\nQ: Is it OK to respond to \"thanks\" with \"sure\"? When someone says\n\n..., thanks.\n\nIs it appropriate to respond with\n\nsure\n\nIf so, in which situations it would be OK, and what exactly would it imply?\n\nA: It is a perfectly normal response that people will understand if you say it to them. I say of course when someone says Thank you, and I see that as a normal response also. The same with no problem, you're welcome, yes, etc. I see it is as a much better response than receiving no response at all.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1739480897.9709, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1739480895.7817, "finish": 1739480897.9709, "ip": "", "conv_id": "aa4f0f6f6f4a457985f9c91b71a26da7", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "what does it mean when someone says sure?", "output": [["what does it mean when someone says sure?", "English Stackexchange\n\nQ: Can \"Sure\" be used to respond to \"Thanks\"? I often hear \"Sure\" in response when I say \"Thank you\" or \"Thanks\" to someone. I don't know — is this correct usage? If it is considered good, I'll use it someday.\n\nA: Yes, in the US at least. In the UK, it's unusual, on the other hand.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1739483787.8573, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1739483785.545, "finish": 1739483787.8573, "ip": "", "conv_id": "e182e26efd0d4a6ab3bd47ece9ce7438", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "corpus": "wikipedia"}
+{"tstamp": 1739483787.8573, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1739483785.545, "finish": 1739483787.8573, "ip": "", "conv_id": "8402c865852c4b9189e926e76304dac0", "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"}