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data/retrieval_individual-76c7b5f4-5fbc-4311-bf05-8defcdc1ba4e.jsonl

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 {"tstamp": 1722999098.0787, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722999095.8443, "finish": 1722999098.0787, "ip": "", "conv_id": "a145bc97fe314ce0b9c4d2b83f8377f2", "model_name": "GritLM/GritLM-7B", "prompt": "what is the magnitude and direction of acceleration?", "output": [["what is the magnitude and direction of acceleration?", "Acceleration\n\nIn mechanics, acceleration is the rate of change of the velocity of an object with respect to time. Acceleration is one of several components of kinematics, the study of motion. Accelerations are vector quantities (in that they have magnitude and direction). The orientation of an object's acceleration is given by the orientation of the net force acting on that object. The magnitude of an object's acceleration, as described by Newton's Second Law, is the combined effect of two causes:\nthe net balance of all external forces acting onto that object — magnitude is directly proportional to this net resulting force;\nthat object's mass, depending on the materials out of which it is made — magnitude is inversely proportional to the object's mass.\nThe SI unit for acceleration is metre per second squared (, )."]], "corpus": "wikipedia"}
 {"tstamp": 1722999128.7713, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722999128.4601, "finish": 1722999128.7713, "ip": "", "conv_id": "01f79a2dd2524ea992386e1b26589672", "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": 1722999128.7713, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722999128.4601, "finish": 1722999128.7713, "ip": "", "conv_id": "d255232afe114ce4b9faefdabac794dc", "model_name": "embed-english-v3.0", "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 test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
+{"tstamp": 1723001412.3474, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1723001410.0007, "finish": 1723001412.3474, "ip": "", "conv_id": "c4f3d89f32dd4b66854d816bb7071e74", "model_name": "voyage-multilingual-2", "prompt": "Is embedding vectors the only method to calculate similarity of text documents?", "output": [["Is embedding vectors the only method to calculate similarity of text documents?", "Stackoverflow Stackexchange\n\nQ: Best way to compare meaning of text documents? I'm trying to find the best way to compare two text documents using AI and machine learning methods. I've used the TF-IDF-Cosine Similarity and other similarity measures, but this compares the documents at a word (or n-gram) level.\nI'm looking for a method that allows me to compare the meaning of the documents. What is the best way to do that?\n\nA: You should start reading about word2vec model.\nuse gensim, get the pretrained model of google.\nFor vectoring a document, use Doc2vec() function. \nAfter getting vectors for all your document, use some distance metric like cosine distance or euclidean distance for comparison.  \n"]], "corpus": "stackexchange"}
+{"tstamp": 1723001412.3474, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723001410.0007, "finish": 1723001412.3474, "ip": "", "conv_id": "f26f551c048942b1ae4396ef0942d21c", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Is embedding vectors the only method to calculate similarity of text documents?", "output": [["Is embedding vectors the only method to calculate similarity of text documents?", "Stats Stackexchange\n\nQ: Other documents features than tf-idf for clustering? What are other feature representations for documents that are used for clustering textual documents? The only representation I'm aware of is tf-idf. Are there other ones?\n\nA: Technically, TF/IDF is not a feature extraction scheme.\nThe feature representation is \"the words in the document\". The TF/IDF part is the weighting of the individual features for similarity computation.\nThere are other representations, including bag of words, n-grams (really look this one up) and in a wider sense: minhash.\n"]], "corpus": "stackexchange"}