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 {"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"}
+{"tstamp": 1723003556.2211, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1723003553.8521, "finish": 1723003556.2211, "ip": "", "conv_id": "4033af620c0b4bf3a153433643d20058", "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": 1723003556.2211, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723003553.8521, "finish": 1723003556.2211, "ip": "", "conv_id": "1ab4f79daaea4d2989fa78aafc49a42f", "model_name": "BAAI/bge-large-en-v1.5", "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": 1723003597.9188, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723003595.5796, "finish": 1723003597.9188, "ip": "", "conv_id": "12cfaacef313404ab4c8c005035947a1", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Cara menanam pisang ", "output": [["Cara menanam pisang ", "Latundan banana\n\nThe Latundan banana (also called Tundan, silk banana, Pisang raja sereh, Manzana banana, or apple banana) is a triploid hybrid banana cultivar of the AAB \"Pome\" group from the Philippines. It is one of the most common banana cultivars in Southeast Asia and the Philippines, along with Lacatan and Saba bananas. Its Malaysian name is pisang rastali.\nDescription\nLatundan banana plants typically reach a height of 3-4 meter (10-13 feet). They require full or partial sun exposure. The flowers are yellow, purple, or ivory in color. The fruits are round-tipped with thin yellow skin that splits once fully ripe. They are smaller than the Lacatan cultivar and the commercially dominant Cavendish bananas. They have a slightly acidic, apple-like flavor.\nTaxonomy\nIn older classifications, the Latundan cultivar was once the plant referred to as Musa sapientum. It has since been discovered that Musa sapientum is a hybrid cultivar of the wild seeded bananas Musa balbisiana and Musa acuminata and not a species.\nThe Latundan banana is a triploid (AAB) hybrid.\nIts full name is Musa acuminata × M. balbisiana (AAB Group) 'Silk'.\nUses\nCulinary\nLatundan bananas are popular dessert bananas that are eaten raw or cooked without the skin. The shoots and stalks are eaten cooked. In Myanmar and Thailand, the flowers are cooked and eaten in some recipes.\nHome Decor\nThey are also cultivated as ornamental plants.\nDiseases"]], "corpus": "wikipedia"}
+{"tstamp": 1723003597.9188, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1723003595.5796, "finish": 1723003597.9188, "ip": "", "conv_id": "1be0cc46e1d146a28860e14f96fa544d", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Cara menanam pisang ", "output": [["Cara menanam pisang ", "Banana fritter\n\nPisang goreng is usually sold together with other gorengan fritters including fried tofu and tempeh. However, Pisang goreng Pontianak are widely popular in Indonesia with certain retail outlets exclusively selling only this type of banana fritters.\nPisang goreng variants\nIndonesia has many varieties of pisang goreng, including:\nPisang goreng kipas or Pisang goreng Pontianak Refers to banana cut in the shape of a fan, battered, and deep fried. The term pisang goreng Pontianak is often used interchangeably with pisang goreng kipas, as both have a similar fan-like shape, but the former is often filled or served with kaya jam.\nPisang goreng pasir Literally meaning \"sandy fried banana\", bread crumbs are added to the batter, resulting in grainy, crispy crumbs on the skin, giving it a similar texture to croquette.\nPisang goreng kremes Javanese pisang goreng kremes is quite similar to pisang goreng pasir, but with a different batter composition and a different frying technique. The batter coating is made of rice flour, vanilla extract, and coconut milk, deep fried in an ample of hot cooking oil, creating crispy and crunchy kremes granules in the coating, and resulting in a sweet, fragrant aroma.\nPisang goreng madu Literally meaning \"honey fried banana\", honey is added into the batter, and prior to serving honey is drizzled upon the fried banana. The color is rather dark due to the caramelised honey."]], "corpus": "wikipedia"}