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 {"tstamp": 1732397393.2244, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1732397391.0425, "finish": 1732397393.2244, "ip": "", "conv_id": "638a80dada674cf7a35d0a9b7a8213ef", "model_name": "nomic-ai/nomic-embed-text-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": 1732398706.1551, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1732398703.8878, "finish": 1732398706.1551, "ip": "", "conv_id": "61aeb95a63e04d72a14db4f9391d1ed2", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "List of nearest terrestrial exoplanet candidates\n\nOn August 24, 2016, astronomers announced the discovery of a rocky planet in the habitable zone of Proxima Centauri, the closest star to Earth (not counting the Sun). Called Proxima b, the planet is 1.3 times the mass of Earth and has an orbital period of roughly 11.2 Earth days. However, Proxima Centauri's classification as a red dwarf casts doubts on the habitability of any exoplanets in its orbit due to low stellar flux, high probability of tidal locking, small circumstellar habitable zones and high stellar variation. Another likely candidate is Alpha Centauri, Earth's nearest Sun-like star system 4.37 light-years away. Estimates place the probability of finding a habitable planet around Alpha Centauri A or B at roughly 75%. Alpha Centauri is the target of several exoplanet-finding missions, including Breakthrough Starshot and Mission Centaur, the latter of which is chronicled in the 2016 documentary film The Search for Earth Proxima.\nData Table\nNote: There is no scientific consensus about terrestrial composition of most of the planets in the list. Sources in the \"Main source\" column confirm the possibility of terrestrial composition.\nIn September 2012, the discovery of two planets orbiting Gliese 163 was announced.  One of the planets, Gliese 163 c, about 6.9 times the mass of Earth and somewhat hotter, was considered to be within the habitable zone, but is probably not terrestrial.\nIn May 2016, the finding of three Earth-like planets of ultracool dwarf TRAPPIST-1 has been released.\nThe existence of the planet Gliese 832 c was refuted in 2022, when a study found that the radial velocity signal shows characteristics of a signal originating from stellar activity, and not from a planet.\nStatistics\nNote: in most cases the composition of the atmosphere and atmosphere pressure of exoplanets are unknown, so surface temperatures are estimates based on computer models and expert opinions."]], "corpus": "wikipedia"}
 {"tstamp": 1732398706.1551, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1732398703.8878, "finish": 1732398706.1551, "ip": "", "conv_id": "ac97b50ca5b14a06a929e284209d4a29", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "List of nearest terrestrial exoplanet candidates\n\nOn August 24, 2016, astronomers announced the discovery of a rocky planet in the habitable zone of Proxima Centauri, the closest star to Earth (not counting the Sun). Called Proxima b, the planet is 1.3 times the mass of Earth and has an orbital period of roughly 11.2 Earth days. However, Proxima Centauri's classification as a red dwarf casts doubts on the habitability of any exoplanets in its orbit due to low stellar flux, high probability of tidal locking, small circumstellar habitable zones and high stellar variation. Another likely candidate is Alpha Centauri, Earth's nearest Sun-like star system 4.37 light-years away. Estimates place the probability of finding a habitable planet around Alpha Centauri A or B at roughly 75%. Alpha Centauri is the target of several exoplanet-finding missions, including Breakthrough Starshot and Mission Centaur, the latter of which is chronicled in the 2016 documentary film The Search for Earth Proxima.\nData Table\nNote: There is no scientific consensus about terrestrial composition of most of the planets in the list. Sources in the \"Main source\" column confirm the possibility of terrestrial composition.\nIn September 2012, the discovery of two planets orbiting Gliese 163 was announced.  One of the planets, Gliese 163 c, about 6.9 times the mass of Earth and somewhat hotter, was considered to be within the habitable zone, but is probably not terrestrial.\nIn May 2016, the finding of three Earth-like planets of ultracool dwarf TRAPPIST-1 has been released.\nThe existence of the planet Gliese 832 c was refuted in 2022, when a study found that the radial velocity signal shows characteristics of a signal originating from stellar activity, and not from a planet.\nStatistics\nNote: in most cases the composition of the atmosphere and atmosphere pressure of exoplanets are unknown, so surface temperatures are estimates based on computer models and expert opinions."]], "corpus": "wikipedia"}
+{"tstamp": 1732409086.3431, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1732409083.4236, "finish": 1732409086.3431, "ip": "", "conv_id": "fa161f2ceabc4ed4be8ab322853c837e", "model_name": "BM25", "prompt": "Analysis of phrasal representation in pre-trained transformer models", "output": [["Analysis of phrasal representation in pre-trained transformer models", "BERT (language model)\n\nThe high performance of the BERT model could also be attributed to the fact that it is bidirectionally trained. This means that BERT, based on the Transformer model architecture, applies its self-attention mechanism to learn information from a text from the left and right side during training, and consequently gains a deep understanding of the context. For example, the word fine can have two different meanings depending on the context (I feel fine today, She has fine blond hair).  BERT considers the words surrounding the target word fine from the left and right side.\nHowever it comes at a cost: due to encoder-only architecture lacking a decoder, BERT can't be prompted and can't generate text, while bidirectional models in general do not work effectively without the right side, thus being difficult to prompt, with even short text generation requiring sophisticated computationally expensive techniques.\nIn contrast to deep learning neural networks which require very large amounts of data, BERT has already been pre-trained which means that it has learnt the representations of the words and sentences as well as the underlying semantic relations that they are connected with. BERT can then be fine-tuned on smaller datasets for specific tasks such as sentiment classification. The pre-trained models are chosen according to the content of the given dataset one uses but also the goal of the task. For example, if the task is a sentiment classification task on financial data, a pre-trained model for the analysis of sentiment of financial text should be chosen. The weights of the original pre-trained models were released on GitHub."]], "corpus": "wikipedia"}
+{"tstamp": 1732409086.3431, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1732409083.4236, "finish": 1732409086.3431, "ip": "", "conv_id": "55e6e19b61254858860338718a6337a9", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Analysis of phrasal representation in pre-trained transformer models", "output": [["Analysis of phrasal representation in pre-trained transformer models", "BERT (language model)\n\nBidirectional Encoder Representations from Transformers (BERT) is a language model based on the transformer architecture, notable for its dramatic improvement over previous state of the art models. It was introduced in October 2018 by researchers at Google. A 2020 literature survey concluded that \"in a little over a year, BERT has become a ubiquitous baseline in Natural Language Processing (NLP) experiments counting over 150 research publications analyzing and improving the model.\"\nBERT was originally implemented in the English language at two model sizes: (1) BERTBASE: 12 encoders with 12 bidirectional self-attention heads totaling 110 million parameters, and (2) BERTLARGE: 24 encoders with 16 bidirectional self-attention heads totaling 340 million parameters. Both models were pre-trained on the Toronto BookCorpus (800M words) and English Wikipedia  (2,500M words).\nDesign\nBERT is an \"encoder-only\" transformer architecture."]], "corpus": "wikipedia"}