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 {"tstamp": 1723094784.4735, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1723094783.8607, "finish": 1723094784.4735, "ip": "", "conv_id": "4a10a067951246b3b9caec626adc8f8b", "model_name": "BM25", "prompt": "what type of coding scheme is used to prepare data for transmission or storage on a physical medium", "output": [["what type of coding scheme is used to prepare data for transmission or storage on a physical medium", "Line code\n\nTransmission and storage After line coding, the signal is put through a physical communication channel, either a transmission medium or data storage medium. The most common physical channels are:\nthe line-coded signal can directly be put on a transmission line, in the form of variations of the voltage or current (often using differential signaling).\nthe line-coded signal (the baseband signal) undergoes further pulse shaping (to reduce its frequency bandwidth) and then is modulated (to shift its frequency) to create an RF signal that can be sent through free space.\nthe line-coded signal can be used to turn on and off a light source in free-space optical communication, most commonly used in an infrared remote control.\nthe line-coded signal can be printed on paper to create a bar code.\nthe line-coded signal can be converted to magnetized spots on a hard drive or tape drive.\nthe line-coded signal can be converted to pits on an optical disc.\nSome of the more common binary line codes include:\nEach line code has advantages and disadvantages. Line codes are chosen to meet one or more of the following criteria:\nMinimize transmission hardware\nFacilitate synchronization\nEase error detection and correction\nAchieve a target spectral density\nEliminate a DC component"]], "corpus": "wikipedia"}
 {"tstamp": 1723094848.7283, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723094848.591, "finish": 1723094848.7283, "ip": "", "conv_id": "3f42d1721e62405b94ad31cf12186e73", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "which US president created the EPA", "output": [["which US president created the EPA", "United States Environmental Protection Agency\n\nThe Environmental Protection Agency (EPA) is an independent agency of the United States government tasked with environmental protection matters. President Richard Nixon proposed the establishment of EPA on July 9, 1970; it began operation on December 2, 1970, after Nixon signed an executive order. The order establishing the EPA was ratified by committee hearings in the House and Senate.\nThe agency is led by its administrator, who is appointed by the president and approved by the Senate. The current administrator is Michael S. Regan. The EPA is not a Cabinet department, but the administrator is normally given cabinet rank. The EPA has its headquarters in Washington, D.C.. There are regional offices for each of the agency's ten regions, as well as 27 laboratories around the country.\nThe agency conducts environmental assessment, research, and education. It has the responsibility of maintaining and enforcing national standards under a variety of environmental laws, in consultation with state, tribal, and local governments. EPA enforcement powers include fines, sanctions, and other measures.\nIt delegates some permitting, monitoring, and enforcement responsibility to U.S. states and the federally recognized tribes. The agency also works with industries and all levels of government in a wide variety of voluntary pollution prevention programs and energy conservation efforts."]], "corpus": "wikipedia"}
 {"tstamp": 1723094848.7283, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1723094848.591, "finish": 1723094848.7283, "ip": "", "conv_id": "2e26300716634d749db0e5d26982b3ef", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "which US president created the EPA", "output": [["which US president created the EPA", "United States Environmental Protection Agency\n\nFinally the Nixon administration made the environment a priority in 1969-1971 and set up a series of major agencies headed by the new EPA. The National Environmental Policy Act of 1969 (NEPA) President Nixon signed NEPA into law on January 1, 1970. The law created the Council on Environmental Quality (CEQ) in the Executive Office of the President. NEPA required that a detailed statement of environmental impacts be prepared for all major federal actions significantly affecting the environment. The \"detailed statement\" would ultimately be referred to as an environmental impact statement (EIS).\nEstablishment\nOn July 9, 1970, Nixon proposed an executive reorganization that consolidated many environmental responsibilities of the federal government under one agency, a new Environmental Protection Agency. This proposal included merging pollution control programs from a number of departments, such as the combination of pesticide programs from the United States Department of Agriculture and the United States Department of the Interior. After conducting hearings during that summer, the House and Senate approved the proposal. The EPA was created 90 days before it had to operate, and officially opened its doors on December 2, 1970. The agency's first administrator, William Ruckelshaus, took the oath of office on December 4, 1970."]], "corpus": "wikipedia"}
+{"tstamp": 1723099474.8717, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1723099474.5558, "finish": 1723099474.8717, "ip": "", "conv_id": "a0a6380f5a7e4df7bafdbb6640ac8753", "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": 1723099474.8717, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1723099474.5558, "finish": 1723099474.8717, "ip": "", "conv_id": "6ea2c93aba104681adabc1e463204208", "model_name": "Alibaba-NLP/gte-Qwen2-7B-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": 1723099534.0832, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1723099533.858, "finish": 1723099534.0832, "ip": "", "conv_id": "5880811a538d4375845ac62d23833282", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Please find me the paper on training code large language models to follow instructions via git commits.", "output": [["Please find me the paper on training code large language models to follow instructions via git commits.", "Title: OctoPack: Instruction Tuning Code Large Language Models\n\nAbstract: Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack."]], "corpus": "arxiv"}
+{"tstamp": 1723099534.0832, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1723099533.858, "finish": 1723099534.0832, "ip": "", "conv_id": "1871a3a8dd9546aa87435271717b0d63", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Please find me the paper on training code large language models to follow instructions via git commits.", "output": [["Please find me the paper on training code large language models to follow instructions via git commits.", "Title: OctoPack: Instruction Tuning Code Large Language Models\n\nAbstract: Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack."]], "corpus": "arxiv"}