Scheduled Commit

data/retrieval_battle-031dca12-9a97-4178-b56e-afd2ee86cecb.jsonl

@@ -13,3 +13,4 @@
 {"tstamp": 1735816406.9167, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "c5173fcf718f4d2bb7b77581438cde67", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "how i can put an LLM model on android ", "0_output": [["how i can put an LLM model on android ", "Large language model\n\nA large language model (LLM) is a computational model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. Based on language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a computationally intensive self-supervised and semi-supervised training process. LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word.\nLLMs are artificial neural networks that utilize the transformer architecture, invented in 2017. The largest and most capable LLMs, , are built with a decoder-only transformer-based architecture, which enables efficient processing and generation of large-scale text data."]], "0_corpus": "wikipedia", "1_conv_id": "d97a2b54c7ec47daa4a7ad959daf93ed", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "how i can put an LLM model on android ", "1_output": [["how i can put an LLM model on android ", "Large language model\n\nFlamingo demonstrated the effectiveness of the tokenization method, finetuning a pair of pretrained language model and image encoder to perform better on visual question answering than models trained from scratch. Google PaLM model was fine-tuned into a multimodal model PaLM-E using the tokenization method, and applied to robotic control. LLaMA models have also been turned multimodal using the tokenization method, to allow image inputs, and video inputs.\nGPT-4 can use both text and image as inputs (although the vision component was not released to the public until GPT-4V); Google DeepMind's Gemini is also multimodal.\nProperties\nScaling laws\nThe following four hyper-parameters characterize an LLM:\ncost of (pre-)training (),\nsize of the artificial neural network itself, such as number of parameters  (i.e. amount of neurons in its layers, amount of weights between them and biases),\nsize of its (pre-)training dataset (i.e. number of tokens in corpus, ),\nperformance after (pre-)training.\nThey are related by simple statistical laws, called \"scaling laws\". One particular scaling law (\"Chinchilla scaling\") for LLM autoregressively trained for one epoch, with a log-log learning rate schedule, states that:\nwhere the variables are\nis the cost of training the model, in FLOPs.\nis the number of parameters in the model.\nis the number of tokens in the training set.\nis the average negative log-likelihood loss per token (nats/token), achieved by the trained LLM on the test dataset.\nand the statistical hyper-parameters are"]], "1_corpus": "wikipedia"}
 {"tstamp": 1735867861.6484, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1775dfab1ced4b1fb9d034ba0107a941", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "docker-compose", "0_output": [["docker-compose", "Stackoverflow Stackexchange\n\nQ: Static function declared but not defined in C++ I'm getting an error from the following code using C++.\nMain.cpp\n#include \"file.h\"\n\nint main()\n{\n   int k = GetInteger();\n   return 0;\n}\n\nFile.h\nstatic int GetInteger();\n\nFile.cpp\n#include \"file.h\"\n\nstatic int GetInteger()\n{\n   return 1;\n}\n\nThe error I get:\nError C2129: static function 'int GetInteger(void)' declared but not defined.\n\nI've read the famous article \"Organizing Code File in C and C++\", but don't understand what is wrong with this code.\n\nA: Because in this case, static means that the name of the function has\ninternal linkage; that GetInteger in one translation unit is unrelated\nto GetInteger in any other translation unit.  The keyword static is\noverloaded: in some cases, it affects lifetime, and in others, binding.\nIt's particularly confusing here, because \"static\" is also the name of a\nlifetime.  Functions, and data declared at namespace scope, always\nhave static lifetime; when static appears in their declaration, it\ncauses internal binding, instead of external. \n"]], "0_corpus": "stackexchange", "1_conv_id": "a2388458dae64dc2a436d25afca4a5b0", "1_model_name": "BM25", "1_prompt": "docker-compose", "1_output": [["docker-compose", "Stackoverflow Stackexchange\n\nQ: docker compose inside docker in a docker I am pretty new to docker and was following the documentation found here, trying deploy several containers inside dind using docker-compose 1.14.0 I get the following \ndocker run -v /home/dudarev/compose/:/compose/ --privileged docker:dind /compose/docker-compose\n/usr/local/bin/dockerd-entrypoint.sh: exec: line 21: /compose/docker-compose: not found\n\nDid I miss something?\n\nA: Add docker-compose installation to your Dockerfile before executing docker run. \nFor example, if you have an Ubuntu docker, add to your Dockerfile:\nRUN aptitude -y install docker-compose\nRUN ln -s /usr/local/bin/docker-compose /compose/docker-compose\n\nBecause it looks like if your entry-point looks up docker compose in /compose folder, while docker-compose is installed in /usr/local/bin by default.\n\nIf you want a concrete docker-compose version (for example 1.20.0-rc2):\n\nRUN curl -L https://github.com/docker/compose/releases/download/1.20.0-rc2/docker-compose-`uname -s`-`uname -m` -o /compose/docker-compose\nRUN chmod +x /compose/docker-compose\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1735897301.5128, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "9a463b1c10354845b1a960a1a1dd52bf", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "0_output": [["Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "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"]], "0_corpus": "wikipedia", "1_conv_id": "1aa53bd671a2435987c08395357601f4", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "1_output": [["Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "Computing Machinery and Intelligence\n\n\"Computing Machinery and Intelligence\" is a seminal paper written by Alan Turing on the topic of artificial intelligence. The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public.\nTuring's paper considers the question \"Can machines think?\" Turing says that since the words \"think\" and \"machine\" cannot be clearly defined we should \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" To do this, he must first find a simple and unambiguous idea to replace the word \"think\", second he must explain exactly which \"machines\" he is considering, and finally, armed with these tools, he formulates a new question, related to the first, that he believes he can answer in the affirmative."]], "1_corpus": "wikipedia"}
+{"tstamp": 1735932045.7805, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "98144d9d0aa54f6bba70378e6d5933da", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "what openai embedding dimension to use", "0_output": [["what openai embedding dimension to use", "Word embedding\n\nIn natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.\nMethods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in terms of the context in which words appear.\nWord and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis.\nDevelopment and history of the approach\nIn distributional semantics, a quantitative methodological approach to understanding meaning in observed language, word embeddings or semantic feature space models have been used as a knowledge representation for some time. Such models aim to quantify and categorize semantic similarities between linguistic items based on their distributional properties in large samples of language data.  The underlying idea that \"a word is characterized by the company it keeps\" was proposed in a 1957 article by John Rupert Firth, but also has roots in the contemporaneous work on search systems and in cognitive psychology."]], "0_corpus": "wikipedia", "1_conv_id": "82e5c5b1bf9a468186f7afe7e374731a", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "what openai embedding dimension to use", "1_output": [["what openai embedding dimension to use", "OpenAI\n\nOpenAI is an American artificial intelligence (AI) research organization founded in December 2015 and headquartered in San Francisco. Its mission is to ensure that artificial general intelligence (AGI) benefits all of humanity, with AGI defined as \"highly autonomous systems that outperform humans at most economically valuable work\". As a leading organization in the ongoing AI boom, OpenAI is known for the GPT family of large language models, the DALL-E series of text-to-image models, and a text-to-video model named Sora. Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI.\nThe organization consists of the non-profit OpenAI, Inc. registered in Delaware and its for-profit subsidiary OpenAI Global, LLC. Microsoft owns roughly 49% of OpenAI's equity, having invested US$13 billion. It also provides computing resources to OpenAI through its Microsoft Azure cloud platform.\nIn 2023 and 2024, OpenAI faced multiple lawsuits for alleged copyright infringement against authors and media companies whose work was used to train some of OpenAI's products. In November 2023, OpenAI's board removed Sam Altman as CEO citing a lack of confidence in him, and then reinstated him five days later after negotiations resulting in a reconstructed board. OpenAI's board has since added former US Treasury Secretary Lawrence Summers and former National Security Agency head Paul Nakasone.\nHistory"]], "1_corpus": "wikipedia"}

data/retrieval_individual-031dca12-9a97-4178-b56e-afd2ee86cecb.jsonl

@@ -82,3 +82,5 @@
 {"tstamp": 1735897267.8374, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1735897265.6256, "finish": 1735897267.8374, "ip": "", "conv_id": "1aa53bd671a2435987c08395357601f4", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "output": [["Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "Computing Machinery and Intelligence\n\n\"Computing Machinery and Intelligence\" is a seminal paper written by Alan Turing on the topic of artificial intelligence. The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public.\nTuring's paper considers the question \"Can machines think?\" Turing says that since the words \"think\" and \"machine\" cannot be clearly defined we should \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" To do this, he must first find a simple and unambiguous idea to replace the word \"think\", second he must explain exactly which \"machines\" he is considering, and finally, armed with these tools, he formulates a new question, related to the first, that he believes he can answer in the affirmative."]], "corpus": "wikipedia"}
 {"tstamp": 1735920460.9185, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1735920458.6327, "finish": 1735920460.9185, "ip": "", "conv_id": "bf7078a3961840999153eca15e80a0a7", "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": 1735920460.9185, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1735920458.6327, "finish": 1735920460.9185, "ip": "", "conv_id": "352c37ba58c9425292a12efdf6215b72", "model_name": "GritLM/GritLM-7B", "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": 1735932029.1532, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1735932023.8881, "finish": 1735932029.1532, "ip": "", "conv_id": "98144d9d0aa54f6bba70378e6d5933da", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "what openai embedding dimension to use", "output": [["what openai embedding dimension to use", "Word embedding\n\nIn natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.\nMethods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in terms of the context in which words appear.\nWord and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis.\nDevelopment and history of the approach\nIn distributional semantics, a quantitative methodological approach to understanding meaning in observed language, word embeddings or semantic feature space models have been used as a knowledge representation for some time. Such models aim to quantify and categorize semantic similarities between linguistic items based on their distributional properties in large samples of language data.  The underlying idea that \"a word is characterized by the company it keeps\" was proposed in a 1957 article by John Rupert Firth, but also has roots in the contemporaneous work on search systems and in cognitive psychology."]], "corpus": "wikipedia"}
+{"tstamp": 1735932029.1532, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1735932023.8881, "finish": 1735932029.1532, "ip": "", "conv_id": "82e5c5b1bf9a468186f7afe7e374731a", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "what openai embedding dimension to use", "output": [["what openai embedding dimension to use", "OpenAI\n\nOpenAI is an American artificial intelligence (AI) research organization founded in December 2015 and headquartered in San Francisco. Its mission is to ensure that artificial general intelligence (AGI) benefits all of humanity, with AGI defined as \"highly autonomous systems that outperform humans at most economically valuable work\". As a leading organization in the ongoing AI boom, OpenAI is known for the GPT family of large language models, the DALL-E series of text-to-image models, and a text-to-video model named Sora. Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI.\nThe organization consists of the non-profit OpenAI, Inc. registered in Delaware and its for-profit subsidiary OpenAI Global, LLC. Microsoft owns roughly 49% of OpenAI's equity, having invested US$13 billion. It also provides computing resources to OpenAI through its Microsoft Azure cloud platform.\nIn 2023 and 2024, OpenAI faced multiple lawsuits for alleged copyright infringement against authors and media companies whose work was used to train some of OpenAI's products. In November 2023, OpenAI's board removed Sam Altman as CEO citing a lack of confidence in him, and then reinstated him five days later after negotiations resulting in a reconstructed board. OpenAI's board has since added former US Treasury Secretary Lawrence Summers and former National Security Agency head Paul Nakasone.\nHistory"]], "corpus": "wikipedia"}