Scheduled Commit

data/clustering_battle-af410ee3-7691-4d91-abd0-61898a8363dc.jsonl

@@ -2,3 +2,4 @@
 {"tstamp": 1739758613.8003, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "169f4d79d72a4c5295f8bf50be86426d", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": ["cirrus", "altostratus", "cumulus", "nimbus", "stratus", "democracy", "republic"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "1836e26285f14c86af3d94d089367894", "1_model_name": "text-embedding-3-large", "1_prompt": ["cirrus", "altostratus", "cumulus", "nimbus", "stratus", "democracy", "republic"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1739758632.5518, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "9b314e4f2b5d442d85a55ede0329989f", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": ["Volkswagen", "Ford", "Tesla", "Toyota", "Roman", "Incan", "Egyptian", "Chinese", "Mayan", "Greek", "Mesopotamian"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "b4d2d3abb3b347b686ec1ab36a2d832d", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["Volkswagen", "Ford", "Tesla", "Toyota", "Roman", "Incan", "Egyptian", "Chinese", "Mayan", "Greek", "Mesopotamian"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1739758657.7844, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "4870bba430124a6d9f2baa8e3a1c1a95", "0_model_name": "embed-english-v3.0", "0_prompt": ["jiu-jitsu", "aikido", "taekwondo", "judo", "kayak", "motorboat", "yacht", "cruise ship", "canoe"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "b8dc17f9c5b24a1fa5fe30dcfdbf90a4", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": ["jiu-jitsu", "aikido", "taekwondo", "judo", "kayak", "motorboat", "yacht", "cruise ship", "canoe"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1739877313.7783, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "e14b09cf2f9044c19e0ddf80afdd3948", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "2169d7aa00cd4a6dab00e0f527c6b324", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-af410ee3-7691-4d91-abd0-61898a8363dc.jsonl

@@ -10,3 +10,5 @@
 {"tstamp": 1739758666.9427, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1739758666.6638, "finish": 1739758666.9427, "ip": "", "conv_id": "796074680ba54227b44c6ea8da10bf5d", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["pu-erh", "white", "green", "black", "chamomile", "TikTok", "Twitter", "LinkedIn", "Facebook", "Instagram", "claustrophobia", "nyctophobia", "acrophobia", "arachnophobia", "agoraphobia", "ophidiophobia", "mackerel", "halibut", "trout", "bass", "tuna", "salmon", "cod"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1739810035.2147, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1739810035.1791, "finish": 1739810035.2147, "ip": "", "conv_id": "d3090a81bdf2416f99710a9deb426701", "model_name": "text-embedding-004", "prompt": ["Dog"], "ncluster": 1, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1739810056.7094, "task_type": "clustering", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1739810056.6861, "finish": 1739810056.7094, "ip": "", "conv_id": "4970886ebbed49feb909de52923a4c15", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": ["Dog"], "ncluster": 1, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1739877252.1957, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1739877252.1034, "finish": 1739877252.1957, "ip": "", "conv_id": "e14b09cf2f9044c19e0ddf80afdd3948", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1739877252.1957, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1739877252.1034, "finish": 1739877252.1957, "ip": "", "conv_id": "2169d7aa00cd4a6dab00e0f527c6b324", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-af410ee3-7691-4d91-abd0-61898a8363dc.jsonl

@@ -112,3 +112,8 @@
 {"tstamp": 1739870034.6442, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1739870032.0031, "finish": 1739870034.6442, "ip": "", "conv_id": "6571b71ce56a4baa9c6794a2eab762c7", "model_name": "text-embedding-004", "prompt": "I am happy!", "output": [["I am happy!", "Joy\n\nJoy is the state of being that allows one to experience feelings of intense, long-lasting happiness and satisfaction of life. It is closely related to, and often evoked by, well-being, success, or good fortune. Happiness, pleasure, and gratitude are closely related to joy but are not identical to it."]], "corpus": "wikipedia"}
 {"tstamp": 1739870084.6397, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1739870082.4996, "finish": 1739870084.6397, "ip": "", "conv_id": "f90c40794c0240e289f9747acc6c980a", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "where is cuba located on a world map", "output": [["where is cuba located on a world map", "Geography of Cuba\n\nCuba lies west of the North Atlantic Ocean, east of the Gulf of Mexico, south of the Straits of Florida, northwest of the Windward Passage, and northeast of the Yucatán Channel. The main island (Cuba), at , makes up most of the land area and is the 17th-largest island in the world by land area.\nThe island is  long and  across its widest points and  across its narrowest points. The largest island outside the main island is the Isla de la Juventud (Isle of Youth) in the southwest, with an area of .\nThe main island consists mostly of flat to rolling plains. At the southeastern end is the Sierra Maestra, a range of steep mountains whose highest point is the Pico Turquino at .\nHavana is the largest city and capital; other major cities include Santiago de Cuba and Camagüey. Better-known smaller towns include Baracoa, which was the first Spanish settlement on Cuba, Trinidad, a UNESCO world heritage site, and Bayamo.\nPhysical geography\nCuba is located  west of Haiti across the Windward Passage,  south of The Bahamas (Cay Lobos),  south of the United States (Key West, Florida),  east of Mexico, and  north of Jamaica. It was made in three stages."]], "corpus": "wikipedia"}
 {"tstamp": 1739870084.6397, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1739870082.4996, "finish": 1739870084.6397, "ip": "", "conv_id": "ee02efe3077c435ea8e0f83a69010c75", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "where is cuba located on a world map", "output": [["where is cuba located on a world map", "Geography of Cuba\n\nCuba lies west of the North Atlantic Ocean, east of the Gulf of Mexico, south of the Straits of Florida, northwest of the Windward Passage, and northeast of the Yucatán Channel. The main island (Cuba), at , makes up most of the land area and is the 17th-largest island in the world by land area.\nThe island is  long and  across its widest points and  across its narrowest points. The largest island outside the main island is the Isla de la Juventud (Isle of Youth) in the southwest, with an area of .\nThe main island consists mostly of flat to rolling plains. At the southeastern end is the Sierra Maestra, a range of steep mountains whose highest point is the Pico Turquino at .\nHavana is the largest city and capital; other major cities include Santiago de Cuba and Camagüey. Better-known smaller towns include Baracoa, which was the first Spanish settlement on Cuba, Trinidad, a UNESCO world heritage site, and Bayamo.\nPhysical geography\nCuba is located  west of Haiti across the Windward Passage,  south of The Bahamas (Cay Lobos),  south of the United States (Key West, Florida),  east of Mexico, and  north of Jamaica. It was made in three stages."]], "corpus": "wikipedia"}
+{"tstamp": 1739877072.9595, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1739877070.7219, "finish": 1739877072.9595, "ip": "", "conv_id": "9e9e311a9ab74626980836223d65d6c9", "model_name": "GritLM/GritLM-7B", "prompt": "Research on improving semantic segmentation by adding a semantic attention operation in the encoder of transformer models.", "output": [["Research on improving semantic segmentation by adding a semantic attention operation in the encoder of transformer models.", "Title: SeMask: Semantically Masked Transformers for Semantic Segmentation\n\nAbstract: Finetuning a pretrained backbone in the encoder part of an image transformer network has been the traditional approach for the semantic segmentation task. However, such an approach leaves out the semantic context that an image provides during the encoding stage. This paper argues that incorporating semantic information of the image into pretrained hierarchical transformer-based backbones while finetuning improves the performance considerably. To achieve this, we propose SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic attention operation. In addition, we use a lightweight semantic decoder during training to provide supervision to the intermediate semantic prior maps at every stage. Our experiments demonstrate that incorporating semantic priors enhances the performance of the established hierarchical encoders with a slight increase in the number of FLOPs. We provide empirical proof by integrating SeMask into Swin Transformer and Mix Transformer backbones as our encoder paired with different decoders. Our framework achieves a new state-of-the-art of 58.25% mIoU on the ADE20K dataset and improvements of over 3% in the mIoU metric on the Cityscapes dataset. The code and checkpoints are publicly available at https://github.com/Picsart-AI-Research/SeMask-Segmentation ."]], "corpus": "arxiv"}
+{"tstamp": 1739877072.9595, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1739877070.7219, "finish": 1739877072.9595, "ip": "", "conv_id": "5ad75356ba81437ebf6e555e53fe5488", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Research on improving semantic segmentation by adding a semantic attention operation in the encoder of transformer models.", "output": [["Research on improving semantic segmentation by adding a semantic attention operation in the encoder of transformer models.", "Title: Transformer-Aided Semantic Communications\n\nAbstract: The transformer structure employed in large language models (LLMs), as a specialized category of deep neural networks (DNNs) featuring attention mechanisms, stands out for their ability to identify and highlight the most relevant aspects of input data. Such a capability is particularly beneficial in addressing a variety of communication challenges, notably in the realm of semantic communication where proper encoding of the relevant data is critical especially in systems with limited bandwidth. In this work, we employ vision transformers specifically for the purpose of compression and compact representation of the input image, with the goal of preserving semantic information throughout the transmission process. Through the use of the attention mechanism inherent in transformers, we create an attention mask. This mask effectively prioritizes critical segments of images for transmission, ensuring that the reconstruction phase focuses on key objects highlighted by the mask. Our methodology significantly improves the quality of semantic communication and optimizes bandwidth usage by encoding different parts of the data in accordance with their semantic information content, thus enhancing overall efficiency. We evaluate the effectiveness of our proposed framework using the TinyImageNet dataset, focusing on both reconstruction quality and accuracy. Our evaluation results demonstrate that our framework successfully preserves semantic information, even when only a fraction of the encoded data is transmitted, according to the intended compression rates."]], "corpus": "arxiv"}
+{"tstamp": 1739877157.8753, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1739877155.6255, "finish": 1739877157.8753, "ip": "", "conv_id": "df93ccc1a3324e5091d8e68411769728", "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": 1739877172.7841, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1739877166.754, "finish": 1739877172.7841, "ip": "", "conv_id": "342b4ea427ea4108a7d24c6175ecab13", "model_name": "BM25", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \"AI\" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \"AI\".\nAbout 800,000 \"AI\"-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \"I propose to consider the question 'can machines think'?\" He advised changing the question from whether a machine \"thinks\", to \"whether or not it is possible for machinery to show intelligent behaviour\". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \"actually\" thinking or literally has a \"mind\". Turing notes that we can not determine these things about other people but \"it is usual to have a polite convention that everyone thinks.\""]], "corpus": "wikipedia"}
+{"tstamp": 1739877172.7841, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1739877166.754, "finish": 1739877172.7841, "ip": "", "conv_id": "1157e9aeac254ce1b2fab87d69d0b8c6", "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"}