Scheduled Commit

data/clustering_battle-35e094d9-c3d4-447e-b2f4-7dd3f5d1d585.jsonl

@@ -1 +1,2 @@
 {"tstamp": 1723244337.8557, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8ee353339b874131883f4e758c4fb6c2", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Apple", "Samsung", "Huawei", "LG", "OnePlus", "Xiaomi", "wisdom tooth", "molar", "incisor", "premolar", "canine", "Opera", "Safari", "Brave", "Edge", "Firefox", "Chrome", "Capricorn", "Leo", "Taurus", "Aries", "Scorpio", "Libra", "Cancer", "Gemini", "canoe", "motorboat", "yacht", "catamaran"], "0_ncluster": 5, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "dca734cd08a946dc991a76cbd4459fd7", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["Apple", "Samsung", "Huawei", "LG", "OnePlus", "Xiaomi", "wisdom tooth", "molar", "incisor", "premolar", "canine", "Opera", "Safari", "Brave", "Edge", "Firefox", "Chrome", "Capricorn", "Leo", "Taurus", "Aries", "Scorpio", "Libra", "Cancer", "Gemini", "canoe", "motorboat", "yacht", "catamaran"], "1_ncluster": 5, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1723409498.4434, "task_type": "clustering", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "98569eae160342459406a000ac1341bd", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design", "beam clear span", "anchor bolt design", "jack stud design"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "ae75439bc445400696766ec73cdf8625", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design", "beam clear span", "anchor bolt design", "jack stud design"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-35e094d9-c3d4-447e-b2f4-7dd3f5d1d585.jsonl

@@ -34,3 +34,9 @@
 {"tstamp": 1723409292.0951, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723409292.0161, "finish": 1723409292.0951, "ip": "", "conv_id": "ae75439bc445400696766ec73cdf8625", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["shear wall design", "window header", "subfloor spacinng"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1723409310.5352, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1723409310.4564, "finish": 1723409310.5352, "ip": "", "conv_id": "98569eae160342459406a000ac1341bd", "model_name": "GritLM/GritLM-7B", "prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1723409310.5352, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723409310.4564, "finish": 1723409310.5352, "ip": "", "conv_id": "ae75439bc445400696766ec73cdf8625", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723409376.2627, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1723409376.1831, "finish": 1723409376.2627, "ip": "", "conv_id": "98569eae160342459406a000ac1341bd", "model_name": "GritLM/GritLM-7B", "prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design", "beam clear span"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723409376.2627, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723409376.1831, "finish": 1723409376.2627, "ip": "", "conv_id": "ae75439bc445400696766ec73cdf8625", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design", "beam clear span"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723409403.2207, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1723409403.138, "finish": 1723409403.2207, "ip": "", "conv_id": "98569eae160342459406a000ac1341bd", "model_name": "GritLM/GritLM-7B", "prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design", "beam clear span", "anchor bolt design"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723409403.2207, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723409403.138, "finish": 1723409403.2207, "ip": "", "conv_id": "ae75439bc445400696766ec73cdf8625", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design", "beam clear span", "anchor bolt design"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723409482.8391, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1723409482.7583, "finish": 1723409482.8391, "ip": "", "conv_id": "98569eae160342459406a000ac1341bd", "model_name": "GritLM/GritLM-7B", "prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design", "beam clear span", "anchor bolt design", "jack stud design"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723409482.8391, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723409482.7583, "finish": 1723409482.8391, "ip": "", "conv_id": "ae75439bc445400696766ec73cdf8625", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["shear wall design", "window header", "subfloor spacinng", "joist design", "beam clear span", "anchor bolt design", "jack stud design"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-35e094d9-c3d4-447e-b2f4-7dd3f5d1d585.jsonl

@@ -185,3 +185,5 @@
 {"tstamp": 1723400973.0637, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1723400970.4715, "finish": 1723400973.0637, "ip": "", "conv_id": "e7c0e184b5b944d7b205617825ffb9c6", "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": 1723409117.7031, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1723409115.362, "finish": 1723409117.7031, "ip": "", "conv_id": "76f6965b78ea4ab295ced0ec506be1da", "model_name": "intfloat/e5-mistral-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": 1723409117.7031, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1723409115.362, "finish": 1723409117.7031, "ip": "", "conv_id": "dbdb49caa4964061884673a7efac3eb9", "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": 1723409595.6599, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723409594.7419, "finish": 1723409595.6599, "ip": "", "conv_id": "a9e47624cc0440a5a49967dd35426979", "model_name": "text-embedding-3-large", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "corpus": "arxiv"}
+{"tstamp": 1723409595.6599, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1723409594.7419, "finish": 1723409595.6599, "ip": "", "conv_id": "48be1d13d2d94023927786ff4a3bd9fd", "model_name": "BM25", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Michael E. Fisher - teacher, mentor, colleague and friend: a (very) personal account\n\nAbstract: The only rational way of educating is to be an example. If one cant help it, a warning example. Albert Einstein. I had the good fortune and privilege of having Michael Fisher as my teacher, supervisor, mentor and friend. During my years as a scientist, teacher and supervisor of about one hundred students and post docs I found myself innumerable times realizing that I am following or at least trying to follow Michaels example. These pages are my attempt to convey recollections of my association with Michael, focusing on how he served as an example for me."]], "corpus": "arxiv"}