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data/clustering_battle-2b220830-844b-4128-a00a-03ab29c3b3ac.jsonl

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+{"tstamp": 1730903999.328, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "03f0f264892c41f280ec2b3492c22ef7", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": ["baseball cap", "cowboy hat", "beanie", "convex", "parabolic", "basketball", "volleyball"], "0_ncluster": 3, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "0901c93377d04d3eae507a7f87cddf94", "1_model_name": "text-embedding-004", "1_prompt": ["baseball cap", "cowboy hat", "beanie", "convex", "parabolic", "basketball", "volleyball"], "1_ncluster": 3, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-2b220830-844b-4128-a00a-03ab29c3b3ac.jsonl

@@ -34,3 +34,7 @@
 {"tstamp": 1730814561.6123, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1730814561.4459, "finish": 1730814561.6123, "ip": "", "conv_id": "47190d2d607b4d8894e464ccaf412278", "model_name": "embed-english-v3.0", "prompt": ["Scientists achieved quantum entanglement between particles separated by a record distance. ", "\nThe new quantum computer solved a complex problem in mere seconds. ", "\nResearchers observed a novel quantum phenomenon in superconducting materials. ", "\nThe chef combined molecular gastronomy techniques with traditional French cuisine. ", "\nA rare variety of truffles was discovered in an unexpected region of Italy. ", "\nThe annual food festival showcased fusion dishes from around the world. ", "\nFarmers implemented vertical farming methods to maximize urban food production. ", "\nA new drought-resistant strain of wheat showed promising results in field trials. ", "\nThe organic farm used beneficial insects to control pests without chemicals. ", "\nResearchers developed a more efficient irrigation system for arid climates.\n"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1730814566.7385, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1730814566.5241, "finish": 1730814566.7385, "ip": "", "conv_id": "c0699a060f3147d7976f17424cbb75bc", "model_name": "GritLM/GritLM-7B", "prompt": ["Scientists achieved quantum entanglement between particles separated by a record distance. ", "\nThe new quantum computer solved a complex problem in mere seconds. ", "\nResearchers observed a novel quantum phenomenon in superconducting materials. ", "\nThe chef combined molecular gastronomy techniques with traditional French cuisine. ", "\nA rare variety of truffles was discovered in an unexpected region of Italy. ", "\nThe annual food festival showcased fusion dishes from around the world. ", "\nFarmers implemented vertical farming methods to maximize urban food production. ", "\nA new drought-resistant strain of wheat showed promising results in field trials. ", "\nThe organic farm used beneficial insects to control pests without chemicals. ", "\nResearchers developed a more efficient irrigation system for arid climates.\n"], "ncluster": 3, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1730814566.7385, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1730814566.5241, "finish": 1730814566.7385, "ip": "", "conv_id": "47190d2d607b4d8894e464ccaf412278", "model_name": "embed-english-v3.0", "prompt": ["Scientists achieved quantum entanglement between particles separated by a record distance. ", "\nThe new quantum computer solved a complex problem in mere seconds. ", "\nResearchers observed a novel quantum phenomenon in superconducting materials. ", "\nThe chef combined molecular gastronomy techniques with traditional French cuisine. ", "\nA rare variety of truffles was discovered in an unexpected region of Italy. ", "\nThe annual food festival showcased fusion dishes from around the world. ", "\nFarmers implemented vertical farming methods to maximize urban food production. ", "\nA new drought-resistant strain of wheat showed promising results in field trials. ", "\nThe organic farm used beneficial insects to control pests without chemicals. ", "\nResearchers developed a more efficient irrigation system for arid climates.\n"], "ncluster": 3, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1730903963.717, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1730903963.4588, "finish": 1730903963.717, "ip": "", "conv_id": "03f0f264892c41f280ec2b3492c22ef7", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["baseball cap", "cowboy hat", "beanie", "convex", "parabolic", "basketball", "volleyball"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1730903963.717, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1730903963.4588, "finish": 1730903963.717, "ip": "", "conv_id": "0901c93377d04d3eae507a7f87cddf94", "model_name": "text-embedding-004", "prompt": ["baseball cap", "cowboy hat", "beanie", "convex", "parabolic", "basketball", "volleyball"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1730903990.4119, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1730903990.1561, "finish": 1730903990.4119, "ip": "", "conv_id": "03f0f264892c41f280ec2b3492c22ef7", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["baseball cap", "cowboy hat", "beanie", "convex", "parabolic", "basketball", "volleyball"], "ncluster": 3, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1730903990.4119, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1730903990.1561, "finish": 1730903990.4119, "ip": "", "conv_id": "0901c93377d04d3eae507a7f87cddf94", "model_name": "text-embedding-004", "prompt": ["baseball cap", "cowboy hat", "beanie", "convex", "parabolic", "basketball", "volleyball"], "ncluster": 3, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-2b220830-844b-4128-a00a-03ab29c3b3ac.jsonl

@@ -135,3 +135,5 @@
 {"tstamp": 1730880509.1519, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1730880507.7758, "finish": 1730880509.1519, "ip": "", "conv_id": "2ca47dbde2e740a6895a531f95c869b4", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "hello, this's a test!", "output": [["hello, this's a test!", "Hello World\n\nHello World may refer to:\n\"Hello, World!\" program, a computer program that outputs or displays the message \"Hello, World!\"\nMusic\n\"Hello World!\" (composition), song by the Iamus computer\n\"Hello World\" (Tremeloes song), 1969\n\"Hello World\" (Lady Antebellum song), 2010\n\"Hello World\", a song by Nik Kershaw from the album To Be Frank\n\"Hello, World!\", a 2015 song by Bump of Chicken\n\"Hello World\", a 2015 song by Ginny Blackmore\n\"Hello World\", a song by Belle Perez\n\"Hello, World!\", a 2018 song by Vocaloid producer YZYX featuring Hatsune Miku\n\"Hello World\", a 2022 song by Alan Walker featuring Torine\n\"Hello World\", a 2024 song by LiSA\nAlbums\nHello World, 2011 album by Back-On\nHello World (Information Society album), 2014\nHello World (Scandal album), 2014\nHello, World! (EP), 2022 EP by Xdinary Heroes\nHello World: The Motown Solo Collection, compilation album by Michael Jackson\nOther uses\nHelloworld Travel, an Australian-based travel agency\nHelloworld (TV program), an Australian travel and lifestyle television program\nHello World (film), a 2019 Japanese animated film\nHello World (Web Series), a 2022 Telugu TV Series\nHello World: How to Be Human in the Age of the Machine, a book by Hannah Fry"]], "corpus": "wikipedia"}
 {"tstamp": 1730889247.3692, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1730889245.0763, "finish": 1730889247.3692, "ip": "", "conv_id": "10aeec274f524e51939f11053eaf852f", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Glucono delta-lactone", "output": [["Glucono delta-lactone", "Glucono delta-lactone\n\nGlucono-delta-lactone (GDL), also known as gluconolactone, is an organic compound with the formula .  A colorless solid, it is an oxidized derivative of glucose.\nIt is typically produced by the aerobic oxidation of glucose in the presence of the enzyme glucose oxidase.  The conversion cogenerates hydrogen peroxide, which is often the key product of the enzyme:"]], "corpus": "wikipedia"}
 {"tstamp": 1730889247.3692, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1730889245.0763, "finish": 1730889247.3692, "ip": "", "conv_id": "c20d865b968e483f80e879c672599a75", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Glucono delta-lactone", "output": [["Glucono delta-lactone", "Glucono delta-lactone\n\nGlucono-delta-lactone (GDL), also known as gluconolactone, is an organic compound with the formula .  A colorless solid, it is an oxidized derivative of glucose.\nIt is typically produced by the aerobic oxidation of glucose in the presence of the enzyme glucose oxidase.  The conversion cogenerates hydrogen peroxide, which is often the key product of the enzyme:"]], "corpus": "wikipedia"}
+{"tstamp": 1730903944.497, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1730903942.2504, "finish": 1730903944.497, "ip": "", "conv_id": "5e25e6eb69e844b8b8c3a98d90291777", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Study demonstrating Codex's ability to solve, explain, and generate math problems at human level", "output": [["Study demonstrating Codex's ability to solve, explain, and generate math problems at human level", "Title: A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level\n\nAbstract: We demonstrate that a neural network pre-trained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates new questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's Codex transformer and execute them to solve course problems at 81% automatic accuracy. We curate a new dataset of questions from MIT's largest mathematics courses (Single Variable and Multivariable Calculus, Differential Equations, Introduction to Probability and Statistics, Linear Algebra, and Mathematics for Computer Science) and Columbia University's Computational Linear Algebra. We solve questions from a MATH dataset (on Prealgebra, Algebra, Counting and Probability, Intermediate Algebra, Number Theory, and Precalculus), the latest benchmark of advanced mathematics problems designed to assess mathematical reasoning. We randomly sample questions and generate solutions with multiple modalities, including numbers, equations, and plots. The latest GPT-3 language model pre-trained on text automatically solves only 18.8% of these university questions using zero-shot learning and 30.8% using few-shot learning and the most recent chain of thought prompting. In contrast, program synthesis with few-shot learning using Codex fine-tuned on code generates programs that automatically solve 81% of these questions. Our approach improves the previous state-of-the-art automatic solution accuracy on the benchmark topics from 8.8% to 81.1%. We perform a survey to evaluate the quality and difficulty of generated questions. This work is the first to automatically solve university-level mathematics course questions at a human level and the first work to explain and generate university-level mathematics course questions at scale, a milestone for higher education."]], "corpus": "arxiv"}
+{"tstamp": 1730903944.497, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1730903942.2504, "finish": 1730903944.497, "ip": "", "conv_id": "5e0cd83a493e4629a4e1d9e0f80982ce", "model_name": "text-embedding-004", "prompt": "Study demonstrating Codex's ability to solve, explain, and generate math problems at human level", "output": [["Study demonstrating Codex's ability to solve, explain, and generate math problems at human level", "Title: Evaluating Large Language Models Trained on Code\n\nAbstract: We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."]], "corpus": "arxiv"}