Scheduled Commit

data/clustering_battle-b05ca3f8-c521-4bfc-a840-ff14f8eda5db.jsonl

@@ -28,3 +28,6 @@
 {"tstamp": 1734525192.1585, "task_type": "clustering", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "8a8ca1bd44c945ebaec822c90cbe60a4", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": ["green", "light green", "red", "blue", "pink", "rose", "sky", "light blue"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "48b6aa57753543a98fb5cce65cfd37b1", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": ["green", "light green", "red", "blue", "pink", "rose", "sky", "light blue"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1734525294.0102, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "1e5cfb7e6588451f817dc409f31c5c96", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "5a8c153ca0cc43e09e614526c27cf835", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1734525328.5593, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "d83abba7d217460c9e5bf419be4eeafc", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "00da04e0c34f4584bd31237eb34033b4", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1734526108.1241, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "11ea1d1441bb402cb08cd06c17243b2f", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "1d82f4952d10444cbc8d7da5e3bb74ea", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1734526143.9635, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "880b498635c541799e6a4cf77268bf34", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "c3fb9ff1b61740f48efab6055f723060", "1_model_name": "embed-english-v3.0", "1_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1734526173.1823, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "912460edf89349d9890ef3975036bd9d", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "06631b9a288b4693b74417409f0dc817", "1_model_name": "embed-english-v3.0", "1_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-b05ca3f8-c521-4bfc-a840-ff14f8eda5db.jsonl

@@ -158,3 +158,11 @@
 {"tstamp": 1734525306.9404, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1734525306.8486, "finish": 1734525306.9404, "ip": "", "conv_id": "00da04e0c34f4584bd31237eb34033b4", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1734525350.0612, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734525349.963, "finish": 1734525350.0612, "ip": "", "conv_id": "478bb778c8e0402ca38d61cf9e99d967", "model_name": "GritLM/GritLM-7B", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1734525350.0612, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1734525349.963, "finish": 1734525350.0612, "ip": "", "conv_id": "0f33fa7e5d304a0884146038b8cdd321", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734526060.424, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734526060.3196, "finish": 1734526060.424, "ip": "", "conv_id": "478bb778c8e0402ca38d61cf9e99d967", "model_name": "GritLM/GritLM-7B", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue", "green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734526060.424, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1734526060.3196, "finish": 1734526060.424, "ip": "", "conv_id": "0f33fa7e5d304a0884146038b8cdd321", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue", "green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734526072.8822, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1734526072.802, "finish": 1734526072.8822, "ip": "", "conv_id": "11ea1d1441bb402cb08cd06c17243b2f", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734526072.8822, "task_type": "clustering", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1734526072.802, "finish": 1734526072.8822, "ip": "", "conv_id": "1d82f4952d10444cbc8d7da5e3bb74ea", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734526124.0347, "task_type": "clustering", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1734526123.815, "finish": 1734526124.0347, "ip": "", "conv_id": "880b498635c541799e6a4cf77268bf34", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734526124.0347, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1734526123.815, "finish": 1734526124.0347, "ip": "", "conv_id": "c3fb9ff1b61740f48efab6055f723060", "model_name": "embed-english-v3.0", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734526159.8115, "task_type": "clustering", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1734526159.6536, "finish": 1734526159.8115, "ip": "", "conv_id": "912460edf89349d9890ef3975036bd9d", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734526159.8115, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1734526159.6536, "finish": 1734526159.8115, "ip": "", "conv_id": "06631b9a288b4693b74417409f0dc817", "model_name": "embed-english-v3.0", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-b05ca3f8-c521-4bfc-a840-ff14f8eda5db.jsonl

@@ -416,3 +416,5 @@
 {"tstamp": 1734517349.5989, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734517349.3473, "finish": 1734517349.5989, "ip": "", "conv_id": "ecbe9f8a8c4e4fb78f4290f050be297a", "model_name": "GritLM/GritLM-7B", "prompt": "who invented the toaster?", "output": [["who invented the toaster?", "Toaster\n\nThe first stand-alone electric toaster, the Eclipse, was made in 1893 by Crompton & Company of Chelmsford, Essex. Its bare wires toasted bread on one side at a time.\nThe problem of the heating element was solved in 1905 by a young engineer named Albert Marsh, who designed an alloy of nickel and chromium, which came to be known as Nichrome.\nThe first US patent application for an electric toaster was filed by George Schneider of the American Electrical Heater Company of Detroit in collaboration with Marsh. One of the first applications that the Hoskins company considered for its Chromel wire was for use in toasters, but the company eventually abandoned such efforts, to focus on making just the wire itself.\nThe first commercially successful electric toaster was introduced by General Electric in 1909 for the GE model D-12.\nDual-side toasting and automated pop-up technologies\nIn 1913, Lloyd Groff Copeman and his wife Hazel Berger Copeman applied for various toaster patents, and in that same year, the Copeman Electric Stove Company introduced a toaster with an automatic bread turner. Before this, electric toasters cooked bread on one side, meaning the bread needed to be flipped by hand to cook both sides. Copeman's toaster turned the bread around without having to touch it."]], "corpus": "wikipedia"}
 {"tstamp": 1734517404.9325, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1734517404.6883, "finish": 1734517404.9325, "ip": "", "conv_id": "fa49281849bf45e0b4b0e0b224543f56", "model_name": "text-embedding-004", "prompt": "a fistfull of dollars", "output": [["a fistfull of dollars", "A Fistful of Dollars\n\nThe film has been identified as an unofficial remake of the Akira Kurosawa film, Yojimbo (1961), which resulted in a successful lawsuit by Toho, Yojimbos production company. Kurosawa wrote to Leone directly, saying, \"Signor Leone, I have just had the chance to see your film. It is a very fine film, but it is my film. Since Japan is a signatory of the Berne Convention on the international copyright, you must pay me.\" He and Toho received 15% of the film's revenue, and it is believed that Kurosawa earned more money from the financial settlement than he had made on his own film, Yojimbo.\nFew spaghetti Westerns had been released in the United States at the time, so many of the European cast and crew adopted American-sounding stage names. These included Leone (\"Bob Robertson\"), Gian Maria Volonté (\"Johnny Wels\") and composer Ennio Morricone (\"Dan Savio\"). A Fistful of Dollars was shot in Spain, mostly near Hoyo de Manzanares close to Madrid, but also (like its two sequels) in the Tabernas Desert and in the Cabo de Gata-Níjar Natural Park, both in the province of Almería.\nPlot\nAn unnamed stranger arrives at the little town of San Miguel, on the Mexico–United States border. Silvanito, the town's innkeeper, tells the Stranger about a feud between two smuggler families vying to gain control of the town: the Rojo brothers (Don Miguel, Esteban & Ramón), and the family comprising the town sheriff, John Baxter, his matriarchal wife, Consuelo, and their son, Antonio. To make money, the Stranger decides to play these families against each other. He demonstrates his deadliness to both sides by shooting four men with ease, including the three who insulted him as he entered the town."]], "corpus": "wikipedia"}
 {"tstamp": 1734517404.9325, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734517404.6883, "finish": 1734517404.9325, "ip": "", "conv_id": "811416338f624cdea5ea81d287003f2d", "model_name": "GritLM/GritLM-7B", "prompt": "a fistfull of dollars", "output": [["a fistfull of dollars", "A Fistful of Dollars\n\nA Fistful of Dollars () is a 1964 spaghetti Western film directed by Sergio Leone and starring Clint Eastwood in his first leading role, alongside Gian Maria Volonté, Marianne Koch, Wolfgang Lukschy, Sieghardt Rupp, José Calvo, Antonio Prieto and Joseph Egger. The film, an international co-production between Italy, West Germany and Spain, was filmed on a low budget (reported to be $200,000), and Eastwood was paid $15,000 for his role.\nReleased in Italy in 1964 and in the United States in 1967, the film initiated the popularity of the spaghetti Western genre. It was followed by For a Few Dollars More and The Good, the Bad and the Ugly, both also starring Eastwood. Collectively, the films are known as the Dollars Trilogy, or the Man with No Name Trilogy, after the United Artists publicity campaign referred to Eastwood's character in all three films as the \"Man with No Name\". All three films were released in sequence in the United States in 1967, catapulting Eastwood into stardom."]], "corpus": "wikipedia"}
+{"tstamp": 1734526175.5029, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1734526175.1915, "finish": 1734526175.5029, "ip": "", "conv_id": "565087bdda584a488fb349f8c4204247", "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": 1734526175.5029, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1734526175.1915, "finish": 1734526175.5029, "ip": "", "conv_id": "58dd6c1c386d47f2b103f882b616840a", "model_name": "Salesforce/SFR-Embedding-2_R", "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"}