Scheduled Commit

data/clustering_battle-0325b067-7cac-407c-9ffa-76d99f10c9f7.jsonl

@@ -1,4 +1,2 @@
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data/clustering_battle-115786ef-c6bb-4ff6-b66a-85511fd72c53.jsonl

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data/clustering_battle-40ef8ce0-457c-41e8-8b3f-024c4ed67062.jsonl

@@ -1,6 +1,3 @@
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 {"tstamp": 1722272781.662, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "2fa935503d7444289937354e37d78a32", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "c047cd4aa1044007b410fc06159c0ff2", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_battle-5ff75294-3b96-4e61-b91f-99fe468ad573.jsonl

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@@ -14,13 +13,11 @@
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data/clustering_battle-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -4,7 +4,6 @@
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data/clustering_battle-9c2f2ba1-3fd3-43ca-a968-8d9a53b79a4c.jsonl

@@ -1,3 +1 @@
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data/clustering_individual-0325b067-7cac-407c-9ffa-76d99f10c9f7.jsonl

@@ -2,11 +2,9 @@
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 {"tstamp": 1722263931.8531, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722263931.6012, "finish": 1722263931.8531, "ip": "", "conv_id": "f41d5abfc81e464f99649a2115bbc6f3", "model_name": "text-embedding-004", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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 {"tstamp": 1722264046.8499, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722264046.7437, "finish": 1722264046.8499, "ip": "", "conv_id": "2c8225553fcc4e4fb2cebb7679a345c8", "model_name": "GritLM/GritLM-7B", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-115786ef-c6bb-4ff6-b66a-85511fd72c53.jsonl

@@ -3,7 +3,6 @@
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data/clustering_individual-40ef8ce0-457c-41e8-8b3f-024c4ed67062.jsonl

@@ -1,13 +1,10 @@
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 {"tstamp": 1722272554.9151, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722272547.7734, "finish": 1722272554.9151, "ip": "", "conv_id": "393889c596aa4ae09e16e34862121291", "model_name": "embed-english-v3.0", "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"}
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 {"tstamp": 1722272664.3561, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722272664.215, "finish": 1722272664.3561, "ip": "", "conv_id": "6f918894f8be4a1fbbc39605b7d82eeb", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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data/clustering_individual-5ff75294-3b96-4e61-b91f-99fe468ad573.jsonl

@@ -40,7 +40,6 @@
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@@ -66,7 +65,6 @@
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 {"tstamp": 1722277558.3334, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722277558.2458, "finish": 1722277558.3334, "ip": "", "conv_id": "2b2185253ff94ae49c4e852521ffe4b3", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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@@ -84,7 +82,6 @@
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 {"tstamp": 1722277760.2923, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722277760.2172, "finish": 1722277760.2923, "ip": "", "conv_id": "a2c497eeb6d64886a21888c13f656ce6", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["Apple", "Hammer", "Dog", "Guitar", "Banana", "Screwdriver", "Cat", "Piano", "Orange", "Wrench", "Rabbit", "Violin"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
@@ -98,7 +95,6 @@
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 {"tstamp": 1722277919.4988, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722277918.812, "finish": 1722277919.4988, "ip": "", "conv_id": "a6bd45de40d741ef88a4c79583520af4", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722277919.4988, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722277918.812, "finish": 1722277919.4988, "ip": "", "conv_id": "3cab612d49d044a2b684781558bf147d", "model_name": "text-embedding-3-large", "prompt": ["Mars", "Soccer", "Copper", "Democracy", "Jupiter", "Basketball", "Silver", "Monarchy", "Saturn", "Tennis", "Gold", "Oligarchy"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-79029e82-3905-4a19-8fd7-0e6319f51acd.jsonl

@@ -3,7 +3,6 @@
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 {"tstamp": 1722282323.8945, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722282323.7395, "finish": 1722282323.8945, "ip": "", "conv_id": "8fcbb775fa534d6f9f98bc235ca6dfc9", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "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": 1722282329.9572, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722282329.8585, "finish": 1722282329.9572, "ip": "", "conv_id": "f9f7a20b7b6e482bb5a6d46e0ab8d793", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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 {"tstamp": 1722282363.4669, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722282358.0763, "finish": 1722282363.4669, "ip": "", "conv_id": "10957173cee142dbba2922f1af8b5a3b", "model_name": "embed-english-v3.0", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722282376.6844, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722282376.5111, "finish": 1722282376.6844, "ip": "", "conv_id": "d87bc59e4487439d8de7408701843f84", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -1,8 +1,6 @@
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 {"tstamp": 1722361015.4569, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722361004.935, "finish": 1722361015.4569, "ip": "", "conv_id": "77e0a3977a7d4677a040920e83e9eba3", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["yacht", "kayak", "nitrogen", "sodium", "hydrogen", "oxygen", "carbon", "iron", "calcium", "Renaissance", "Cubism", "Impressionism", "Baroque", "Surrealism", "auburn", "brunette", "gray", "redhead", "black", "plasma", "solid", "liquid"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722361050.8948, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722361050.6801, "finish": 1722361050.8948, "ip": "", "conv_id": "780568d2a5a44373b3165001892bf905", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["historical fiction", "fantasy", "science fiction", "horror", "thriller", "romance", "mystery", "linen", "silk", "wool", "leather", "denim", "incisor", "premolar", "molar", "canine", "wisdom tooth", "tornado", "hailstorm", "blizzard", "fog", "drought"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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 {"tstamp": 1722361056.8438, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722361056.7634, "finish": 1722361056.8438, "ip": "", "conv_id": "d8ca698edd9246e4bd9219064067b85c", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["penguin", "tiger", "dolphin", "giraffe", "lion", "koala", "airplane", "boat", "bicycle", "car", "motorcycle", "train", "Cancer", "Leo", "concave", "convex", "parabolic", "mackerel", "halibut", "cod", "salmon", "bass", "trout", "tuna"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722363261.7965, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722363261.7141, "finish": 1722363261.7965, "ip": "", "conv_id": "b76eafd60a354f2f9f8b9bfe3fa6de9e", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722363261.7965, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722363261.7141, "finish": 1722363261.7965, "ip": "", "conv_id": "fb2cf7f9ac6f41eba82993886cfe0176", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
@@ -36,23 +34,18 @@
 {"tstamp": 1722367016.4134, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722367016.1449, "finish": 1722367016.4134, "ip": "", "conv_id": "1d7ff0ec14534a8a8e417bc5e41188f7", "model_name": "GritLM/GritLM-7B", "prompt": ["A", "O", "caldera", "composite", "shield", "elephant", "giraffe", "penguin", "tiger", "lion", "dolphin", "square", "rectangle", "circle", "hexagon", "triangle", "octagon", "Ursa Major", "Cygnus", "Cassiopeia", "Leo", "Scorpius", "Taurus", "Orion"], "ncluster": 5, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722367072.765, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722367071.9605, "finish": 1722367072.765, "ip": "", "conv_id": "49918e2cfa004e65b3d3d613733b7f66", "model_name": "text-embedding-3-large", "prompt": ["mathematics", "literature", "Norse", "Hindu", "contemporary", "salsa", "ballet", "hip-hop", "ballroom", "jazz", "conscientiousness", "openness", "neuroticism", "extroversion", "stomach", "lungs", "pancreas", "heart", "brain"], "ncluster": 5, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722367072.765, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722367071.9605, "finish": 1722367072.765, "ip": "", "conv_id": "bfefbf9a7a1446ffbf84e23200f20d1f", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["mathematics", "literature", "Norse", "Hindu", "contemporary", "salsa", "ballet", "hip-hop", "ballroom", "jazz", "conscientiousness", "openness", "neuroticism", "extroversion", "stomach", "lungs", "pancreas", "heart", "brain"], "ncluster": 5, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722369158.5386, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722369158.4888, "finish": 1722369158.5386, "ip": "", "conv_id": "a1c84e4acb3d4ef8a7c5eebe9e5d4e4e", "model_name": "text-embedding-004", "prompt": ["Randy Kehler (July 16, 1944 – July 21, 2024) was an American pacifist, tax resister, and social justice advocate. Kehler objected to America's involvement in the Vietnam War and refused to cooperate with the draft. He is also known for his decision, along with his wife Betsy Corner, to stop paying federal income taxes in protest of war and military spending, a decision that led to the Internal Revenue Service (IRS) seizing their house in 1989.\n\nKehler was involved in several anti-war organizations in the 1960s and 1970s, and in the early 1980s was a leader in the movement against nuclear weapons.[1]\n\nEarly life and education\nKehler was born on July 16, 1944, in Bronxville, New York, and was raised in Scarsdale.[1] He attended Phillips Exeter Academy and graduated from Harvard University in 1967 with a degree in government.[1] While at Harvard, Kehler became involved with the Harlem chapter of Congress of Racial Equality (CORE).[1] Kehler has credited Martin Luther King Jr.'s \"I Have a Dream\" during the March on Washington for Jobs and Freedom in 1963 with shaping his interest in radical politics.[1]\n\nOpposition to the Vietnam War\nIn 1969, during the Vietnam War, Kehler returned his draft card to the Selective Service System. He refused to seek exemption as a conscientious objector, because he felt that doing so would be a form of cooperation with the US government's actions in Vietnam. After being called for induction and refusing to submit, he was charged with a federal crime. Found guilty at trial, Kehler served twenty-two months of a two-year sentence.[1]\n\nA 2020 documentary film, The Boys Who Said No!, features footage of and an interview with Kehler as one of several Vietnam-era draft resisters discussing that form of anti-war activism.[2]\n\nDaniel Ellsberg's exposure to Kehler in August 1969 (as Kehler was preparing to submit to his sentence) at the 13th Triennial Meeting of the War Resisters International, held at Haverford College, was a pivotal event in Ellsberg's decision to copy and release the Pentagon Papers.[3]\n\nAnti-nuclear activism\nKehler became active in anti-nuclear proliferation and nuclear disarmament movements while leading a grassroots campaign in western Massachusetts to support the concept of a nuclear freeze. His efforts led to his meeting fellow activist Randy Forsberg, who was leading a similar effort at a national level.[4] From 1981 through 1984, Kehler served as Executive Director of the National Nuclear Weapons Freeze Campaign.[5]\n\nKehler advocated against the use of nuclear power and led campaigns for the closure of nuclear power plants, including Vermont Yankee in Vernon, Vermont.[6][7]\n\nResistance of federal income tax\nFrom 1977 onward, Kehler and his wife Betsy Corner refused to pay their federal income taxes in protest of war and military expenditures; they continued to pay their state and local taxes, and donated the money they owed in federal income taxes to charity.[8] This led to the seizure of their house in Colrain, Massachusetts by the IRS in 1989. The home was subsequently purchased by the federal government. Kehler and Corner, along with supporters from the local community, struggled for years with the government and with another couple who attempted to purchase and move in to the home. The events were documented in the 1997 documentary film An Act of Conscience.[9][10][11]\n\nKehler died at his home in Shelburne Falls, Massachusetts, on July 21, 2024, at the age of 80.[5]\n\n"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722369158.5386, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722369158.4888, "finish": 1722369158.5386, "ip": "", "conv_id": "655d241a42174f3bad02c05df2f3e727", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Randy Kehler (July 16, 1944 – July 21, 2024) was an American pacifist, tax resister, and social justice advocate. Kehler objected to America's involvement in the Vietnam War and refused to cooperate with the draft. He is also known for his decision, along with his wife Betsy Corner, to stop paying federal income taxes in protest of war and military spending, a decision that led to the Internal Revenue Service (IRS) seizing their house in 1989.\n\nKehler was involved in several anti-war organizations in the 1960s and 1970s, and in the early 1980s was a leader in the movement against nuclear weapons.[1]\n\nEarly life and education\nKehler was born on July 16, 1944, in Bronxville, New York, and was raised in Scarsdale.[1] He attended Phillips Exeter Academy and graduated from Harvard University in 1967 with a degree in government.[1] While at Harvard, Kehler became involved with the Harlem chapter of Congress of Racial Equality (CORE).[1] Kehler has credited Martin Luther King Jr.'s \"I Have a Dream\" during the March on Washington for Jobs and Freedom in 1963 with shaping his interest in radical politics.[1]\n\nOpposition to the Vietnam War\nIn 1969, during the Vietnam War, Kehler returned his draft card to the Selective Service System. He refused to seek exemption as a conscientious objector, because he felt that doing so would be a form of cooperation with the US government's actions in Vietnam. After being called for induction and refusing to submit, he was charged with a federal crime. Found guilty at trial, Kehler served twenty-two months of a two-year sentence.[1]\n\nA 2020 documentary film, The Boys Who Said No!, features footage of and an interview with Kehler as one of several Vietnam-era draft resisters discussing that form of anti-war activism.[2]\n\nDaniel Ellsberg's exposure to Kehler in August 1969 (as Kehler was preparing to submit to his sentence) at the 13th Triennial Meeting of the War Resisters International, held at Haverford College, was a pivotal event in Ellsberg's decision to copy and release the Pentagon Papers.[3]\n\nAnti-nuclear activism\nKehler became active in anti-nuclear proliferation and nuclear disarmament movements while leading a grassroots campaign in western Massachusetts to support the concept of a nuclear freeze. His efforts led to his meeting fellow activist Randy Forsberg, who was leading a similar effort at a national level.[4] From 1981 through 1984, Kehler served as Executive Director of the National Nuclear Weapons Freeze Campaign.[5]\n\nKehler advocated against the use of nuclear power and led campaigns for the closure of nuclear power plants, including Vermont Yankee in Vernon, Vermont.[6][7]\n\nResistance of federal income tax\nFrom 1977 onward, Kehler and his wife Betsy Corner refused to pay their federal income taxes in protest of war and military expenditures; they continued to pay their state and local taxes, and donated the money they owed in federal income taxes to charity.[8] This led to the seizure of their house in Colrain, Massachusetts by the IRS in 1989. The home was subsequently purchased by the federal government. Kehler and Corner, along with supporters from the local community, struggled for years with the government and with another couple who attempted to purchase and move in to the home. The events were documented in the 1997 documentary film An Act of Conscience.[9][10][11]\n\nKehler died at his home in Shelburne Falls, Massachusetts, on July 21, 2024, at the age of 80.[5]\n\n"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722369179.7486, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722369168.9576, "finish": 1722369179.7486, "ip": "", "conv_id": "a1c84e4acb3d4ef8a7c5eebe9e5d4e4e", "model_name": "text-embedding-004", "prompt": ["Randy Kehler (July 16, 1944 – July 21, 2024) was an American pacifist, tax resister, and social justice advocate. Kehler objected to America's involvement in the Vietnam War and refused to cooperate with the draft. He is also known for his decision, along with his wife Betsy Corner, to stop paying federal income taxes in protest of war and military spending, a decision that led to the Internal Revenue Service (IRS) seizing their house in 1989.\n\nKehler was involved in several anti-war organizations in the 1960s and 1970s, and in the early 1980s was a leader in the movement against nuclear weapons.[1]\n\nEarly life and education\nKehler was born on July 16, 1944, in Bronxville, New York, and was raised in Scarsdale.[1] He attended Phillips Exeter Academy and graduated from Harvard University in 1967 with a degree in government.[1] While at Harvard, Kehler became involved with the Harlem chapter of Congress of Racial Equality (CORE).[1] Kehler has credited Martin Luther King Jr.'s \"I Have a Dream\" during the March on Washington for Jobs and Freedom in 1963 with shaping his interest in radical politics.[1]\n\nOpposition to the Vietnam War\nIn 1969, during the Vietnam War, Kehler returned his draft card to the Selective Service System. He refused to seek exemption as a conscientious objector, because he felt that doing so would be a form of cooperation with the US government's actions in Vietnam. After being called for induction and refusing to submit, he was charged with a federal crime. Found guilty at trial, Kehler served twenty-two months of a two-year sentence.[1]\n\nA 2020 documentary film, The Boys Who Said No!, features footage of and an interview with Kehler as one of several Vietnam-era draft resisters discussing that form of anti-war activism.[2]\n\nDaniel Ellsberg's exposure to Kehler in August 1969 (as Kehler was preparing to submit to his sentence) at the 13th Triennial Meeting of the War Resisters International, held at Haverford College, was a pivotal event in Ellsberg's decision to copy and release the Pentagon Papers.[3]\n\nAnti-nuclear activism\nKehler became active in anti-nuclear proliferation and nuclear disarmament movements while leading a grassroots campaign in western Massachusetts to support the concept of a nuclear freeze. His efforts led to his meeting fellow activist Randy Forsberg, who was leading a similar effort at a national level.[4] From 1981 through 1984, Kehler served as Executive Director of the National Nuclear Weapons Freeze Campaign.[5]\n\nKehler advocated against the use of nuclear power and led campaigns for the closure of nuclear power plants, including Vermont Yankee in Vernon, Vermont.[6][7]\n\nResistance of federal income tax\nFrom 1977 onward, Kehler and his wife Betsy Corner refused to pay their federal income taxes in protest of war and military expenditures; they continued to pay their state and local taxes, and donated the money they owed in federal income taxes to charity.[8] This led to the seizure of their house in Colrain, Massachusetts by the IRS in 1989. The home was subsequently purchased by the federal government. Kehler and Corner, along with supporters from the local community, struggled for years with the government and with another couple who attempted to purchase and move in to the home. The events were documented in the 1997 documentary film An Act of Conscience.[9][10][11]\n\nKehler died at his home in Shelburne Falls, Massachusetts, on July 21, 2024, at the age of 80.[5]\n\n", "convex", "plane", "parabolic", "concave", "wool", "denim", "linen", "question mark", "colon", "comma", "exclamation point", "period", "hyphen", "jiu-jitsu", "muay thai", "kung fu", "Chinese", "French"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722369179.7486, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722369168.9576, "finish": 1722369179.7486, "ip": "", "conv_id": "655d241a42174f3bad02c05df2f3e727", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Randy Kehler (July 16, 1944 – July 21, 2024) was an American pacifist, tax resister, and social justice advocate. Kehler objected to America's involvement in the Vietnam War and refused to cooperate with the draft. He is also known for his decision, along with his wife Betsy Corner, to stop paying federal income taxes in protest of war and military spending, a decision that led to the Internal Revenue Service (IRS) seizing their house in 1989.\n\nKehler was involved in several anti-war organizations in the 1960s and 1970s, and in the early 1980s was a leader in the movement against nuclear weapons.[1]\n\nEarly life and education\nKehler was born on July 16, 1944, in Bronxville, New York, and was raised in Scarsdale.[1] He attended Phillips Exeter Academy and graduated from Harvard University in 1967 with a degree in government.[1] While at Harvard, Kehler became involved with the Harlem chapter of Congress of Racial Equality (CORE).[1] Kehler has credited Martin Luther King Jr.'s \"I Have a Dream\" during the March on Washington for Jobs and Freedom in 1963 with shaping his interest in radical politics.[1]\n\nOpposition to the Vietnam War\nIn 1969, during the Vietnam War, Kehler returned his draft card to the Selective Service System. He refused to seek exemption as a conscientious objector, because he felt that doing so would be a form of cooperation with the US government's actions in Vietnam. After being called for induction and refusing to submit, he was charged with a federal crime. Found guilty at trial, Kehler served twenty-two months of a two-year sentence.[1]\n\nA 2020 documentary film, The Boys Who Said No!, features footage of and an interview with Kehler as one of several Vietnam-era draft resisters discussing that form of anti-war activism.[2]\n\nDaniel Ellsberg's exposure to Kehler in August 1969 (as Kehler was preparing to submit to his sentence) at the 13th Triennial Meeting of the War Resisters International, held at Haverford College, was a pivotal event in Ellsberg's decision to copy and release the Pentagon Papers.[3]\n\nAnti-nuclear activism\nKehler became active in anti-nuclear proliferation and nuclear disarmament movements while leading a grassroots campaign in western Massachusetts to support the concept of a nuclear freeze. His efforts led to his meeting fellow activist Randy Forsberg, who was leading a similar effort at a national level.[4] From 1981 through 1984, Kehler served as Executive Director of the National Nuclear Weapons Freeze Campaign.[5]\n\nKehler advocated against the use of nuclear power and led campaigns for the closure of nuclear power plants, including Vermont Yankee in Vernon, Vermont.[6][7]\n\nResistance of federal income tax\nFrom 1977 onward, Kehler and his wife Betsy Corner refused to pay their federal income taxes in protest of war and military expenditures; they continued to pay their state and local taxes, and donated the money they owed in federal income taxes to charity.[8] This led to the seizure of their house in Colrain, Massachusetts by the IRS in 1989. The home was subsequently purchased by the federal government. Kehler and Corner, along with supporters from the local community, struggled for years with the government and with another couple who attempted to purchase and move in to the home. The events were documented in the 1997 documentary film An Act of Conscience.[9][10][11]\n\nKehler died at his home in Shelburne Falls, Massachusetts, on July 21, 2024, at the age of 80.[5]\n\n", "convex", "plane", "parabolic", "concave", "wool", "denim", "linen", "question mark", "colon", "comma", "exclamation point", "period", "hyphen", "jiu-jitsu", "muay thai", "kung fu", "Chinese", "French"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722369158.5386, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722369158.4888, "finish": 1722369158.5386, "ip": "", "conv_id": "a1c84e4acb3d4ef8a7c5eebe9e5d4e4e", "model_name": "text-embedding-004", "prompt": ["Randy Kehler (July 16, 1944 \u2013 July 21, 2024) was an American pacifist, tax resister, and social justice advocate. Kehler objected to America's involvement in the Vietnam War and refused to cooperate with the draft. He is also known for his decision, along with his wife Betsy Corner, to stop paying federal income taxes in protest of war and military spending, a decision that led to the Internal Revenue Service (IRS) seizing their house in 1989.\n\nKehler was involved in several anti-war organizations in the 1960s and 1970s, and in the early 1980s was a leader in the movement against nuclear weapons.[1]\n\nEarly life and education\nKehler was born on July 16, 1944, in Bronxville, New York, and was raised in Scarsdale.[1] He attended Phillips Exeter Academy and graduated from Harvard University in 1967 with a degree in government.[1] While at Harvard, Kehler became involved with the Harlem chapter of Congress of Racial Equality (CORE).[1] Kehler has credited Martin Luther King Jr.'s \"I Have a Dream\" during the March on Washington for Jobs and Freedom in 1963 with shaping his interest in radical politics.[1]\n\nOpposition to the Vietnam War\nIn 1969, during the Vietnam War, Kehler returned his draft card to the Selective Service System. He refused to seek exemption as a conscientious objector, because he felt that doing so would be a form of cooperation with the US government's actions in Vietnam. After being called for induction and refusing to submit, he was charged with a federal crime. Found guilty at trial, Kehler served twenty-two months of a two-year sentence.[1]\n\nA 2020 documentary film, The Boys Who Said No!, features footage of and an interview with Kehler as one of several Vietnam-era draft resisters discussing that form of anti-war activism.[2]\n\nDaniel Ellsberg's exposure to Kehler in August 1969 (as Kehler was preparing to submit to his sentence) at the 13th Triennial Meeting of the War Resisters International, held at Haverford College, was a pivotal event in Ellsberg's decision to copy and release the Pentagon Papers.[3]\n\nAnti-nuclear activism\nKehler became active in anti-nuclear proliferation and nuclear disarmament movements while leading a grassroots campaign in western Massachusetts to support the concept of a nuclear freeze. His efforts led to his meeting fellow activist Randy Forsberg, who was leading a similar effort at a national level.[4] From 1981 through 1984, Kehler served as Executive Director of the National Nuclear Weapons Freeze Campaign.[5]\n\nKehler advocated against the use of nuclear power and led campaigns for the closure of nuclear power plants, including Vermont Yankee in Vernon, Vermont.[6][7]\n\nResistance of federal income tax\nFrom 1977 onward, Kehler and his wife Betsy Corner refused to pay their federal income taxes in protest of war and military expenditures; they continued to pay their state and local taxes, and donated the money they owed in federal income taxes to charity.[8] This led to the seizure of their house in Colrain, Massachusetts by the IRS in 1989. The home was subsequently purchased by the federal government. Kehler and Corner, along with supporters from the local community, struggled for years with the government and with another couple who attempted to purchase and move in to the home. The events were documented in the 1997 documentary film An Act of Conscience.[9][10][11]\n\nKehler died at his home in Shelburne Falls, Massachusetts, on July 21, 2024, at the age of 80.[5]\n\n"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722369158.5386, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722369158.4888, "finish": 1722369158.5386, "ip": "", "conv_id": "655d241a42174f3bad02c05df2f3e727", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Randy Kehler (July 16, 1944 \u2013 July 21, 2024) was an American pacifist, tax resister, and social justice advocate. Kehler objected to America's involvement in the Vietnam War and refused to cooperate with the draft. He is also known for his decision, along with his wife Betsy Corner, to stop paying federal income taxes in protest of war and military spending, a decision that led to the Internal Revenue Service (IRS) seizing their house in 1989.\n\nKehler was involved in several anti-war organizations in the 1960s and 1970s, and in the early 1980s was a leader in the movement against nuclear weapons.[1]\n\nEarly life and education\nKehler was born on July 16, 1944, in Bronxville, New York, and was raised in Scarsdale.[1] He attended Phillips Exeter Academy and graduated from Harvard University in 1967 with a degree in government.[1] While at Harvard, Kehler became involved with the Harlem chapter of Congress of Racial Equality (CORE).[1] Kehler has credited Martin Luther King Jr.'s \"I Have a Dream\" during the March on Washington for Jobs and Freedom in 1963 with shaping his interest in radical politics.[1]\n\nOpposition to the Vietnam War\nIn 1969, during the Vietnam War, Kehler returned his draft card to the Selective Service System. He refused to seek exemption as a conscientious objector, because he felt that doing so would be a form of cooperation with the US government's actions in Vietnam. After being called for induction and refusing to submit, he was charged with a federal crime. Found guilty at trial, Kehler served twenty-two months of a two-year sentence.[1]\n\nA 2020 documentary film, The Boys Who Said No!, features footage of and an interview with Kehler as one of several Vietnam-era draft resisters discussing that form of anti-war activism.[2]\n\nDaniel Ellsberg's exposure to Kehler in August 1969 (as Kehler was preparing to submit to his sentence) at the 13th Triennial Meeting of the War Resisters International, held at Haverford College, was a pivotal event in Ellsberg's decision to copy and release the Pentagon Papers.[3]\n\nAnti-nuclear activism\nKehler became active in anti-nuclear proliferation and nuclear disarmament movements while leading a grassroots campaign in western Massachusetts to support the concept of a nuclear freeze. His efforts led to his meeting fellow activist Randy Forsberg, who was leading a similar effort at a national level.[4] From 1981 through 1984, Kehler served as Executive Director of the National Nuclear Weapons Freeze Campaign.[5]\n\nKehler advocated against the use of nuclear power and led campaigns for the closure of nuclear power plants, including Vermont Yankee in Vernon, Vermont.[6][7]\n\nResistance of federal income tax\nFrom 1977 onward, Kehler and his wife Betsy Corner refused to pay their federal income taxes in protest of war and military expenditures; they continued to pay their state and local taxes, and donated the money they owed in federal income taxes to charity.[8] This led to the seizure of their house in Colrain, Massachusetts by the IRS in 1989. The home was subsequently purchased by the federal government. Kehler and Corner, along with supporters from the local community, struggled for years with the government and with another couple who attempted to purchase and move in to the home. The events were documented in the 1997 documentary film An Act of Conscience.[9][10][11]\n\nKehler died at his home in Shelburne Falls, Massachusetts, on July 21, 2024, at the age of 80.[5]\n\n"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722369179.7486, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722369168.9576, "finish": 1722369179.7486, "ip": "", "conv_id": "a1c84e4acb3d4ef8a7c5eebe9e5d4e4e", "model_name": "text-embedding-004", "prompt": ["Randy Kehler (July 16, 1944 \u2013 July 21, 2024) was an American pacifist, tax resister, and social justice advocate. Kehler objected to America's involvement in the Vietnam War and refused to cooperate with the draft. He is also known for his decision, along with his wife Betsy Corner, to stop paying federal income taxes in protest of war and military spending, a decision that led to the Internal Revenue Service (IRS) seizing their house in 1989.\n\nKehler was involved in several anti-war organizations in the 1960s and 1970s, and in the early 1980s was a leader in the movement against nuclear weapons.[1]\n\nEarly life and education\nKehler was born on July 16, 1944, in Bronxville, New York, and was raised in Scarsdale.[1] He attended Phillips Exeter Academy and graduated from Harvard University in 1967 with a degree in government.[1] While at Harvard, Kehler became involved with the Harlem chapter of Congress of Racial Equality (CORE).[1] Kehler has credited Martin Luther King Jr.'s \"I Have a Dream\" during the March on Washington for Jobs and Freedom in 1963 with shaping his interest in radical politics.[1]\n\nOpposition to the Vietnam War\nIn 1969, during the Vietnam War, Kehler returned his draft card to the Selective Service System. He refused to seek exemption as a conscientious objector, because he felt that doing so would be a form of cooperation with the US government's actions in Vietnam. After being called for induction and refusing to submit, he was charged with a federal crime. Found guilty at trial, Kehler served twenty-two months of a two-year sentence.[1]\n\nA 2020 documentary film, The Boys Who Said No!, features footage of and an interview with Kehler as one of several Vietnam-era draft resisters discussing that form of anti-war activism.[2]\n\nDaniel Ellsberg's exposure to Kehler in August 1969 (as Kehler was preparing to submit to his sentence) at the 13th Triennial Meeting of the War Resisters International, held at Haverford College, was a pivotal event in Ellsberg's decision to copy and release the Pentagon Papers.[3]\n\nAnti-nuclear activism\nKehler became active in anti-nuclear proliferation and nuclear disarmament movements while leading a grassroots campaign in western Massachusetts to support the concept of a nuclear freeze. His efforts led to his meeting fellow activist Randy Forsberg, who was leading a similar effort at a national level.[4] From 1981 through 1984, Kehler served as Executive Director of the National Nuclear Weapons Freeze Campaign.[5]\n\nKehler advocated against the use of nuclear power and led campaigns for the closure of nuclear power plants, including Vermont Yankee in Vernon, Vermont.[6][7]\n\nResistance of federal income tax\nFrom 1977 onward, Kehler and his wife Betsy Corner refused to pay their federal income taxes in protest of war and military expenditures; they continued to pay their state and local taxes, and donated the money they owed in federal income taxes to charity.[8] This led to the seizure of their house in Colrain, Massachusetts by the IRS in 1989. The home was subsequently purchased by the federal government. Kehler and Corner, along with supporters from the local community, struggled for years with the government and with another couple who attempted to purchase and move in to the home. The events were documented in the 1997 documentary film An Act of Conscience.[9][10][11]\n\nKehler died at his home in Shelburne Falls, Massachusetts, on July 21, 2024, at the age of 80.[5]\n\n", "convex", "plane", "parabolic", "concave", "wool", "denim", "linen", "question mark", "colon", "comma", "exclamation point", "period", "hyphen", "jiu-jitsu", "muay thai", "kung fu", "Chinese", "French"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722369179.7486, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722369168.9576, "finish": 1722369179.7486, "ip": "", "conv_id": "655d241a42174f3bad02c05df2f3e727", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Randy Kehler (July 16, 1944 \u2013 July 21, 2024) was an American pacifist, tax resister, and social justice advocate. Kehler objected to America's involvement in the Vietnam War and refused to cooperate with the draft. He is also known for his decision, along with his wife Betsy Corner, to stop paying federal income taxes in protest of war and military spending, a decision that led to the Internal Revenue Service (IRS) seizing their house in 1989.\n\nKehler was involved in several anti-war organizations in the 1960s and 1970s, and in the early 1980s was a leader in the movement against nuclear weapons.[1]\n\nEarly life and education\nKehler was born on July 16, 1944, in Bronxville, New York, and was raised in Scarsdale.[1] He attended Phillips Exeter Academy and graduated from Harvard University in 1967 with a degree in government.[1] While at Harvard, Kehler became involved with the Harlem chapter of Congress of Racial Equality (CORE).[1] Kehler has credited Martin Luther King Jr.'s \"I Have a Dream\" during the March on Washington for Jobs and Freedom in 1963 with shaping his interest in radical politics.[1]\n\nOpposition to the Vietnam War\nIn 1969, during the Vietnam War, Kehler returned his draft card to the Selective Service System. He refused to seek exemption as a conscientious objector, because he felt that doing so would be a form of cooperation with the US government's actions in Vietnam. After being called for induction and refusing to submit, he was charged with a federal crime. Found guilty at trial, Kehler served twenty-two months of a two-year sentence.[1]\n\nA 2020 documentary film, The Boys Who Said No!, features footage of and an interview with Kehler as one of several Vietnam-era draft resisters discussing that form of anti-war activism.[2]\n\nDaniel Ellsberg's exposure to Kehler in August 1969 (as Kehler was preparing to submit to his sentence) at the 13th Triennial Meeting of the War Resisters International, held at Haverford College, was a pivotal event in Ellsberg's decision to copy and release the Pentagon Papers.[3]\n\nAnti-nuclear activism\nKehler became active in anti-nuclear proliferation and nuclear disarmament movements while leading a grassroots campaign in western Massachusetts to support the concept of a nuclear freeze. His efforts led to his meeting fellow activist Randy Forsberg, who was leading a similar effort at a national level.[4] From 1981 through 1984, Kehler served as Executive Director of the National Nuclear Weapons Freeze Campaign.[5]\n\nKehler advocated against the use of nuclear power and led campaigns for the closure of nuclear power plants, including Vermont Yankee in Vernon, Vermont.[6][7]\n\nResistance of federal income tax\nFrom 1977 onward, Kehler and his wife Betsy Corner refused to pay their federal income taxes in protest of war and military expenditures; they continued to pay their state and local taxes, and donated the money they owed in federal income taxes to charity.[8] This led to the seizure of their house in Colrain, Massachusetts by the IRS in 1989. The home was subsequently purchased by the federal government. Kehler and Corner, along with supporters from the local community, struggled for years with the government and with another couple who attempted to purchase and move in to the home. The events were documented in the 1997 documentary film An Act of Conscience.[9][10][11]\n\nKehler died at his home in Shelburne Falls, Massachusetts, on July 21, 2024, at the age of 80.[5]\n\n", "convex", "plane", "parabolic", "concave", "wool", "denim", "linen", "question mark", "colon", "comma", "exclamation point", "period", "hyphen", "jiu-jitsu", "muay thai", "kung fu", "Chinese", "French"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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 {"tstamp": 1722377365.7802, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722377365.1018, "finish": 1722377365.7802, "ip": "", "conv_id": "d75c178f71aa4029b483e659345f2810", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722377365.7802, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722377365.1018, "finish": 1722377365.7802, "ip": "", "conv_id": "0791968853c24b55ac4ee1321eee3890", "model_name": "text-embedding-3-large", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
@@ -62,7 +55,6 @@
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 {"tstamp": 1722377438.5758, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722377438.4864, "finish": 1722377438.5758, "ip": "", "conv_id": "b978f54eb46d43958c35fe696b370749", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["muay thai", "kung fu", "rupee", "yen", "bass", "mackerel", "trout"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-9c2f2ba1-3fd3-43ca-a968-8d9a53b79a4c.jsonl

@@ -2,28 +2,19 @@
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data/clustering_individual-b9229914-47bc-4da8-a21b-89329fff8207.jsonl

@@ -2,8 +2,8 @@
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data/clustering_individual-cc11c9ee-fc93-4cd6-a4b5-246a7f21038c.jsonl

@@ -8,7 +8,6 @@
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 {"tstamp": 1722332262.454, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722332262.3737, "finish": 1722332262.454, "ip": "", "conv_id": "376e71040faa4bf5b22698108934a486", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["toric", "progressive", "octagon", "rectangle", "linen", "polyester", "wool", "silk", "cotton", "leather", "nitrogen", "calcium", "sodium", "iron", "oxygen", "power supply", "CPU", "hard drive", "GPU", "RAM"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722354917.2437, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722354917.1731, "finish": 1722354917.2437, "ip": "", "conv_id": "1db398f712f84d478f31a6cd2a450750", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["North America", "Asia", "linguine", "spaghetti", "lasagna", "fettuccine"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722354917.2437, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722354917.1731, "finish": 1722354917.2437, "ip": "", "conv_id": "ca7609be3f2d44a3a851fc4de73429fe", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["North America", "Asia", "linguine", "spaghetti", "lasagna", "fettuccine"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_individual-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

@@ -1,6 +1,6 @@
 {"tstamp": 1722404039.6586, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722404030.4965, "finish": 1722404039.6586, "ip": "", "conv_id": "645bc99853634e95b1d3d7d38bf439e4", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["canine", "wisdom tooth", "cricket", "tennis", "sneakers", "high heels", "loafers", "sandals", "boots", "hydroelectric", "biomass", "quill", "crayon", "fountain pen", "pencil", "chalk"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722404039.6586, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722404030.4965, "finish": 1722404039.6586, "ip": "", "conv_id": "8920ba375d3d4b5b8da667b39fb2aee1", "model_name": "text-embedding-004", "prompt": ["canine", "wisdom tooth", "cricket", "tennis", "sneakers", "high heels", "loafers", "sandals", "boots", "hydroelectric", "biomass", "quill", "crayon", "fountain pen", "pencil", "chalk"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
-{"tstamp": 1722406142.1568, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722406142.1303, "finish": 1722406142.1568, "ip": "", "conv_id": "65e94e06e60049a0b93f76e75f5f3795", "model_name": "GritLM/GritLM-7B", "prompt": ["Türkçe biliyor musun "], "ncluster": 1, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722406142.1568, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722406142.1303, "finish": 1722406142.1568, "ip": "", "conv_id": "65e94e06e60049a0b93f76e75f5f3795", "model_name": "GritLM/GritLM-7B", "prompt": ["T\u00fcrk\u00e7e biliyor musun "], "ncluster": 1, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722418842.482, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722418842.1963, "finish": 1722418842.482, "ip": "", "conv_id": "c1ac01c2f4384e999c782170c0e08531", "model_name": "embed-english-v3.0", "prompt": ["green", "red", "orange", "yellow", "blue", "purple", "pink", "banana", "mango", "pear", "apple", "kiwi", "hoe", "wheelbarrow", "shovel", "RAM", "power supply", "SSD", "motherboard", "CPU", "hard drive", "AWS", "Azure", "IBM Cloud", "Google Cloud"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722418842.482, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722418842.1963, "finish": 1722418842.482, "ip": "", "conv_id": "66e0f885e0ac467b83d06bb1d5811e61", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["green", "red", "orange", "yellow", "blue", "purple", "pink", "banana", "mango", "pear", "apple", "kiwi", "hoe", "wheelbarrow", "shovel", "RAM", "power supply", "SSD", "motherboard", "CPU", "hard drive", "AWS", "Azure", "IBM Cloud", "Google Cloud"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722418849.5116, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722418849.2741, "finish": 1722418849.5116, "ip": "", "conv_id": "c1ac01c2f4384e999c782170c0e08531", "model_name": "embed-english-v3.0", "prompt": ["green", "red", "orange", "yellow", "blue", "purple", "pink", "banana", "mango", "pear", "apple", "kiwi", "hoe", "wheelbarrow", "shovel", "RAM", "power supply", "SSD", "motherboard", "CPU", "hard drive", "AWS", "Azure", "IBM Cloud", "Google Cloud"], "ncluster": 5, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
@@ -9,3 +9,11 @@
 {"tstamp": 1722419201.6559, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722419201.4263, "finish": 1722419201.6559, "ip": "", "conv_id": "053450d09e7d47c1ba1a39e226cdfbc4", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722419250.4205, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722419250.3309, "finish": 1722419250.4205, "ip": "", "conv_id": "346e5cbb1f934cf6911d6ce7bfa3d236", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722419250.4205, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722419250.3309, "finish": 1722419250.4205, "ip": "", "conv_id": "ba8218c6c5ce4b98ae2e8e4e97573696", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722434299.6602, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722434299.6151, "finish": 1722434299.6602, "ip": "", "conv_id": "938bf910e95f4eb393b20ba88e335007", "model_name": "GritLM/GritLM-7B", "prompt": ["Mexico"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722434299.6602, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722434299.6151, "finish": 1722434299.6602, "ip": "", "conv_id": "ebdaf08c76f649468390d3d01dbedc98", "model_name": "text-embedding-004", "prompt": ["Mexico"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722434304.705, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722434304.4767, "finish": 1722434304.705, "ip": "", "conv_id": "938bf910e95f4eb393b20ba88e335007", "model_name": "GritLM/GritLM-7B", "prompt": ["Mexico", "Munich"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722434304.705, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722434304.4767, "finish": 1722434304.705, "ip": "", "conv_id": "ebdaf08c76f649468390d3d01dbedc98", "model_name": "text-embedding-004", "prompt": ["Mexico", "Munich"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722434315.3251, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722434315.0532, "finish": 1722434315.3251, "ip": "", "conv_id": "938bf910e95f4eb393b20ba88e335007", "model_name": "GritLM/GritLM-7B", "prompt": ["Mexico", "Munich", "green"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722434315.3251, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722434315.0532, "finish": 1722434315.3251, "ip": "", "conv_id": "ebdaf08c76f649468390d3d01dbedc98", "model_name": "text-embedding-004", "prompt": ["Mexico", "Munich", "green"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722434360.303, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722434360.0462, "finish": 1722434360.303, "ip": "", "conv_id": "938bf910e95f4eb393b20ba88e335007", "model_name": "GritLM/GritLM-7B", "prompt": ["Mexico", "Munich", "green", "pink"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722434360.303, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722434360.0462, "finish": 1722434360.303, "ip": "", "conv_id": "ebdaf08c76f649468390d3d01dbedc98", "model_name": "text-embedding-004", "prompt": ["Mexico", "Munich", "green", "pink"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_battle-02ddbc43-8a77-4d21-b3a5-485d76c2003b.jsonl

@@ -1,2 +1 @@
-{"tstamp": 1722223022.6009, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "776f02bd82f74b80aff4960db5817568", "0_model_name": "BM25", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Stackoverflow Stackexchange\n\nQ: Controller Testing with Minitest and Devise failing In a Rails 3.28 application using Devise, I setup a controller that checks whether a user is logged in or not with if user_signed_in?. Once the tests hit this line, I always get a undefined method 'authenticate!' for nil:NilClass error. I wanted to test some code in a scenario that no user is logged in.\nI am thinking that I need to setup the tests according to the options specified in the Devise Wiki here, however, all the instructions pertain to rspec.\nIn particular, I think I need to have the following to work in minitest:\nRSpec.configure do |config|\n  config.include Devise::TestHelpers, :type => :controller\n  config.extend ControllerMacros, :type => :controller\nend\n\nHas anyone succeded in setting this up to have Devise work with minitest?\n\nA: Add the following to your minitest_helper.rb file:\nclass MiniTest::Rails::ActionController::TestCase\n  include Devise::TestHelpers\nend\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "d88e38d4fcfa46b08b4d9c9a414d77c7", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Stackoverflow Stackexchange\n\nQ: android - getting nullpointerexception for no specific reason I am working on an android project and as far as I know, I don't make any specific mistakes in my code that would lead to an exception. \nI get these two error lines everytime I run my project, but the rest of my code runs without any issues.\nThe NullPointerException doesn't have a concrete occurance location, which confuses me.\nI have already searched for similar issues. A possible solution was adding the READ_EXTERNAL_STORAGE and WRITE_EXTERNAL_STORAGE as uses-permission in my Android Manifest file, but to no avail.\n2020-02-28 XX:XX:XX.333 14615-14651/com.example.package_name E/Perf: Fail to get file list com.example.package_name\n2020-02-28 XX:XX:XX.333 14615-14651/com.example.package_name E/Perf: getFolderSize() : Exception_1 = java.lang.NullPointerException: Attempt to get length of null array\n\n\nA: The error was thrown by my OnePlus, on the android emulator there was no error shown\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722223253.8945, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "42ce372770374b24ad9d88378f058800", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "0_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."]], "0_corpus": "arxiv", "1_conv_id": "4cce5456d4794da8bc4d8b54a94fa7a6", "1_model_name": "text-embedding-004", "1_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "1_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."]], "1_corpus": "arxiv"}

data/retrieval_battle-0325b067-7cac-407c-9ffa-76d99f10c9f7.jsonl

@@ -2,8 +2,4 @@
 {"tstamp": 1722264341.2759, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "220765d4770d4fef8af22b7bbcdda9d5", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Brave New World Aldous Huxley", "0_output": [["Brave New World Aldous Huxley", "Scifi Stackexchange\n\nQ: Can someone identify this book, where children are bred for specific jobs? Things I remember about this book:\n\n\n*\n\n*Every child was selected for a certain job/career;\n\n*If twins were born, one was always killed;\n\n*Seeing colors for the first time (describing the color of an apple);\n\n*Seeing war and death and poverty.\n\n\nDoes anyone have any idea what this book might be?\n\nA: I don't remember the exact details about senses and conditioning, but Huxley's Brave New World fits with the job-specific breeding.\n"]], "0_corpus": "stackexchange", "1_conv_id": "16a9be556f8a44b4a2957bd030a812d1", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Brave New World Aldous Huxley", "1_output": [["Brave New World Aldous Huxley", "Stackoverflow Stackexchange\n\nQ: Spying on selfinjecting bean How to spy on a bean, that injects itself?\nSuppose we have\n@Service\npublic class SomeClass {\n    @Autowired\n    private SomeClass someClass;\n    ...\n}\n\nNow I want to use the following in my test:\n@SpyBean\nprivate SomeClass someClass;\n\nI'm getting the following error:\n\nBean with name 'someClass' has been injected into other beans [someClass] in its raw version as part of a circular reference, but has eventually been wrapped. This means that said other beans do not use the final version of the bean. This is often the result of over-eager type matching - consider using 'getBeanNamesOfType' with the 'allowEagerInit' flag turned off, for example.\n\nAlso without self injection tests run flawlessly.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722264358.0593, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "16064c069fd346239d4042c7655a55a3", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Brave New World Aldous Huxley", "0_output": [["Brave New World Aldous Huxley", "Scifi Stackexchange\n\nQ: Can someone identify this book, where children are bred for specific jobs? Things I remember about this book:\n\n\n*\n\n*Every child was selected for a certain job/career;\n\n*If twins were born, one was always killed;\n\n*Seeing colors for the first time (describing the color of an apple);\n\n*Seeing war and death and poverty.\n\n\nDoes anyone have any idea what this book might be?\n\nA: I don't remember the exact details about senses and conditioning, but Huxley's Brave New World fits with the job-specific breeding.\n"]], "0_corpus": "stackexchange", "1_conv_id": "7fed0018728a4e1ab6f78a71685f5183", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Brave New World Aldous Huxley", "1_output": [["Brave New World Aldous Huxley", "Stackoverflow Stackexchange\n\nQ: Spying on selfinjecting bean How to spy on a bean, that injects itself?\nSuppose we have\n@Service\npublic class SomeClass {\n    @Autowired\n    private SomeClass someClass;\n    ...\n}\n\nNow I want to use the following in my test:\n@SpyBean\nprivate SomeClass someClass;\n\nI'm getting the following error:\n\nBean with name 'someClass' has been injected into other beans [someClass] in its raw version as part of a circular reference, but has eventually been wrapped. This means that said other beans do not use the final version of the bean. This is often the result of over-eager type matching - consider using 'getBeanNamesOfType' with the 'allowEagerInit' flag turned off, for example.\n\nAlso without self injection tests run flawlessly.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722264366.3314, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "2ff44f1683a2402088a4b6c00e43e0bd", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Brave New World Aldous Huxley", "0_output": [["Brave New World Aldous Huxley", "Scifi Stackexchange\n\nQ: Can someone identify this book, where children are bred for specific jobs? Things I remember about this book:\n\n\n*\n\n*Every child was selected for a certain job/career;\n\n*If twins were born, one was always killed;\n\n*Seeing colors for the first time (describing the color of an apple);\n\n*Seeing war and death and poverty.\n\n\nDoes anyone have any idea what this book might be?\n\nA: I don't remember the exact details about senses and conditioning, but Huxley's Brave New World fits with the job-specific breeding.\n"]], "0_corpus": "stackexchange", "1_conv_id": "ba2e1f6e5cce435ca5e45f3d4f5cb369", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Brave New World Aldous Huxley", "1_output": [["Brave New World Aldous Huxley", "Scifi Stackexchange\n\nQ: Can someone identify this book, where children are bred for specific jobs? Things I remember about this book:\n\n\n*\n\n*Every child was selected for a certain job/career;\n\n*If twins were born, one was always killed;\n\n*Seeing colors for the first time (describing the color of an apple);\n\n*Seeing war and death and poverty.\n\n\nDoes anyone have any idea what this book might be?\n\nA: I don't remember the exact details about senses and conditioning, but Huxley's Brave New World fits with the job-specific breeding.\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722265189.8218, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "1ea9d7c8b1c44c2ba8dcad8d187ced07", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "In English when to use \u2013, \u2014 vs -?", "0_output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use -, \u2013 and \u2014? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (\u2013) and em-dashes (\u2014).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London\u2013Amsterdam flight is delayed.\n\n*The meeting is from 3:00\u20134:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire\u2014it had been awake half the night\u2014flew out of its coffin.\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "35c5ee02a1934fa2abbc55634c12bc8f", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "In English when to use \u2013, \u2014 vs -?", "1_output": [["In English when to use \u2013, \u2014 vs -?", "Serverfault Stackexchange\n\nQ: Xdebug Profiler Aggregate always crashes (xdebug.profiler_aggregate=1 causes coredump in xdebug.so xdebug_profiler_output_aggr_data()) Whenever I go to localhost I get a 404 error, until I mark xdebug.profiler_aggregate=0\nThe reason why is xdebug.so coredumps (SEGFAULT)\n[Xdebug]\nzend_extension=/usr/lib/php/20160303/xdebug.so\nxdebug.remote_enable=1\nxdebug.remote_connect_back = 0\nxdebug.remote_handler = \"dbgp\"\nxdebug.remote_port = 9000\nxdebug.idekey = \"PHPSTORM\"\nxdebug.profiler_enable=1\nxdebug.profiler_output_dir=/tmp/\nxdebug.profiler_aggregate=1\n\n$ dmesg\n[  584.455746] php-fpm7.1[4645]: segfault at 30 ip 00007f5d3969f6b3 sp 00007ffdc8b87a20 error 4 in xdebug.so[7f5d3967e000+39000]\n\n$\u00a0sudo cat /var/log/php7.1-fpm.log\n[31-Aug-2017 14:59:01] WARNING: [pool www] child 4645 exited on signal 11 (SIGSEGV - core dumped) after 0.138218 seconds from start\n[31-Aug-2017 14:59:01] NOTICE: [pool www] child 4647 started\n[31-Aug-2017 14:59:07] NOTICE: Terminating ...\n[31-Aug-2017 14:59:07] NOTICE: exiting, bye-bye!\nin xdebug_profiler_output_aggr_data() with 0 entries\n\nWhen I change the last line in the conf file to\nxdebug.profiler_aggregate=0\nit works fine again.\nI've tried reinstalling php, xdebug, and updating and restarting, and I've tried removing all other xdebug settings except for that one\n\nA: I don't know how you have installed Xdebug, but I suggest you to install a recent version of Xdebug, with pecl, not with apt (if you have apt).\nsudo apt-get remove php-xdebug\nsudo apt install php-pear php-dev\nsudo pecl install xdebug\n\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722265212.1176, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "e5ec831cc92841dba4c8be52734fcfa1", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "In English when to use \u2013, \u2014 vs -?", "0_output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use -, \u2013 and \u2014? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (\u2013) and em-dashes (\u2014).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London\u2013Amsterdam flight is delayed.\n\n*The meeting is from 3:00\u20134:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire\u2014it had been awake half the night\u2014flew out of its coffin.\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "932248db7f9240a392b0995752bec422", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "In English when to use \u2013, \u2014 vs -?", "1_output": [["In English when to use \u2013, \u2014 vs -?", "Serverfault Stackexchange\n\nQ: Xdebug Profiler Aggregate always crashes (xdebug.profiler_aggregate=1 causes coredump in xdebug.so xdebug_profiler_output_aggr_data()) Whenever I go to localhost I get a 404 error, until I mark xdebug.profiler_aggregate=0\nThe reason why is xdebug.so coredumps (SEGFAULT)\n[Xdebug]\nzend_extension=/usr/lib/php/20160303/xdebug.so\nxdebug.remote_enable=1\nxdebug.remote_connect_back = 0\nxdebug.remote_handler = \"dbgp\"\nxdebug.remote_port = 9000\nxdebug.idekey = \"PHPSTORM\"\nxdebug.profiler_enable=1\nxdebug.profiler_output_dir=/tmp/\nxdebug.profiler_aggregate=1\n\n$ dmesg\n[  584.455746] php-fpm7.1[4645]: segfault at 30 ip 00007f5d3969f6b3 sp 00007ffdc8b87a20 error 4 in xdebug.so[7f5d3967e000+39000]\n\n$\u00a0sudo cat /var/log/php7.1-fpm.log\n[31-Aug-2017 14:59:01] WARNING: [pool www] child 4645 exited on signal 11 (SIGSEGV - core dumped) after 0.138218 seconds from start\n[31-Aug-2017 14:59:01] NOTICE: [pool www] child 4647 started\n[31-Aug-2017 14:59:07] NOTICE: Terminating ...\n[31-Aug-2017 14:59:07] NOTICE: exiting, bye-bye!\nin xdebug_profiler_output_aggr_data() with 0 entries\n\nWhen I change the last line in the conf file to\nxdebug.profiler_aggregate=0\nit works fine again.\nI've tried reinstalling php, xdebug, and updating and restarting, and I've tried removing all other xdebug settings except for that one\n\nA: I don't know how you have installed Xdebug, but I suggest you to install a recent version of Xdebug, with pecl, not with apt (if you have apt).\nsudo apt-get remove php-xdebug\nsudo apt install php-pear php-dev\nsudo pecl install xdebug\n\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722265217.8761, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8e8b4d339c2c40c39b9b0e23e505899e", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "In English when to use \u2013, \u2014 vs -?", "0_output": [["In English when to use \u2013, \u2014 vs -?", "Serverfault Stackexchange\n\nQ: Xdebug Profiler Aggregate always crashes (xdebug.profiler_aggregate=1 causes coredump in xdebug.so xdebug_profiler_output_aggr_data()) Whenever I go to localhost I get a 404 error, until I mark xdebug.profiler_aggregate=0\nThe reason why is xdebug.so coredumps (SEGFAULT)\n[Xdebug]\nzend_extension=/usr/lib/php/20160303/xdebug.so\nxdebug.remote_enable=1\nxdebug.remote_connect_back = 0\nxdebug.remote_handler = \"dbgp\"\nxdebug.remote_port = 9000\nxdebug.idekey = \"PHPSTORM\"\nxdebug.profiler_enable=1\nxdebug.profiler_output_dir=/tmp/\nxdebug.profiler_aggregate=1\n\n$ dmesg\n[  584.455746] php-fpm7.1[4645]: segfault at 30 ip 00007f5d3969f6b3 sp 00007ffdc8b87a20 error 4 in xdebug.so[7f5d3967e000+39000]\n\n$\u00a0sudo cat /var/log/php7.1-fpm.log\n[31-Aug-2017 14:59:01] WARNING: [pool www] child 4645 exited on signal 11 (SIGSEGV - core dumped) after 0.138218 seconds from start\n[31-Aug-2017 14:59:01] NOTICE: [pool www] child 4647 started\n[31-Aug-2017 14:59:07] NOTICE: Terminating ...\n[31-Aug-2017 14:59:07] NOTICE: exiting, bye-bye!\nin xdebug_profiler_output_aggr_data() with 0 entries\n\nWhen I change the last line in the conf file to\nxdebug.profiler_aggregate=0\nit works fine again.\nI've tried reinstalling php, xdebug, and updating and restarting, and I've tried removing all other xdebug settings except for that one\n\nA: I don't know how you have installed Xdebug, but I suggest you to install a recent version of Xdebug, with pecl, not with apt (if you have apt).\nsudo apt-get remove php-xdebug\nsudo apt install php-pear php-dev\nsudo pecl install xdebug\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "11a7b09091d546a0a456f974b24cc0ec", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "In English when to use \u2013, \u2014 vs -?", "1_output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use -, \u2013 and \u2014? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (\u2013) and em-dashes (\u2014).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London\u2013Amsterdam flight is delayed.\n\n*The meeting is from 3:00\u20134:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire\u2014it had been awake half the night\u2014flew out of its coffin.\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722265223.6933, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "ad89106dd2ac429195138135d84158f9", "0_model_name": "BM25", "0_prompt": "In English when to use \u2013, \u2014 vs -?", "0_output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use \"If I was\" vs. \"If I were\"? \n*\n\n*If I was...\n\n*If I were...\n\n\nWhen is it correct to use \"If I was\" vs. \"If I were\" in standard English?\n\nA: When in doubt, always use the subjunctive mood:\n\nIf I were you...\n\nIt will make you sound smarter and it is technically correct since \"the subjunctive mood is used to express a wish or possible situation that is currently not true.\"\n"]], "0_corpus": "stackexchange", "1_conv_id": "3beaa950d23844ba9d09916b299805f5", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "In English when to use \u2013, \u2014 vs -?", "1_output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use -, \u2013 and \u2014? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (\u2013) and em-dashes (\u2014).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London\u2013Amsterdam flight is delayed.\n\n*The meeting is from 3:00\u20134:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire\u2014it had been awake half the night\u2014flew out of its coffin.\n\n"]], "1_corpus": "stackexchange"}
-{"tstamp": 1722265231.0602, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "1277d2d9c15847a0a8ee99fbe5573d0d", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "In English when to use \u2013, \u2014 vs -?", "0_output": [["In English when to use \u2013, \u2014 vs -?", "Serverfault Stackexchange\n\nQ: Xdebug Profiler Aggregate always crashes (xdebug.profiler_aggregate=1 causes coredump in xdebug.so xdebug_profiler_output_aggr_data()) Whenever I go to localhost I get a 404 error, until I mark xdebug.profiler_aggregate=0\nThe reason why is xdebug.so coredumps (SEGFAULT)\n[Xdebug]\nzend_extension=/usr/lib/php/20160303/xdebug.so\nxdebug.remote_enable=1\nxdebug.remote_connect_back = 0\nxdebug.remote_handler = \"dbgp\"\nxdebug.remote_port = 9000\nxdebug.idekey = \"PHPSTORM\"\nxdebug.profiler_enable=1\nxdebug.profiler_output_dir=/tmp/\nxdebug.profiler_aggregate=1\n\n$ dmesg\n[  584.455746] php-fpm7.1[4645]: segfault at 30 ip 00007f5d3969f6b3 sp 00007ffdc8b87a20 error 4 in xdebug.so[7f5d3967e000+39000]\n\n$\u00a0sudo cat /var/log/php7.1-fpm.log\n[31-Aug-2017 14:59:01] WARNING: [pool www] child 4645 exited on signal 11 (SIGSEGV - core dumped) after 0.138218 seconds from start\n[31-Aug-2017 14:59:01] NOTICE: [pool www] child 4647 started\n[31-Aug-2017 14:59:07] NOTICE: Terminating ...\n[31-Aug-2017 14:59:07] NOTICE: exiting, bye-bye!\nin xdebug_profiler_output_aggr_data() with 0 entries\n\nWhen I change the last line in the conf file to\nxdebug.profiler_aggregate=0\nit works fine again.\nI've tried reinstalling php, xdebug, and updating and restarting, and I've tried removing all other xdebug settings except for that one\n\nA: I don't know how you have installed Xdebug, but I suggest you to install a recent version of Xdebug, with pecl, not with apt (if you have apt).\nsudo apt-get remove php-xdebug\nsudo apt install php-pear php-dev\nsudo pecl install xdebug\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "8215081572f64d86b34945865ce9b2f8", "1_model_name": "BM25", "1_prompt": "In English when to use \u2013, \u2014 vs -?", "1_output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use \"If I was\" vs. \"If I were\"? \n*\n\n*If I was...\n\n*If I were...\n\n\nWhen is it correct to use \"If I was\" vs. \"If I were\" in standard English?\n\nA: When in doubt, always use the subjunctive mood:\n\nIf I were you...\n\nIt will make you sound smarter and it is technically correct since \"the subjunctive mood is used to express a wish or possible situation that is currently not true.\"\n"]], "1_corpus": "stackexchange"}

data/retrieval_battle-1fa504af-967a-4ff8-a6ff-4abf5fbdd1c5.jsonl

@@ -1,4 +1,3 @@
 {"tstamp": 1722266149.2701, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "8c04b396b11b41daa58f4645afb37357", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "do dusk balls work in max raid dens?", "0_output": [["do dusk balls work in max raid dens?", "Gaming Stackexchange\n\nQ: Do turns carry over from previous encounters with fleeing Legendary Pok\u00e9mon? I've been trying to catch Thundurus, but I don't know which is the best Pok\u00e9 Ball, the Quick ball or the Ultra ball? \nI left the wild Thundurus with 1 hp and asleep, but I don't know if it counts as one turn on the new encounters or if my previous turns carry over. I am using both types of Pok\u00e9 Balls at the moment, and it does not look like any are good. >.<\n\nA: Despite the fact that you may have been pecking away at wandering pokemon for many, many turns, whenever you meet them, it starts a new battle, from turn 1.\nThis means that the Quick Ball has twice the capture rate (x4) than that of the Ultra Ball (x2).\n"]], "0_corpus": "stackexchange", "1_conv_id": "ecb249a3187244458381cbb0af7b2bb2", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "do dusk balls work in max raid dens?", "1_output": [["do dusk balls work in max raid dens?", "Stackoverflow Stackexchange\n\nQ: AWS-SDK: Query parameter in listobjects of S3 I want to fetch objects after a particular date. Using the AWS CLI I can list objects using below command: \naws s3api list-objects-v2 --bucket \"bucket1\" --prefix \"file-\" --query \"(Contents[?LastModified>'2019-02-06T05:34:12.000Z'])[0]\"\nBut I want to do it from my code so please let me know how can I filter objects usin NPM AWS-SDK.\nNote: I can do it using exec or spawn but for this I have to configure profile using CLI which will create credential file on local so I dont want to do this.\n\nA: Use the AWS SDK for node.js. Call the listObjectsV2 method and then use jmespath.js in the callback method to filter the output of the API call. This is the equivalent of what the AWS CLI does via the --query param.\nSomething like this (untested)\nvar params = {\n  Bucket: \"bucket1\", \n  Prefix: \"file-\"\n };\n s3.listObjectsV2(params, function(err, data) {\n   if (err) console.log(err, err.stack); // an error occurred\n   else {\n       query = \"Contents[?LastModified>'2019-02-06T05:34:12.000Z']\"\n       var results = jmespath.search(data,query);\n   }\n };\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722266165.7978, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "cacdd0fd6204439b8b8081984a882bac", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "can myiasis kill a dog?", "0_output": [["can myiasis kill a dog?", "Stackoverflow Stackexchange\n\nQ: Visual C# backtrace: how to know where [external code] resides? I am using visual c# to debug a large c# project. The project is very large and only some of  the pdbs are build by myself. I can build all the pdb however it's very time consuming. I am debugging a backtrace but a part of the trace is marked with [External Code]. I know some pdb is missing, but how can I know which DLL the external code resides? In C I can get the EIP then see the process mapping to determine where the EIP lives, but how this can be done in .Net environment?\n\nA: Go into Tools | Options | Debugging | General, and uncheck the \"Enable Just My Code\" checkbox.  Now Visual Studio will show the full call stack (at least for managed code, you still may see some \"native to managed transitions\" in there).\n"]], "0_corpus": "stackexchange", "1_conv_id": "fed637e5f4d04a5fa833747983dfde2a", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "can myiasis kill a dog?", "1_output": [["can myiasis kill a dog?", "Gaming Stackexchange\n\nQ: Can My Pet Be Killed? Maybe I just haven't noticed, but it my pet never seems to take damage.  Can he be killed?  What happens if he can, and does die?\n\nA: No, your pet cannot die.  If your pet gets too low on life, they'll panic, run away and return when they recover.  They'll regenerate life slowly while fleeing, so if you wait a bit, they'll come back on their own.\nYou can speed the healing process by giving your pet a health potion.  I believe this requires that you shift-click or shift-hotkey (ie, if your health potion is 1, press Shift-1) a health potion.\nYou can also make your pet tougher by feeding it fish, so they don't take as much damage, and therefore don't run away as easily.  Different fish have different attributes and last for different amount of time, so experiment to find the one that works best for you, and then feed it that fish before/during a fight with a large boss or other dangerous area of the game.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722266265.9303, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "2c0c8080a45d4806a27966e78ac25d0a", "0_model_name": "voyage-multilingual-2", "0_prompt": "how long is one full rotation of the earth", "0_output": [["how long is one full rotation of the earth", "Earth's rotation\n\nThe apparent solar time is a measure of Earth's rotation and the difference between it and the mean solar time is known as the equation of time.\nStellar and sidereal day\nEarth's rotation period relative to the International Celestial Reference Frame, called its stellar day by the International Earth Rotation and Reference Systems Service (IERS), is  seconds of mean solar time (UT1) , ). Earth's rotation period relative to the precessing mean vernal equinox, named sidereal day, is  of mean solar time (UT1) , ). Thus, the sidereal day is shorter than the stellar day by about .\nBoth the stellar day and the sidereal day are shorter than the mean solar day by about  . This is a result of the Earth turning 1 additional rotation, relative to the celestial reference frame, as it orbits the Sun (so 366.24 rotations/y). The mean solar day in SI seconds is available from the IERS for the periods  and .\nRecently (1999\u20132010) the average annual length of the mean solar day in excess of 86,400 SI seconds has varied between  and , which must be added to both the stellar and sidereal days given in mean solar time above to obtain their lengths in SI seconds (see Fluctuations in the length of day)."]], "0_corpus": "wikipedia", "1_conv_id": "4a656424412845e18fdce95bcd7c5bc4", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "how long is one full rotation of the earth", "1_output": [["how long is one full rotation of the earth", "Earth's rotation\n\nEarth rotates once in about 24 hours with respect to the Sun, but once every 23 hours, 56 minutes and 4 seconds with respect to other distant stars (see below). Earth's rotation is slowing slightly with time; thus, a day was shorter in the past. This is due to the tidal effects the Moon has on Earth's rotation. Atomic clocks show that the modern day is longer by about 1.7 milliseconds than a century ago, slowly increasing the rate at which UTC is adjusted by leap seconds. Analysis of historical astronomical records shows a slowing trend; the length of a day increased by about 2.3 milliseconds per century since the 8th century BCE.\nScientists reported that in 2020 Earth had started spinning faster, after consistently spinning slower than 86,400 seconds per day in the decades before. On June 29, 2022, Earth's spin was completed in 1.59 milliseconds under 24 hours, setting a new record. Because of that trend, engineers worldwide are discussing a 'negative leap second' and other possible timekeeping measures.\nThis increase in speed is thought to be due to various factors, including the complex motion of its molten core, oceans, and atmosphere, the effect of celestial bodies such as the Moon, and possibly climate change, which is causing the ice at Earth's poles to melt. The masses of ice account for the Earth's shape being that of an oblate spheroid, bulging around the equator. When these masses are reduced, the poles rebound from the loss of weight, and Earth becomes more spherical, which has the effect of bringing mass closer to its centre of gravity. Conservation of angular momentum dictates that a mass distributed more closely around its centre of gravity spins faster."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722266358.0591, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "49e9a27906a64047b72faad280947b7b", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "why is padme so much older than anakin?", "0_output": [["why is padme so much older than anakin?", "Scifi Stackexchange\n\nQ: Is the dating of Anakin's fall and redemption/death relative to his age intentional? Anakin was born in 41.9 BBY. He had his 10th birthday a few days after Ep I ended, and 20th birthday a day before Ep II started. Moving forward, he fell to the dark side at the age of 23, and was redeemed and subsequently died 23 years later, at 46.\nIs this intended by design, in and out of universe, that he spent 23 years in the light and 23 years in the darkness? By the right of the G, by the will of the Force, was Anakin Skywalker specifically meant to live a life of balance - one part light, equal part dark - or is this just coincidence?\n"]], "0_corpus": "stackexchange", "1_conv_id": "90c19bc95b7343d281a4aab9bbe79f96", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "why is padme so much older than anakin?", "1_output": [["why is padme so much older than anakin?", "Stackoverflow Stackexchange\n\nQ: How to tell TypeScript my function argument is an Enum key? I have an enum thats maps HTTP status to their code, as follow:\n\nI can easily get those codes by accessing their keys (notice how IntelliSense shows HttpStatus.NOT_FOUND = 404 on the right panel)\n\nNow let's say I have a function called sendStatus:\nHow should I type this function in order to get those codes autocompleted by IntelliSense?\n\nUsing keyof typeof doesn't work because it doesn't autocomplete the enum values.\n\nA: You can test keyof typeof by running example like this, which shows that Enum itself works fine in your case.\nenum HttpStatus {\n    OK = 200,\n    CREATED = 201\n}\n\nfunction printStatus(code: keyof typeof HttpStatus) {\n    const num = HttpStatus[code];\n    if (num <= HttpStatus.CREATED) {\n       console.log('HTTP Status key is: ', code);\n       console.log('HTTP Status value is: ', num);\n    }\n}\n\nprintStatus(\"OK\");\n\n// Prints out\n// > HTTP Status key is:  \u2013 \"OK\"\n// > HTTP Status value is:  \u2013 200\n// Which shows that Enum value is being detected properly\n\nkeyof typeof gets you type that represents all Enum keys as string, and this is why autocompletion behaves like that.\nhttps://www.typescriptlang.org/docs/handbook/enums.html#enums-at-compile-time\n"]], "1_corpus": "stackexchange"}

data/retrieval_battle-cca49e2f-b827-45ae-b911-5f48dd8c5375.jsonl

@@ -1 +1 @@
-{"tstamp": 1722352956.1747, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "b561f14ea5fc4ec980d9def108b74c04", "0_model_name": "BM25", "0_prompt": "what is the difference between function and functionality?", "0_output": [["what is the difference between function and functionality?", "Math Stackexchange\n\nQ: What's the difference between \"relation\", \"mapping\", and \"function\"? I think that a mapping and function are the same; there's only a difference between a mapping and relation. But I'm confused. What's the difference between a relation and a mapping and a function? \n\nA: There is basically no difference between mapping and function. In algebra, one uses the notion of operation which is the same as mapping or function. The notion of relation is more general. Functions are specific relations (those which are left-total and right-unique). \n"]], "0_corpus": "stackexchange", "1_conv_id": "52861ba10bfa4eafa34c266b5978e46f", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "what is the difference between function and functionality?", "1_output": [["what is the difference between function and functionality?", "English Stackexchange\n\nQ: \"Functionalities\" vs \"features\" - what's the difference? How are those two words different? Are they considered synonyms, especially when describing computer software?\n\nA: \"It is important to remember the distinction between product functions and product features.  Functions are the “product’s answer to the set of user tasks”; features are the “user tools” inherent in the product used to perform the functions (Wood, 1995).  Placing a telephone call is a function; the dial tone and the touch-tone keypad are features used to accomplish the function.  Product Types have different sets of functions, and each Model within a Type accomplishes its functions through potentially different features.\" http://inclusive.com/mmr/findings/functions_and_features.htm\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722352956.1747, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "b561f14ea5fc4ec980d9def108b74c04", "0_model_name": "BM25", "0_prompt": "what is the difference between function and functionality?", "0_output": [["what is the difference between function and functionality?", "Math Stackexchange\n\nQ: What's the difference between \"relation\", \"mapping\", and \"function\"? I think that a mapping and function are the same; there's only a difference between a mapping and relation. But I'm confused. What's the difference between a relation and a mapping and a function? \n\nA: There is basically no difference between mapping and function. In algebra, one uses the notion of operation which is the same as mapping or function. The notion of relation is more general. Functions are specific relations (those which are left-total and right-unique). \n"]], "0_corpus": "stackexchange", "1_conv_id": "52861ba10bfa4eafa34c266b5978e46f", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "what is the difference between function and functionality?", "1_output": [["what is the difference between function and functionality?", "English Stackexchange\n\nQ: \"Functionalities\" vs \"features\" - what's the difference? How are those two words different? Are they considered synonyms, especially when describing computer software?\n\nA: \"It is important to remember the distinction between product functions and product features.  Functions are the \u201cproduct\u2019s answer to the set of user tasks\u201d; features are the \u201cuser tools\u201d inherent in the product used to perform the functions (Wood, 1995).  Placing a telephone call is a function; the dial tone and the touch-tone keypad are features used to accomplish the function.  Product Types have different sets of functions, and each Model within a Type accomplishes its functions through potentially different features.\" http://inclusive.com/mmr/findings/functions_and_features.htm\n"]], "1_corpus": "stackexchange"}

data/retrieval_battle-f06090e7-37d3-4ee2-a6a0-793612d9f05a.jsonl

@@ -1 +0,0 @@
-{"tstamp": 1722262765.3508, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "641a108b1ae44303af506a8b9b54f41d", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "do cron jobs run in parallel?", "0_output": [["do cron jobs run in parallel?", "Serverfault Stackexchange\n\nQ: Are Linux cron Jobs each executed sequentially or in parallel? Lets say I create two cron jobs:\n\n\n*\n\n*Cron Job A, runs every day at 8:00AM\n\n*Cron Job B, runs every day at 8:05AM\n\n\nHowever, Cron Job A, takes 12 hours to run (don't ask why, this is an example).\nWill Cron Job B get to run at 8:05AM or do cron jobs only get executed one at a time?\nIf a Cron Job that was previously running was interrupted by a System Restart/Reboot, will it resume/run again after subsequent boot?\n\nA: They are run in parallel.  Cron Job B will run at 8:05 am.\n"]], "0_corpus": "stackexchange", "1_conv_id": "c938c8da1fdc487ea6873094057a6a8d", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "do cron jobs run in parallel?", "1_output": [["do cron jobs run in parallel?", "Superuser Stackexchange\n\nQ: How do I get the Modern UI of Firefox in Windows 8? Mozilla mentions\n\nElm is the experimental repository where most of our Metro development work has been happening.\n\nNow, I grabbed the latest nightly from the Elm repo, installed it & then despite starting it from the start screen, I was still provided the desktop version.\nIs there a way I can get the Metro Modern UI of Firefox in Windows 8? Do I need to add some extra command line arguments or something?\n\nA: It doesn't quite work right yet. Using a shortcut to the executable (Might be in Program Files (x86))\n\"C:\\Program Files\\Nightly\\firefox.exe\" -metrodesktop\n\nwith that new parameter opens up the Metro version of Firefox Elm on the regular desktop.\nChances are this will be fixed in future builds\n\n"]], "1_corpus": "stackexchange"}

data/retrieval_individual-02ddbc43-8a77-4d21-b3a5-485d76c2003b.jsonl

@@ -1,4 +1,3 @@
 {"tstamp": 1722222959.9785, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722222943.427, "finish": 1722222959.9785, "ip": "", "conv_id": "776f02bd82f74b80aff4960db5817568", "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?", "Stackoverflow Stackexchange\n\nQ: Controller Testing with Minitest and Devise failing In a Rails 3.28 application using Devise, I setup a controller that checks whether a user is logged in or not with if user_signed_in?. Once the tests hit this line, I always get a undefined method 'authenticate!' for nil:NilClass error. I wanted to test some code in a scenario that no user is logged in.\nI am thinking that I need to setup the tests according to the options specified in the Devise Wiki here, however, all the instructions pertain to rspec.\nIn particular, I think I need to have the following to work in minitest:\nRSpec.configure do |config|\n  config.include Devise::TestHelpers, :type => :controller\n  config.extend ControllerMacros, :type => :controller\nend\n\nHas anyone succeded in setting this up to have Devise work with minitest?\n\nA: Add the following to your minitest_helper.rb file:\nclass MiniTest::Rails::ActionController::TestCase\n  include Devise::TestHelpers\nend\n\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722222959.9785, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722222943.427, "finish": 1722222959.9785, "ip": "", "conv_id": "d88e38d4fcfa46b08b4d9c9a414d77c7", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Stackoverflow Stackexchange\n\nQ: android - getting nullpointerexception for no specific reason I am working on an android project and as far as I know, I don't make any specific mistakes in my code that would lead to an exception. \nI get these two error lines everytime I run my project, but the rest of my code runs without any issues.\nThe NullPointerException doesn't have a concrete occurance location, which confuses me.\nI have already searched for similar issues. A possible solution was adding the READ_EXTERNAL_STORAGE and WRITE_EXTERNAL_STORAGE as uses-permission in my Android Manifest file, but to no avail.\n2020-02-28 XX:XX:XX.333 14615-14651/com.example.package_name E/Perf: Fail to get file list com.example.package_name\n2020-02-28 XX:XX:XX.333 14615-14651/com.example.package_name E/Perf: getFolderSize() : Exception_1 = java.lang.NullPointerException: Attempt to get length of null array\n\n\nA: The error was thrown by my OnePlus, on the android emulator there was no error shown\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722223229.0432, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722223205.3663, "finish": 1722223229.0432, "ip": "", "conv_id": "42ce372770374b24ad9d88378f058800", "model_name": "intfloat/e5-mistral-7b-instruct", "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": 1722223229.0432, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722223205.3663, "finish": 1722223229.0432, "ip": "", "conv_id": "4cce5456d4794da8bc4d8b54a94fa7a6", "model_name": "text-embedding-004", "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"}

data/retrieval_individual-0325b067-7cac-407c-9ffa-76d99f10c9f7.jsonl

@@ -1,4 +1,3 @@
-{"tstamp": 1722264262.1584, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722264252.9998, "finish": 1722264262.1584, "ip": "", "conv_id": "6f3fa217f298496b91e2bd977a06718d", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "What's the takeaway from Orwell's 1984?", "output": [["What's the takeaway from Orwell's 1984?", "Superuser Stackexchange\n\nQ: How do I create a mysql client docker container that executes a mysql script against another mysql server container? How do I create a mysql client docker container that executes a mysql script against another mysql server container? Which base container should I use?\n\nA: You can really use many different base images. Which one you use only changes how to install mysql client. \nYou could use debian like so \nFROM debian\nRUN apt-get update && \\\n    apt-get install -y mysql-client\nCMD mysql {mysql-client-options} < {sql.script}\n\nIf using alpine, you just change the install to use apk instead\nFROM alpine\nRUN apk update && \\\n    apk add mysql-client\nCMD mysql {mysql-client-options} < {sql.script}\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722264262.1584, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722264252.9998, "finish": 1722264262.1584, "ip": "", "conv_id": "8621f4660242448c8dbb4be0b492f5a9", "model_name": "BM25", "prompt": "What's the takeaway from Orwell's 1984?", "output": [["What's the takeaway from Orwell's 1984?", "English Stackexchange\n\nQ: Should I write Orwell's '1984' in full? Should I write 'Nineteen Eighty-Four', when discussing George Orwell's novel in an essay, or '1984'? Is it considered unconventional, or overly colloquial to use the latter form?\nThis question applies for any book title consisting solely of a number, although I can't call to mind any.\n\nA: Orwell actually called the book Nineteen Eighty-Four, but even that was transformed into nineteen eighty-four on the cover of the first edition \u2014 and the figure 1984 appeared as well.\n\nThere have been many editions, some featuring the title spelled out in words, others using the figures. The 1987 Penguin edition I have, first published by that house in 1954, spells out the title in words and features the original copyright attribution prominently, \"Copyright 1949 by Eric Blair. All rights reserved.\" Given that, it's unlikely that his estate would have given permission for Penguin not to use the original title in its original form, and it should be spelled out in full.\n\"1984first\" by George Orwell; published by Secker and Warburg (London) - Brown University Library. Image via Wikipedia\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722264292.7247, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722264276.8726, "finish": 1722264292.7247, "ip": "", "conv_id": "f7444779f1ef4a6bb23d8d4b89df45e2", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Brave New World Aldous Huxley", "output": [["Brave New World Aldous Huxley", "Stackoverflow Stackexchange\n\nQ: Spying on selfinjecting bean How to spy on a bean, that injects itself?\nSuppose we have\n@Service\npublic class SomeClass {\n    @Autowired\n    private SomeClass someClass;\n    ...\n}\n\nNow I want to use the following in my test:\n@SpyBean\nprivate SomeClass someClass;\n\nI'm getting the following error:\n\nBean with name 'someClass' has been injected into other beans [someClass] in its raw version as part of a circular reference, but has eventually been wrapped. This means that said other beans do not use the final version of the bean. This is often the result of over-eager type matching - consider using 'getBeanNamesOfType' with the 'allowEagerInit' flag turned off, for example.\n\nAlso without self injection tests run flawlessly.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722264292.7247, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722264276.8726, "finish": 1722264292.7247, "ip": "", "conv_id": "0f74f152c34f49fda75dbbe2bf6951eb", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Brave New World Aldous Huxley", "output": [["Brave New World Aldous Huxley", "Stackoverflow Stackexchange\n\nQ: DefaultParser in Apache Commons CLI I wanted to try out the Apache Commons CLI, and figured a good place to start would be the 'usage' section on its web page.\nhttp://commons.apache.org/proper/commons-cli/usage.html\nNow, the example suggest to create a DefaultParser, however the closest sounding I could find is BasicParser. Is this the thing to use, am I missing something?\n\nA: I used GnuParser() instead of DefaultParser() and it works well.\n\nCommandLineParser parser = new GnuParser();\n\nUpdate : In version 1.3.1 of CLI, GnuParser() is now deprecated. So I simply added import org.apache.commons.cli.DefaultParser;and now I use CommandLineParser parser = new DefaultParser();\nAnd all is fine!\n"]], "corpus": "stackexchange"}
@@ -33,13 +32,9 @@
 {"tstamp": 1722265140.3158, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722265140.2368, "finish": 1722265140.3158, "ip": "", "conv_id": "66d4608359274f3d9db9dc9f79ce74cc", "model_name": "BM25", "prompt": "In English when to use \u2013, \u2014 vs -? ", "output": [["In English when to use \u2013, \u2014 vs -? ", "English Stackexchange\n\nQ: When to use \"If I was\" vs. \"If I were\"? \n*\n\n*If I was...\n\n*If I were...\n\n\nWhen is it correct to use \"If I was\" vs. \"If I were\" in standard English?\n\nA: When in doubt, always use the subjunctive mood:\n\nIf I were you...\n\nIt will make you sound smarter and it is technically correct since \"the subjunctive mood is used to express a wish or possible situation that is currently not true.\"\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722265168.9008, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722265168.8863, "finish": 1722265168.9008, "ip": "", "conv_id": "c159835f280040668ab4b8da2bc0186b", "model_name": "BM25", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use \"If I was\" vs. \"If I were\"? \n*\n\n*If I was...\n\n*If I were...\n\n\nWhen is it correct to use \"If I was\" vs. \"If I were\" in standard English?\n\nA: When in doubt, always use the subjunctive mood:\n\nIf I were you...\n\nIt will make you sound smarter and it is technically correct since \"the subjunctive mood is used to express a wish or possible situation that is currently not true.\"\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722265180.7036, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722265178.5517, "finish": 1722265180.7036, "ip": "", "conv_id": "1ea9d7c8b1c44c2ba8dcad8d187ced07", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use -, \u2013 and \u2014? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (\u2013) and em-dashes (\u2014).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London\u2013Amsterdam flight is delayed.\n\n*The meeting is from 3:00\u20134:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire\u2014it had been awake half the night\u2014flew out of its coffin.\n\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722265180.7036, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722265178.5517, "finish": 1722265180.7036, "ip": "", "conv_id": "35c5ee02a1934fa2abbc55634c12bc8f", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "Serverfault Stackexchange\n\nQ: Xdebug Profiler Aggregate always crashes (xdebug.profiler_aggregate=1 causes coredump in xdebug.so xdebug_profiler_output_aggr_data()) Whenever I go to localhost I get a 404 error, until I mark xdebug.profiler_aggregate=0\nThe reason why is xdebug.so coredumps (SEGFAULT)\n[Xdebug]\nzend_extension=/usr/lib/php/20160303/xdebug.so\nxdebug.remote_enable=1\nxdebug.remote_connect_back = 0\nxdebug.remote_handler = \"dbgp\"\nxdebug.remote_port = 9000\nxdebug.idekey = \"PHPSTORM\"\nxdebug.profiler_enable=1\nxdebug.profiler_output_dir=/tmp/\nxdebug.profiler_aggregate=1\n\n$ dmesg\n[  584.455746] php-fpm7.1[4645]: segfault at 30 ip 00007f5d3969f6b3 sp 00007ffdc8b87a20 error 4 in xdebug.so[7f5d3967e000+39000]\n\n$\u00a0sudo cat /var/log/php7.1-fpm.log\n[31-Aug-2017 14:59:01] WARNING: [pool www] child 4645 exited on signal 11 (SIGSEGV - core dumped) after 0.138218 seconds from start\n[31-Aug-2017 14:59:01] NOTICE: [pool www] child 4647 started\n[31-Aug-2017 14:59:07] NOTICE: Terminating ...\n[31-Aug-2017 14:59:07] NOTICE: exiting, bye-bye!\nin xdebug_profiler_output_aggr_data() with 0 entries\n\nWhen I change the last line in the conf file to\nxdebug.profiler_aggregate=0\nit works fine again.\nI've tried reinstalling php, xdebug, and updating and restarting, and I've tried removing all other xdebug settings except for that one\n\nA: I don't know how you have installed Xdebug, but I suggest you to install a recent version of Xdebug, with pecl, not with apt (if you have apt).\nsudo apt-get remove php-xdebug\nsudo apt install php-pear php-dev\nsudo pecl install xdebug\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722265194.9804, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722265192.8285, "finish": 1722265194.9804, "ip": "", "conv_id": "e5ec831cc92841dba4c8be52734fcfa1", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use -, \u2013 and \u2014? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (\u2013) and em-dashes (\u2014).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London\u2013Amsterdam flight is delayed.\n\n*The meeting is from 3:00\u20134:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire\u2014it had been awake half the night\u2014flew out of its coffin.\n\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722265194.9804, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722265192.8285, "finish": 1722265194.9804, "ip": "", "conv_id": "932248db7f9240a392b0995752bec422", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "Serverfault Stackexchange\n\nQ: Xdebug Profiler Aggregate always crashes (xdebug.profiler_aggregate=1 causes coredump in xdebug.so xdebug_profiler_output_aggr_data()) Whenever I go to localhost I get a 404 error, until I mark xdebug.profiler_aggregate=0\nThe reason why is xdebug.so coredumps (SEGFAULT)\n[Xdebug]\nzend_extension=/usr/lib/php/20160303/xdebug.so\nxdebug.remote_enable=1\nxdebug.remote_connect_back = 0\nxdebug.remote_handler = \"dbgp\"\nxdebug.remote_port = 9000\nxdebug.idekey = \"PHPSTORM\"\nxdebug.profiler_enable=1\nxdebug.profiler_output_dir=/tmp/\nxdebug.profiler_aggregate=1\n\n$ dmesg\n[  584.455746] php-fpm7.1[4645]: segfault at 30 ip 00007f5d3969f6b3 sp 00007ffdc8b87a20 error 4 in xdebug.so[7f5d3967e000+39000]\n\n$\u00a0sudo cat /var/log/php7.1-fpm.log\n[31-Aug-2017 14:59:01] WARNING: [pool www] child 4645 exited on signal 11 (SIGSEGV - core dumped) after 0.138218 seconds from start\n[31-Aug-2017 14:59:01] NOTICE: [pool www] child 4647 started\n[31-Aug-2017 14:59:07] NOTICE: Terminating ...\n[31-Aug-2017 14:59:07] NOTICE: exiting, bye-bye!\nin xdebug_profiler_output_aggr_data() with 0 entries\n\nWhen I change the last line in the conf file to\nxdebug.profiler_aggregate=0\nit works fine again.\nI've tried reinstalling php, xdebug, and updating and restarting, and I've tried removing all other xdebug settings except for that one\n\nA: I don't know how you have installed Xdebug, but I suggest you to install a recent version of Xdebug, with pecl, not with apt (if you have apt).\nsudo apt-get remove php-xdebug\nsudo apt install php-pear php-dev\nsudo pecl install xdebug\n\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722265214.8517, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722265214.7218, "finish": 1722265214.8517, "ip": "", "conv_id": "8e8b4d339c2c40c39b9b0e23e505899e", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "Serverfault Stackexchange\n\nQ: Xdebug Profiler Aggregate always crashes (xdebug.profiler_aggregate=1 causes coredump in xdebug.so xdebug_profiler_output_aggr_data()) Whenever I go to localhost I get a 404 error, until I mark xdebug.profiler_aggregate=0\nThe reason why is xdebug.so coredumps (SEGFAULT)\n[Xdebug]\nzend_extension=/usr/lib/php/20160303/xdebug.so\nxdebug.remote_enable=1\nxdebug.remote_connect_back = 0\nxdebug.remote_handler = \"dbgp\"\nxdebug.remote_port = 9000\nxdebug.idekey = \"PHPSTORM\"\nxdebug.profiler_enable=1\nxdebug.profiler_output_dir=/tmp/\nxdebug.profiler_aggregate=1\n\n$ dmesg\n[  584.455746] php-fpm7.1[4645]: segfault at 30 ip 00007f5d3969f6b3 sp 00007ffdc8b87a20 error 4 in xdebug.so[7f5d3967e000+39000]\n\n$\u00a0sudo cat /var/log/php7.1-fpm.log\n[31-Aug-2017 14:59:01] WARNING: [pool www] child 4645 exited on signal 11 (SIGSEGV - core dumped) after 0.138218 seconds from start\n[31-Aug-2017 14:59:01] NOTICE: [pool www] child 4647 started\n[31-Aug-2017 14:59:07] NOTICE: Terminating ...\n[31-Aug-2017 14:59:07] NOTICE: exiting, bye-bye!\nin xdebug_profiler_output_aggr_data() with 0 entries\n\nWhen I change the last line in the conf file to\nxdebug.profiler_aggregate=0\nit works fine again.\nI've tried reinstalling php, xdebug, and updating and restarting, and I've tried removing all other xdebug settings except for that one\n\nA: I don't know how you have installed Xdebug, but I suggest you to install a recent version of Xdebug, with pecl, not with apt (if you have apt).\nsudo apt-get remove php-xdebug\nsudo apt install php-pear php-dev\nsudo pecl install xdebug\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722265214.8517, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722265214.7218, "finish": 1722265214.8517, "ip": "", "conv_id": "11a7b09091d546a0a456f974b24cc0ec", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use -, \u2013 and \u2014? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (\u2013) and em-dashes (\u2014).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London\u2013Amsterdam flight is delayed.\n\n*The meeting is from 3:00\u20134:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire\u2014it had been awake half the night\u2014flew out of its coffin.\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722265221.0683, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722265220.9843, "finish": 1722265221.0683, "ip": "", "conv_id": "ad89106dd2ac429195138135d84158f9", "model_name": "BM25", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use \"If I was\" vs. \"If I were\"? \n*\n\n*If I was...\n\n*If I were...\n\n\nWhen is it correct to use \"If I was\" vs. \"If I were\" in standard English?\n\nA: When in doubt, always use the subjunctive mood:\n\nIf I were you...\n\nIt will make you sound smarter and it is technically correct since \"the subjunctive mood is used to express a wish or possible situation that is currently not true.\"\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722265221.0683, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722265220.9843, "finish": 1722265221.0683, "ip": "", "conv_id": "3beaa950d23844ba9d09916b299805f5", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use -, \u2013 and \u2014? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (\u2013) and em-dashes (\u2014).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London\u2013Amsterdam flight is delayed.\n\n*The meeting is from 3:00\u20134:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire\u2014it had been awake half the night\u2014flew out of its coffin.\n\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722265227.8817, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722265227.7551, "finish": 1722265227.8817, "ip": "", "conv_id": "1277d2d9c15847a0a8ee99fbe5573d0d", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "Serverfault Stackexchange\n\nQ: Xdebug Profiler Aggregate always crashes (xdebug.profiler_aggregate=1 causes coredump in xdebug.so xdebug_profiler_output_aggr_data()) Whenever I go to localhost I get a 404 error, until I mark xdebug.profiler_aggregate=0\nThe reason why is xdebug.so coredumps (SEGFAULT)\n[Xdebug]\nzend_extension=/usr/lib/php/20160303/xdebug.so\nxdebug.remote_enable=1\nxdebug.remote_connect_back = 0\nxdebug.remote_handler = \"dbgp\"\nxdebug.remote_port = 9000\nxdebug.idekey = \"PHPSTORM\"\nxdebug.profiler_enable=1\nxdebug.profiler_output_dir=/tmp/\nxdebug.profiler_aggregate=1\n\n$ dmesg\n[  584.455746] php-fpm7.1[4645]: segfault at 30 ip 00007f5d3969f6b3 sp 00007ffdc8b87a20 error 4 in xdebug.so[7f5d3967e000+39000]\n\n$\u00a0sudo cat /var/log/php7.1-fpm.log\n[31-Aug-2017 14:59:01] WARNING: [pool www] child 4645 exited on signal 11 (SIGSEGV - core dumped) after 0.138218 seconds from start\n[31-Aug-2017 14:59:01] NOTICE: [pool www] child 4647 started\n[31-Aug-2017 14:59:07] NOTICE: Terminating ...\n[31-Aug-2017 14:59:07] NOTICE: exiting, bye-bye!\nin xdebug_profiler_output_aggr_data() with 0 entries\n\nWhen I change the last line in the conf file to\nxdebug.profiler_aggregate=0\nit works fine again.\nI've tried reinstalling php, xdebug, and updating and restarting, and I've tried removing all other xdebug settings except for that one\n\nA: I don't know how you have installed Xdebug, but I suggest you to install a recent version of Xdebug, with pecl, not with apt (if you have apt).\nsudo apt-get remove php-xdebug\nsudo apt install php-pear php-dev\nsudo pecl install xdebug\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722265227.8817, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722265227.7551, "finish": 1722265227.8817, "ip": "", "conv_id": "8215081572f64d86b34945865ce9b2f8", "model_name": "BM25", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use \"If I was\" vs. \"If I were\"? \n*\n\n*If I was...\n\n*If I were...\n\n\nWhen is it correct to use \"If I was\" vs. \"If I were\" in standard English?\n\nA: When in doubt, always use the subjunctive mood:\n\nIf I were you...\n\nIt will make you sound smarter and it is technically correct since \"the subjunctive mood is used to express a wish or possible situation that is currently not true.\"\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722265241.1287, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722265241.0328, "finish": 1722265241.1287, "ip": "", "conv_id": "e319e57eccb14b7390720cfd4c411472", "model_name": "GritLM/GritLM-7B", "prompt": "In English when to use \u2013, \u2014 vs -?", "output": [["In English when to use \u2013, \u2014 vs -?", "English Stackexchange\n\nQ: When to use -, \u2013 and \u2014? \nPossible Duplicate:\nWhen should I use an em-dash, an en-dash, and a hyphen? \n\nThis is about hyphens (-), en-dashes (\u2013) and em-dashes (\u2014).\nWhen to use which one? To be honest, I always use em-dashes unless I join words with a hyphen, but I never use an en-dash.\n\nA: Hyphens are used in compound modifiers.\n\n\n*\n\n*a well-trained professional\n\n*over-the-counter drugs\n\n\nEn dashes are used in place of \"to\" to connect numbers or words.\n\n\n*\n\n*The London\u2013Amsterdam flight is delayed.\n\n*The meeting is from 3:00\u20134:30 p.m.\n\n\nEm dashes help set off amplifying or explanatory statements.\n\n\n*\n\n*The vampire\u2014it had been awake half the night\u2014flew out of its coffin.\n\n"]], "corpus": "stackexchange"}

data/retrieval_individual-1fa504af-967a-4ff8-a6ff-4abf5fbdd1c5.jsonl

@@ -5,4 +5,3 @@
 {"tstamp": 1722266208.7762, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722266172.5065, "finish": 1722266208.7762, "ip": "", "conv_id": "2c0c8080a45d4806a27966e78ac25d0a", "model_name": "voyage-multilingual-2", "prompt": "how long is one full rotation of the earth", "output": [["how long is one full rotation of the earth", "Earth's rotation\n\nThe apparent solar time is a measure of Earth's rotation and the difference between it and the mean solar time is known as the equation of time.\nStellar and sidereal day\nEarth's rotation period relative to the International Celestial Reference Frame, called its stellar day by the International Earth Rotation and Reference Systems Service (IERS), is  seconds of mean solar time (UT1) , ). Earth's rotation period relative to the precessing mean vernal equinox, named sidereal day, is  of mean solar time (UT1) , ). Thus, the sidereal day is shorter than the stellar day by about .\nBoth the stellar day and the sidereal day are shorter than the mean solar day by about  . This is a result of the Earth turning 1 additional rotation, relative to the celestial reference frame, as it orbits the Sun (so 366.24 rotations/y). The mean solar day in SI seconds is available from the IERS for the periods  and .\nRecently (1999\u20132010) the average annual length of the mean solar day in excess of 86,400 SI seconds has varied between  and , which must be added to both the stellar and sidereal days given in mean solar time above to obtain their lengths in SI seconds (see Fluctuations in the length of day)."]], "corpus": "wikipedia"}
 {"tstamp": 1722266208.7762, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722266172.5065, "finish": 1722266208.7762, "ip": "", "conv_id": "4a656424412845e18fdce95bcd7c5bc4", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "how long is one full rotation of the earth", "output": [["how long is one full rotation of the earth", "Earth's rotation\n\nEarth rotates once in about 24 hours with respect to the Sun, but once every 23 hours, 56 minutes and 4 seconds with respect to other distant stars (see below). Earth's rotation is slowing slightly with time; thus, a day was shorter in the past. This is due to the tidal effects the Moon has on Earth's rotation. Atomic clocks show that the modern day is longer by about 1.7 milliseconds than a century ago, slowly increasing the rate at which UTC is adjusted by leap seconds. Analysis of historical astronomical records shows a slowing trend; the length of a day increased by about 2.3 milliseconds per century since the 8th century BCE.\nScientists reported that in 2020 Earth had started spinning faster, after consistently spinning slower than 86,400 seconds per day in the decades before. On June 29, 2022, Earth's spin was completed in 1.59 milliseconds under 24 hours, setting a new record. Because of that trend, engineers worldwide are discussing a 'negative leap second' and other possible timekeeping measures.\nThis increase in speed is thought to be due to various factors, including the complex motion of its molten core, oceans, and atmosphere, the effect of celestial bodies such as the Moon, and possibly climate change, which is causing the ice at Earth's poles to melt. The masses of ice account for the Earth's shape being that of an oblate spheroid, bulging around the equator. When these masses are reduced, the poles rebound from the loss of weight, and Earth becomes more spherical, which has the effect of bringing mass closer to its centre of gravity. Conservation of angular momentum dictates that a mass distributed more closely around its centre of gravity spins faster."]], "corpus": "wikipedia"}
 {"tstamp": 1722266312.76, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722266303.0446, "finish": 1722266312.76, "ip": "", "conv_id": "49e9a27906a64047b72faad280947b7b", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "why is padme so much older than anakin?", "output": [["why is padme so much older than anakin?", "Scifi Stackexchange\n\nQ: Is the dating of Anakin's fall and redemption/death relative to his age intentional? Anakin was born in 41.9 BBY. He had his 10th birthday a few days after Ep I ended, and 20th birthday a day before Ep II started. Moving forward, he fell to the dark side at the age of 23, and was redeemed and subsequently died 23 years later, at 46.\nIs this intended by design, in and out of universe, that he spent 23 years in the light and 23 years in the darkness? By the right of the G, by the will of the Force, was Anakin Skywalker specifically meant to live a life of balance - one part light, equal part dark - or is this just coincidence?\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722266312.76, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722266303.0446, "finish": 1722266312.76, "ip": "", "conv_id": "90c19bc95b7343d281a4aab9bbe79f96", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "why is padme so much older than anakin?", "output": [["why is padme so much older than anakin?", "Stackoverflow Stackexchange\n\nQ: How to tell TypeScript my function argument is an Enum key? I have an enum thats maps HTTP status to their code, as follow:\n\nI can easily get those codes by accessing their keys (notice how IntelliSense shows HttpStatus.NOT_FOUND = 404 on the right panel)\n\nNow let's say I have a function called sendStatus:\nHow should I type this function in order to get those codes autocompleted by IntelliSense?\n\nUsing keyof typeof doesn't work because it doesn't autocomplete the enum values.\n\nA: You can test keyof typeof by running example like this, which shows that Enum itself works fine in your case.\nenum HttpStatus {\n    OK = 200,\n    CREATED = 201\n}\n\nfunction printStatus(code: keyof typeof HttpStatus) {\n    const num = HttpStatus[code];\n    if (num <= HttpStatus.CREATED) {\n       console.log('HTTP Status key is: ', code);\n       console.log('HTTP Status value is: ', num);\n    }\n}\n\nprintStatus(\"OK\");\n\n// Prints out\n// > HTTP Status key is:  \u2013 \"OK\"\n// > HTTP Status value is:  \u2013 200\n// Which shows that Enum value is being detected properly\n\nkeyof typeof gets you type that represents all Enum keys as string, and this is why autocompletion behaves like that.\nhttps://www.typescriptlang.org/docs/handbook/enums.html#enums-at-compile-time\n"]], "corpus": "stackexchange"}

data/retrieval_individual-cca49e2f-b827-45ae-b911-5f48dd8c5375.jsonl

@@ -1,6 +1,6 @@
 {"tstamp": 1722352894.0744, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722352893.4025, "finish": 1722352894.0744, "ip": "", "conv_id": "cb55c654f83a45569aba4269b8509b62", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "why tilt shift look miniature?", "output": [["why tilt shift look miniature?", "Apple Stackexchange\n\nQ: What's the benefit of using shift (for slow animation) when minimizing windows? I came across this today by accident:\nIf you try to minimise a window in OS X 10.8 (might be in others) while holding down the shift key the window will very slowly go into the Dock. The whole animation is slowed.\nAny ideas what the purpose of this is?\n\nA: Steve Jobs shows this off the first time he is showing OS X, at Macworld 2000.  He mentions that the effect was designed for that one demonstration.  It's amazing that it is still around 14 years later.\nhttps://youtu.be/AcGzCIlz8y0?t=6478\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722352894.0744, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722352893.4025, "finish": 1722352894.0744, "ip": "", "conv_id": "dff85719ae0044879505fca6c7c60643", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "why tilt shift look miniature?", "output": [["why tilt shift look miniature?", "Stackoverflow Stackexchange\n\nQ: Select top and bottom rows I'm using SQL Server 2005 and I'm trying to achieve something like this:\nI want to get the first x rows and the last x rows in the same select statement.\nSELECT TOP(5) BOTTOM(5)\n\nOf course BOTTOM does not exist, so I need another solution.  I believe there is an easy and elegant solution that I'm not getting.  Doing the select again with GROUP BY DESC is not an option.\n\nA: I think you've two main options:\nSELECT TOP 5 ...\nFROM ...\nORDER BY ... ASC\n\nUNION\n\nSELECT TOP 5 ...\nFROM ...\nORDER BY ... DESC\n\nOr, if you know how many items there are in the table:\nSELECT ...\nFROM (\n    SELECT ..., ROW_NUMBER() OVER (ORDER BY ... ASC) AS intRow\n    FROM ...\n) AS T\nWHERE intRow BETWEEN 1 AND 5 OR intRow BETWEEN @Number - 5 AND @Number\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722352937.4095, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722352936.8908, "finish": 1722352937.4095, "ip": "", "conv_id": "b561f14ea5fc4ec980d9def108b74c04", "model_name": "BM25", "prompt": "what is the difference between function and functionality?", "output": [["what is the difference between function and functionality?", "Math Stackexchange\n\nQ: What's the difference between \"relation\", \"mapping\", and \"function\"? I think that a mapping and function are the same; there's only a difference between a mapping and relation. But I'm confused. What's the difference between a relation and a mapping and a function? \n\nA: There is basically no difference between mapping and function. In algebra, one uses the notion of operation which is the same as mapping or function. The notion of relation is more general. Functions are specific relations (those which are left-total and right-unique). \n"]], "corpus": "stackexchange"}
-{"tstamp": 1722352937.4095, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722352936.8908, "finish": 1722352937.4095, "ip": "", "conv_id": "52861ba10bfa4eafa34c266b5978e46f", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "what is the difference between function and functionality?", "output": [["what is the difference between function and functionality?", "English Stackexchange\n\nQ: \"Functionalities\" vs \"features\" - what's the difference? How are those two words different? Are they considered synonyms, especially when describing computer software?\n\nA: \"It is important to remember the distinction between product functions and product features.  Functions are the “product’s answer to the set of user tasks”; features are the “user tools” inherent in the product used to perform the functions (Wood, 1995).  Placing a telephone call is a function; the dial tone and the touch-tone keypad are features used to accomplish the function.  Product Types have different sets of functions, and each Model within a Type accomplishes its functions through potentially different features.\" http://inclusive.com/mmr/findings/functions_and_features.htm\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722352980.3758, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722352980.2131, "finish": 1722352980.3758, "ip": "", "conv_id": "f5b211ac97f2492c968d415604a576d6", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "are kakashi and obito the same age?", "output": [["are kakashi and obito the same age?", "Scifi Stackexchange\n\nQ: Why didn't Obito turn blind? I'm curious to learn why Obito didn't turn blind when he used the Mangekyō Sharingan?\n\n\nA: It was because he was injected with Hashirama's cells.\nBackground: Madara, defeated by Hashirama Senju, took a big bite from Hashirama before he died. However, he implanted Izanami to one of his mangekyou sharingan to alter reality, such that he was back alive. He used Hashirama's cells from the chunk he bit off to cultivated more cells and treated himself with those cells to elongate his lifespan.\nMadara found Obito after he was crushed under a boulder and treated him with cells he cultivated from Hashirama Senju. Apparently, those cells not only elongate one's lifespan and heal very fast, but also suppress the side effects of excessive mangekyou sharingan use.\nThis may be one of the several reasons:\n\n\n*\n\n*Hashirama Senju was most famous for his healing jutsus. Any wound he received healed seemingly effortlessly.\n\n*Hashirama Senju was the reincarnation of Asura. \n\n*Hashirama Senju could use the wood release, the only release that can create life.\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722352980.3758, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722352980.2131, "finish": 1722352980.3758, "ip": "", "conv_id": "1612a46e45f94abe9864f631e35b9470", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "are kakashi and obito the same age?", "output": [["are kakashi and obito the same age?", "Scifi Stackexchange\n\nQ: Why didn't Obito turn blind? I'm curious to learn why Obito didn't turn blind when he used the Mangekyō Sharingan?\n\n\nA: It was because he was injected with Hashirama's cells.\nBackground: Madara, defeated by Hashirama Senju, took a big bite from Hashirama before he died. However, he implanted Izanami to one of his mangekyou sharingan to alter reality, such that he was back alive. He used Hashirama's cells from the chunk he bit off to cultivated more cells and treated himself with those cells to elongate his lifespan.\nMadara found Obito after he was crushed under a boulder and treated him with cells he cultivated from Hashirama Senju. Apparently, those cells not only elongate one's lifespan and heal very fast, but also suppress the side effects of excessive mangekyou sharingan use.\nThis may be one of the several reasons:\n\n\n*\n\n*Hashirama Senju was most famous for his healing jutsus. Any wound he received healed seemingly effortlessly.\n\n*Hashirama Senju was the reincarnation of Asura. \n\n*Hashirama Senju could use the wood release, the only release that can create life.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722352937.4095, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722352936.8908, "finish": 1722352937.4095, "ip": "", "conv_id": "52861ba10bfa4eafa34c266b5978e46f", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "what is the difference between function and functionality?", "output": [["what is the difference between function and functionality?", "English Stackexchange\n\nQ: \"Functionalities\" vs \"features\" - what's the difference? How are those two words different? Are they considered synonyms, especially when describing computer software?\n\nA: \"It is important to remember the distinction between product functions and product features.  Functions are the \u201cproduct\u2019s answer to the set of user tasks\u201d; features are the \u201cuser tools\u201d inherent in the product used to perform the functions (Wood, 1995).  Placing a telephone call is a function; the dial tone and the touch-tone keypad are features used to accomplish the function.  Product Types have different sets of functions, and each Model within a Type accomplishes its functions through potentially different features.\" http://inclusive.com/mmr/findings/functions_and_features.htm\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722352980.3758, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722352980.2131, "finish": 1722352980.3758, "ip": "", "conv_id": "f5b211ac97f2492c968d415604a576d6", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "are kakashi and obito the same age?", "output": [["are kakashi and obito the same age?", "Scifi Stackexchange\n\nQ: Why didn't Obito turn blind? I'm curious to learn why Obito didn't turn blind when he used the Mangeky\u014d Sharingan?\n\n\nA: It was because he was injected with Hashirama's cells.\nBackground: Madara, defeated by Hashirama Senju, took a big bite from Hashirama before he died. However, he implanted Izanami to one of his mangekyou sharingan to alter reality, such that he was back alive. He used Hashirama's cells from the chunk he bit off to cultivated more cells and treated himself with those cells to elongate his lifespan.\nMadara found Obito after he was crushed under a boulder and treated him with cells he cultivated from Hashirama Senju. Apparently, those cells not only elongate one's lifespan and heal very fast, but also suppress the side effects of excessive mangekyou sharingan use.\nThis may be one of the several reasons:\n\n\n*\n\n*Hashirama Senju was most famous for his healing jutsus. Any wound he received healed seemingly effortlessly.\n\n*Hashirama Senju was the reincarnation of Asura. \n\n*Hashirama Senju could use the wood release, the only release that can create life.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722352980.3758, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722352980.2131, "finish": 1722352980.3758, "ip": "", "conv_id": "1612a46e45f94abe9864f631e35b9470", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "are kakashi and obito the same age?", "output": [["are kakashi and obito the same age?", "Scifi Stackexchange\n\nQ: Why didn't Obito turn blind? I'm curious to learn why Obito didn't turn blind when he used the Mangeky\u014d Sharingan?\n\n\nA: It was because he was injected with Hashirama's cells.\nBackground: Madara, defeated by Hashirama Senju, took a big bite from Hashirama before he died. However, he implanted Izanami to one of his mangekyou sharingan to alter reality, such that he was back alive. He used Hashirama's cells from the chunk he bit off to cultivated more cells and treated himself with those cells to elongate his lifespan.\nMadara found Obito after he was crushed under a boulder and treated him with cells he cultivated from Hashirama Senju. Apparently, those cells not only elongate one's lifespan and heal very fast, but also suppress the side effects of excessive mangekyou sharingan use.\nThis may be one of the several reasons:\n\n\n*\n\n*Hashirama Senju was most famous for his healing jutsus. Any wound he received healed seemingly effortlessly.\n\n*Hashirama Senju was the reincarnation of Asura. \n\n*Hashirama Senju could use the wood release, the only release that can create life.\n"]], "corpus": "stackexchange"}

data/retrieval_individual-f06090e7-37d3-4ee2-a6a0-793612d9f05a.jsonl

@@ -1,2 +1 @@
 {"tstamp": 1722262755.7136, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722262736.9496, "finish": 1722262755.7136, "ip": "", "conv_id": "641a108b1ae44303af506a8b9b54f41d", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "do cron jobs run in parallel?", "output": [["do cron jobs run in parallel?", "Serverfault Stackexchange\n\nQ: Are Linux cron Jobs each executed sequentially or in parallel? Lets say I create two cron jobs:\n\n\n*\n\n*Cron Job A, runs every day at 8:00AM\n\n*Cron Job B, runs every day at 8:05AM\n\n\nHowever, Cron Job A, takes 12 hours to run (don't ask why, this is an example).\nWill Cron Job B get to run at 8:05AM or do cron jobs only get executed one at a time?\nIf a Cron Job that was previously running was interrupted by a System Restart/Reboot, will it resume/run again after subsequent boot?\n\nA: They are run in parallel.  Cron Job B will run at 8:05 am.\n"]], "corpus": "stackexchange"}
-{"tstamp": 1722262755.7136, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722262736.9496, "finish": 1722262755.7136, "ip": "", "conv_id": "c938c8da1fdc487ea6873094057a6a8d", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "do cron jobs run in parallel?", "output": [["do cron jobs run in parallel?", "Superuser Stackexchange\n\nQ: How do I get the Modern UI of Firefox in Windows 8? Mozilla mentions\n\nElm is the experimental repository where most of our Metro development work has been happening.\n\nNow, I grabbed the latest nightly from the Elm repo, installed it & then despite starting it from the start screen, I was still provided the desktop version.\nIs there a way I can get the Metro Modern UI of Firefox in Windows 8? Do I need to add some extra command line arguments or something?\n\nA: It doesn't quite work right yet. Using a shortcut to the executable (Might be in Program Files (x86))\n\"C:\\Program Files\\Nightly\\firefox.exe\" -metrodesktop\n\nwith that new parameter opens up the Metro version of Firefox Elm on the regular desktop.\nChances are this will be fixed in future builds\n\n"]], "corpus": "stackexchange"}

data/retrieval_side_by_side-81269da8-6dc7-417a-8e0a-245c4a753482.jsonl

@@ -1,2 +1,2 @@
-{"tstamp": 1722386001.9562, "task_type": "retrieval", "type": "tievote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "2987867b0d714ee2b3991f314601f180", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "how to write a good abstract", "0_output": [["how to write a good abstract", "Abstract (summary)\n\nThe use of abstracts to summarise science originates in the early 1800s, when the secretary of the Royal Society would record brief summaries of talks into the minutes of each meeting, which were referred to as 'abstracts'. The Royal Society abstracts from 1800 – 1837 were later collated and published in the society's journal Philosophical Transactions, with the first group appearing in 1832. These abstracts were generally one or more pages long. Other learned societies adopted similar practices. The Royal Astronomical Society (RAS) may have been the first to publish its abstracts: the Monthly Notices of the RAS launched in 1827, containing (among other things) abstracts of talks given at their monthly meetings; the full papers were published months or years later in the Memoirs of the RAS. The RAS abstracts were between one and three paragraphs long. In both cases, these early abstracts were written by the learned society, not the author of the paper. Perhaps the earliest example of an abstract published alongside the paper it summarises was the 1919 paper On the Irregularities of Motion of the Foucault Pendulum published in the Physical Review of the American Physical Society, which often published abstracts thereafter.\nCopyright\nAbstracts are protected under copyright law just as any other form of written speech is protected.\nStructure\nAbstract is often expected to tell a complete story of the paper, as for most readers, abstract is the only part of the paper that will be read. It should allow the reader to give an elevator pitch of the full paper.\nAn academic abstract typically outlines four elements relevant to the completed work:\nThe research focus (statement of the problem(s)/specific gap in existing research/research issue(s) addressed);\nThe research methods (experimental research, case studies,  questionnaires, etc) used to solve the problem;\nThe major results/findings of the research; and\nThe main conclusions and recommendations (i.e., how the work answers the proposed research problem).\nIt may also contain brief references, although some publications' standard style omits references from the abstract, reserving them for the article body (which, by definition, treats the same topics but in more depth)."]], "0_corpus": "wikipedia", "1_conv_id": "13aca6a147054459a7ad0f23455c0359", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "how to write a good abstract", "1_output": [["how to write a good abstract", "Abstract (summary)\n\nAn abstract is a brief summary of a research article, thesis, review, conference proceeding, or any in-depth analysis of a particular subject and is often used to help the reader quickly ascertain the paper's purpose. When used, an abstract always appears at the beginning of a manuscript or typescript, acting as the point-of-entry for any given academic paper or patent application.  Abstracting and indexing services for various academic disciplines are aimed at compiling a body of literature for that particular subject."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722386001.9562, "task_type": "retrieval", "type": "tievote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "2987867b0d714ee2b3991f314601f180", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "how to write a good abstract", "0_output": [["how to write a good abstract", "Abstract (summary)\n\nThe use of abstracts to summarise science originates in the early 1800s, when the secretary of the Royal Society would record brief summaries of talks into the minutes of each meeting, which were referred to as 'abstracts'. The Royal Society abstracts from 1800 \u2013 1837 were later collated and published in the society's journal Philosophical Transactions, with the first group appearing in 1832. These abstracts were generally one or more pages long. Other learned societies adopted similar practices. The Royal Astronomical Society (RAS) may have been the first to publish its abstracts: the Monthly Notices of the RAS launched in 1827, containing (among other things) abstracts of talks given at their monthly meetings; the full papers were published months or years later in the Memoirs of the RAS. The RAS abstracts were between one and three paragraphs long. In both cases, these early abstracts were written by the learned society, not the author of the paper. Perhaps the earliest example of an abstract published alongside the paper it summarises was the 1919 paper On the Irregularities of Motion of the Foucault Pendulum published in the Physical Review of the American Physical Society, which often published abstracts thereafter.\nCopyright\nAbstracts are protected under copyright law just as any other form of written speech is protected.\nStructure\nAbstract is often expected to tell a complete story of the paper, as for most readers, abstract is the only part of the paper that will be read. It should allow the reader to give an elevator pitch of the full paper.\nAn academic abstract typically outlines four elements relevant to the completed work:\nThe research focus (statement of the problem(s)/specific gap in existing research/research issue(s) addressed);\nThe research methods (experimental research, case studies,  questionnaires, etc) used to solve the problem;\nThe major results/findings of the research; and\nThe main conclusions and recommendations (i.e., how the work answers the proposed research problem).\nIt may also contain brief references, although some publications' standard style omits references from the abstract, reserving them for the article body (which, by definition, treats the same topics but in more depth)."]], "0_corpus": "wikipedia", "1_conv_id": "13aca6a147054459a7ad0f23455c0359", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "how to write a good abstract", "1_output": [["how to write a good abstract", "Abstract (summary)\n\nAn abstract is a brief summary of a research article, thesis, review, conference proceeding, or any in-depth analysis of a particular subject and is often used to help the reader quickly ascertain the paper's purpose. When used, an abstract always appears at the beginning of a manuscript or typescript, acting as the point-of-entry for any given academic paper or patent application.  Abstracting and indexing services for various academic disciplines are aimed at compiling a body of literature for that particular subject."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722391832.6707, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "39e382d9a54846878022c80cf55a9a6a", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Which test was devised to determine whether robots can think?", "0_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"]], "0_corpus": "wikipedia", "1_conv_id": "7b9ad82677324bd3a868102ae49d156f", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_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)."]], "1_corpus": "wikipedia"}

data/retrieval_side_by_side-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -1,9 +1,9 @@
-{"tstamp": 1722362878.6442, "task_type": "retrieval", "type": "rightvote", "models": ["jinaai/jina-embeddings-v2-base-en", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "311b2013c4ca442a81473006aa78e849", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "how many digits of pi?", "0_output": [["how many digits of pi?", "Pi\n\nHere, the circumference of a circle is the arc length around the perimeter of the circle, a quantity which can be formally defined independently of geometry using limits—a concept in calculus.  For example, one may directly compute the arc length of the top half of the unit circle, given in Cartesian coordinates by the equation , as the integral:\nAn integral such as this was adopted as the definition of  by Karl Weierstrass, who defined it directly as an integral in 1841.\nIntegration is no longer commonly used in a first analytical definition because, as  explains, differential calculus typically precedes integral calculus in the university curriculum, so it is desirable to have a definition of  that does not rely on the latter.  One such definition, due to Richard Baltzer and popularized by Edmund Landau, is the following:  is twice the smallest positive number at which the cosine function equals 0.  is also the smallest positive number at which the sine function equals zero, and the difference between consecutive zeroes of the sine function. The cosine and sine can be defined independently of geometry as a power series, or as the solution of a differential equation.\nIn a similar spirit,  can be defined using properties of the complex exponential, , of a complex variable . Like the cosine, the complex exponential can be defined in one of several ways. The set of complex numbers at which  is equal to one is then an (imaginary) arithmetic progression of the form:"]], "0_corpus": "wikipedia", "1_conv_id": "8d915aba09224216b108ecbf4449a97f", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "how many digits of pi?", "1_output": [["how many digits of pi?", "Pi\n\nThe number  (; spelled out as \"pi\") is a mathematical constant that is the ratio of a circle's circumference to its diameter, approximately equal to 3.14159. The number  appears in many formulae across mathematics and physics. It is an irrational number, meaning that it cannot be expressed exactly as a ratio of two integers, although fractions such as  are commonly used to approximate it. Consequently, its decimal representation never ends, nor enters a permanently repeating pattern. It is a transcendental number, meaning that it cannot be a solution of an equation involving only finite sums, products, powers, and integers. The transcendence of  implies that it is impossible to solve the ancient challenge of squaring the circle with a compass and straightedge. The decimal digits of  appear to be randomly distributed, but no proof of this conjecture has been found."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722362916.7139, "task_type": "retrieval", "type": "rightvote", "models": ["jinaai/jina-embeddings-v2-base-en", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "94b953499ad2440aa0fc11f41c96e7e6", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "who is the founder of OpenAI", "0_output": [["who is the founder of OpenAI", "OpenAI\n\nContract with Sama In January 2023, OpenAI has been criticized for outsourcing the annotation of data sets to Sama, a company based in San Francisco but employing workers in Kenya. These annotations were used to train an AI model to detect toxicity, which could then be used to filter out toxic content, notably from ChatGPT's training data and outputs. However, these pieces of text usually contained detailed descriptions of various types of violence, including sexual violence. The four Sama employees interviewed by Time described themselves as mentally scarred. OpenAI paid Sama $12.50 per hour of work, and Sama was redistributing the equivalent of between $1.32 and $2.00 per hour post-tax to its annotators. Sama's spokesperson said that the $12.50 was also covering other implicit costs, among which were infrastructure expenses, quality assurance and management.\nLack of transparency\nIn March 2023, the company was also criticized for disclosing particularly few technical details about products like GPT-4, contradicting its initial commitment to openness and making it harder for independent researchers to replicate its work and develop safeguards. OpenAI cited competitiveness and safety concerns to justify this strategic turn. OpenAI's former chief scientist Ilya Sutskever argued in 2023 that open-sourcing increasingly capable models was increasingly risky, and that the safety reasons for not open-sourcing the most potent AI models would become \"obvious\" in a few years.\nNon-disparagement agreement\nOn May 17, 2024, a Vox article reported that OpenAI was asking departing employees to sign a lifelong non-disparagement agreement forbidding them from criticizing OpenAI or acknowledging the existence of the agreement. Daniel Kokotajlo, a former employee, publicly stated that he forfeited his vested equity in OpenAI in order to leave without signing the agreement. Sam Altman stated that he was unaware of the equity cancellation provision, and that OpenAI never enforced it to cancel any employee's vested equity. Vox published leaked documents and emails challenging this claim. On May 23, 2024, OpenAI sent a memo releasing former employees from the agreement."]], "0_corpus": "wikipedia", "1_conv_id": "331fde02b308466c8a4c482e91005684", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "who is the founder of OpenAI", "1_output": [["who is the founder of OpenAI", "OpenAI\n\n2015–2018: Non-profit beginnings In December 2015, OpenAI was founded by Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, John Schulman, Pamela Vagata, and Wojciech Zaremba, with Sam Altman and Elon Musk as the co-chairs. $1 billion in total was pledged by Sam Altman, Greg Brockman, Elon Musk, Reid Hoffman, Jessica Livingston, Peter Thiel, Amazon Web Services (AWS), Infosys, and YC Research. The actual collected total amount of contributions was only $130 million until 2019. According to an investigation led by TechCrunch, Musk was its largest donor while YC Research did not contribute anything at all. The organization stated it would \"freely collaborate\" with other institutions and researchers by making its patents and research open to the public. OpenAI was headquartered at the Pioneer Building in the Mission District, San Francisco.\nAccording to Wired, Brockman met with Yoshua Bengio, one of the \"founding fathers\" of deep learning, and drew up a list of the \"best researchers in the field\". Brockman was able to hire nine of them as the first employees in December 2015. In 2016, OpenAI paid corporate-level (rather than nonprofit-level) salaries, but did not pay AI researchers salaries comparable to those of Facebook or Google."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722362878.6442, "task_type": "retrieval", "type": "rightvote", "models": ["jinaai/jina-embeddings-v2-base-en", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "311b2013c4ca442a81473006aa78e849", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "how many digits of pi?", "0_output": [["how many digits of pi?", "Pi\n\nHere, the circumference of a circle is the arc length around the perimeter of the circle, a quantity which can be formally defined independently of geometry using limits\u2014a concept in calculus.  For example, one may directly compute the arc length of the top half of the unit circle, given in Cartesian coordinates by the equation , as the integral:\nAn integral such as this was adopted as the definition of  by Karl Weierstrass, who defined it directly as an integral in 1841.\nIntegration is no longer commonly used in a first analytical definition because, as  explains, differential calculus typically precedes integral calculus in the university curriculum, so it is desirable to have a definition of  that does not rely on the latter.  One such definition, due to Richard Baltzer and popularized by Edmund Landau, is the following:  is twice the smallest positive number at which the cosine function equals 0.  is also the smallest positive number at which the sine function equals zero, and the difference between consecutive zeroes of the sine function. The cosine and sine can be defined independently of geometry as a power series, or as the solution of a differential equation.\nIn a similar spirit,  can be defined using properties of the complex exponential, , of a complex variable . Like the cosine, the complex exponential can be defined in one of several ways. The set of complex numbers at which  is equal to one is then an (imaginary) arithmetic progression of the form:"]], "0_corpus": "wikipedia", "1_conv_id": "8d915aba09224216b108ecbf4449a97f", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "how many digits of pi?", "1_output": [["how many digits of pi?", "Pi\n\nThe number  (; spelled out as \"pi\") is a mathematical constant that is the ratio of a circle's circumference to its diameter, approximately equal to 3.14159. The number  appears in many formulae across mathematics and physics. It is an irrational number, meaning that it cannot be expressed exactly as a ratio of two integers, although fractions such as  are commonly used to approximate it. Consequently, its decimal representation never ends, nor enters a permanently repeating pattern. It is a transcendental number, meaning that it cannot be a solution of an equation involving only finite sums, products, powers, and integers. The transcendence of  implies that it is impossible to solve the ancient challenge of squaring the circle with a compass and straightedge. The decimal digits of  appear to be randomly distributed, but no proof of this conjecture has been found."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722362916.7139, "task_type": "retrieval", "type": "rightvote", "models": ["jinaai/jina-embeddings-v2-base-en", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "94b953499ad2440aa0fc11f41c96e7e6", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "who is the founder of OpenAI", "0_output": [["who is the founder of OpenAI", "OpenAI\n\nContract with Sama In January 2023, OpenAI has been criticized for outsourcing the annotation of data sets to Sama, a company based in San Francisco but employing workers in Kenya. These annotations were used to train an AI model to detect toxicity, which could then be used to filter out toxic content, notably from ChatGPT's training data and outputs. However, these pieces of text usually contained detailed descriptions of various types of violence, including sexual violence. The four Sama employees interviewed by Time described themselves as mentally scarred. OpenAI paid Sama $12.50 per hour of work, and Sama was redistributing the equivalent of between $1.32 and $2.00 per hour post-tax to its annotators. Sama's spokesperson said that the $12.50 was also covering other implicit costs, among which were infrastructure expenses, quality assurance and management.\nLack of transparency\nIn March 2023, the company was also criticized for disclosing particularly few technical details about products like GPT-4, contradicting its initial commitment to openness and making it harder for independent researchers to replicate its work and develop safeguards. OpenAI cited competitiveness and safety concerns to justify this strategic turn. OpenAI's former chief scientist Ilya Sutskever argued in 2023 that open-sourcing increasingly capable models was increasingly risky, and that the safety reasons for not open-sourcing the most potent AI models would become \"obvious\" in a few years.\nNon-disparagement agreement\nOn May 17, 2024, a Vox article reported that OpenAI was asking departing employees to sign a lifelong non-disparagement agreement forbidding them from criticizing OpenAI or acknowledging the existence of the agreement. Daniel Kokotajlo, a former employee, publicly stated that he forfeited his vested equity in OpenAI in order to leave without signing the agreement. Sam Altman stated that he was unaware of the equity cancellation provision, and that OpenAI never enforced it to cancel any employee's vested equity. Vox published leaked documents and emails challenging this claim. On May 23, 2024, OpenAI sent a memo releasing former employees from the agreement."]], "0_corpus": "wikipedia", "1_conv_id": "331fde02b308466c8a4c482e91005684", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "who is the founder of OpenAI", "1_output": [["who is the founder of OpenAI", "OpenAI\n\n2015\u20132018: Non-profit beginnings In December 2015, OpenAI was founded by Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, John Schulman, Pamela Vagata, and Wojciech Zaremba, with Sam Altman and Elon Musk as the co-chairs. $1 billion in total was pledged by Sam Altman, Greg Brockman, Elon Musk, Reid Hoffman, Jessica Livingston, Peter Thiel, Amazon Web Services (AWS), Infosys, and YC Research. The actual collected total amount of contributions was only $130 million until 2019. According to an investigation led by TechCrunch, Musk was its largest donor while YC Research did not contribute anything at all. The organization stated it would \"freely collaborate\" with other institutions and researchers by making its patents and research open to the public. OpenAI was headquartered at the Pioneer Building in the Mission District, San Francisco.\nAccording to Wired, Brockman met with Yoshua Bengio, one of the \"founding fathers\" of deep learning, and drew up a list of the \"best researchers in the field\". Brockman was able to hire nine of them as the first employees in December 2015. In 2016, OpenAI paid corporate-level (rather than nonprofit-level) salaries, but did not pay AI researchers salaries comparable to those of Facebook or Google."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722363628.2337, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "voyage-multilingual-2"], "ip": "", "0_conv_id": "4f1cbad6ec8d493a9535809100a56469", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "How can I use LORA to heal quantization loss in LLMs?", "0_output": [["How can I use LORA to heal quantization loss in LLMs?", "Title: Accurate LoRA-Finetuning Quantization of LLMs via Information Retention\n\nAbstract: The LoRA-finetuning quantization of LLMs has been extensively studied to obtain accurate yet compact LLMs for deployment on resource-constrained hardware. However, existing methods cause the quantized LLM to severely degrade and even fail to benefit from the finetuning of LoRA. This paper proposes a novel IR-QLoRA for pushing quantized LLMs with LoRA to be highly accurate through information retention. The proposed IR-QLoRA mainly relies on two technologies derived from the perspective of unified information: (1) statistics-based Information Calibration Quantization allows the quantized parameters of LLM to retain original information accurately; (2) finetuning-based Information Elastic Connection makes LoRA utilizes elastic representation transformation with diverse information. Comprehensive experiments show that IR-QLoRA can significantly improve accuracy across LLaMA and LLaMA2 families under 2-4 bit-widths, e.g., 4- bit LLaMA-7B achieves 1.4% improvement on MMLU compared with the state-of-the-art methods. The significant performance gain requires only a tiny 0.31% additional time consumption, revealing the satisfactory efficiency of our IR-QLoRA. We highlight that IR-QLoRA enjoys excellent versatility, compatible with various frameworks (e.g., NormalFloat and Integer quantization) and brings general accuracy gains. The code is available at https://github.com/htqin/ir-qlora."]], "0_corpus": "arxiv", "1_conv_id": "a8cbb87ee4844b2cb5b80e85059775b8", "1_model_name": "voyage-multilingual-2", "1_prompt": "How can I use LORA to heal quantization loss in LLMs?", "1_output": [["How can I use LORA to heal quantization loss in LLMs?", "Title: Accurate LoRA-Finetuning Quantization of LLMs via Information Retention\n\nAbstract: The LoRA-finetuning quantization of LLMs has been extensively studied to obtain accurate yet compact LLMs for deployment on resource-constrained hardware. However, existing methods cause the quantized LLM to severely degrade and even fail to benefit from the finetuning of LoRA. This paper proposes a novel IR-QLoRA for pushing quantized LLMs with LoRA to be highly accurate through information retention. The proposed IR-QLoRA mainly relies on two technologies derived from the perspective of unified information: (1) statistics-based Information Calibration Quantization allows the quantized parameters of LLM to retain original information accurately; (2) finetuning-based Information Elastic Connection makes LoRA utilizes elastic representation transformation with diverse information. Comprehensive experiments show that IR-QLoRA can significantly improve accuracy across LLaMA and LLaMA2 families under 2-4 bit-widths, e.g., 4- bit LLaMA-7B achieves 1.4% improvement on MMLU compared with the state-of-the-art methods. The significant performance gain requires only a tiny 0.31% additional time consumption, revealing the satisfactory efficiency of our IR-QLoRA. We highlight that IR-QLoRA enjoys excellent versatility, compatible with various frameworks (e.g., NormalFloat and Integer quantization) and brings general accuracy gains. The code is available at https://github.com/htqin/ir-qlora."]], "1_corpus": "arxiv"}
 {"tstamp": 1722363658.2864, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "voyage-multilingual-2"], "ip": "", "0_conv_id": "6486597a43d54ee389973933b38d46cc", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Are LLMs good at playing Chess?", "0_output": [["Are LLMs good at playing Chess?", "Title: Can Large Language Models Play Games? A Case Study of A Self-Play Approach\n\nAbstract: Large Language Models (LLMs) harness extensive data from the Internet, storing a broad spectrum of prior knowledge. While LLMs have proven beneficial as decision-making aids, their reliability is hampered by limitations in reasoning, hallucination phenomenon, and so on. On the other hand, Monte-Carlo Tree Search (MCTS) is a heuristic search algorithm that provides reliable decision-making solutions, achieved through recursive rollouts and self-play. However, the effectiveness of MCTS relies heavily on heuristic pruning and external value functions, particularly in complex decision scenarios. This work introduces an innovative approach that bolsters LLMs with MCTS self-play to efficiently resolve deterministic turn-based zero-sum games (DTZG), such as chess and go, without the need for additional training. Specifically, we utilize LLMs as both action pruners and proxies for value functions without the need for additional training. We theoretically prove that the suboptimality of the estimated value in our proposed method scales with $\\tilde{\\mathcal O}\\Bigl(\\frac{|\\tilde {\\mathcal A}|}{\\sqrt{N}} + \\epsilon_\\mathrm{pruner} + \\epsilon_\\mathrm{critic}\\Bigr)$, where \\(N\\) is the number of simulations, $|\\tilde {\\mathcal A}|$ is the cardinality of the pruned action space by LLM, and $\\epsilon_\\mathrm{pruner}$ and $\\epsilon_\\mathrm{critic}$ quantify the errors incurred by adopting LLMs as action space pruner and value function proxy, respectively. Our experiments in chess and go demonstrate the capability of our method to address challenges beyond the scope of MCTS and improve the performance of the directly application of LLMs."]], "0_corpus": "arxiv", "1_conv_id": "bbfdc697cb334ea39a5c618b379a2aad", "1_model_name": "voyage-multilingual-2", "1_prompt": "Are LLMs good at playing Chess?", "1_output": [["Are LLMs good at playing Chess?", "Title: Can Large Language Models Play Games? A Case Study of A Self-Play Approach\n\nAbstract: Large Language Models (LLMs) harness extensive data from the Internet, storing a broad spectrum of prior knowledge. While LLMs have proven beneficial as decision-making aids, their reliability is hampered by limitations in reasoning, hallucination phenomenon, and so on. On the other hand, Monte-Carlo Tree Search (MCTS) is a heuristic search algorithm that provides reliable decision-making solutions, achieved through recursive rollouts and self-play. However, the effectiveness of MCTS relies heavily on heuristic pruning and external value functions, particularly in complex decision scenarios. This work introduces an innovative approach that bolsters LLMs with MCTS self-play to efficiently resolve deterministic turn-based zero-sum games (DTZG), such as chess and go, without the need for additional training. Specifically, we utilize LLMs as both action pruners and proxies for value functions without the need for additional training. We theoretically prove that the suboptimality of the estimated value in our proposed method scales with $\\tilde{\\mathcal O}\\Bigl(\\frac{|\\tilde {\\mathcal A}|}{\\sqrt{N}} + \\epsilon_\\mathrm{pruner} + \\epsilon_\\mathrm{critic}\\Bigr)$, where \\(N\\) is the number of simulations, $|\\tilde {\\mathcal A}|$ is the cardinality of the pruned action space by LLM, and $\\epsilon_\\mathrm{pruner}$ and $\\epsilon_\\mathrm{critic}$ quantify the errors incurred by adopting LLMs as action space pruner and value function proxy, respectively. Our experiments in chess and go demonstrate the capability of our method to address challenges beyond the scope of MCTS and improve the performance of the directly application of LLMs."]], "1_corpus": "arxiv"}
 {"tstamp": 1722363723.7147, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "voyage-multilingual-2"], "ip": "", "0_conv_id": "5b77226518cf43a59a25297c3e1cd6c5", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Can LLMs outperform more common recommendation engines?", "0_output": [["Can LLMs outperform more common recommendation engines?", "Title: Improving Sequential Recommendations with LLMs\n\nAbstract: The sequential recommendation problem has attracted considerable research attention in the past few years, leading to the rise of numerous recommendation models. In this work, we explore how Large Language Models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or improve sequential recommendation approaches. Specifically, we design three orthogonal approaches and hybrids of those to leverage the power of LLMs in different ways. In addition, we investigate the potential of each approach by focusing on its comprising technical aspects and determining an array of alternative choices for each one. We conduct extensive experiments on three datasets and explore a large variety of configurations, including different language models and baseline recommendation models, to obtain a comprehensive picture of the performance of each approach. Among other observations, we highlight that initializing state-of-the-art sequential recommendation models such as BERT4Rec or SASRec with embeddings obtained from an LLM can lead to substantial performance gains in terms of accuracy. Furthermore, we find that fine-tuning an LLM for recommendation tasks enables it to learn not only the tasks, but also concepts of a domain to some extent. We also show that fine-tuning OpenAI GPT leads to considerably better performance than fine-tuning Google PaLM 2. Overall, our extensive experiments indicate a huge potential value of leveraging LLMs in future recommendation approaches. We publicly share the code and data of our experiments to ensure reproducibility."]], "0_corpus": "arxiv", "1_conv_id": "aad470c6fb3e49efae386d6b5d629ec8", "1_model_name": "voyage-multilingual-2", "1_prompt": "Can LLMs outperform more common recommendation engines?", "1_output": [["Can LLMs outperform more common recommendation engines?", "Title: Improving Sequential Recommendations with LLMs\n\nAbstract: The sequential recommendation problem has attracted considerable research attention in the past few years, leading to the rise of numerous recommendation models. In this work, we explore how Large Language Models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or improve sequential recommendation approaches. Specifically, we design three orthogonal approaches and hybrids of those to leverage the power of LLMs in different ways. In addition, we investigate the potential of each approach by focusing on its comprising technical aspects and determining an array of alternative choices for each one. We conduct extensive experiments on three datasets and explore a large variety of configurations, including different language models and baseline recommendation models, to obtain a comprehensive picture of the performance of each approach. Among other observations, we highlight that initializing state-of-the-art sequential recommendation models such as BERT4Rec or SASRec with embeddings obtained from an LLM can lead to substantial performance gains in terms of accuracy. Furthermore, we find that fine-tuning an LLM for recommendation tasks enables it to learn not only the tasks, but also concepts of a domain to some extent. We also show that fine-tuning OpenAI GPT leads to considerably better performance than fine-tuning Google PaLM 2. Overall, our extensive experiments indicate a huge potential value of leveraging LLMs in future recommendation approaches. We publicly share the code and data of our experiments to ensure reproducibility."]], "1_corpus": "arxiv"}
-{"tstamp": 1722363761.9001, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "voyage-multilingual-2"], "ip": "", "0_conv_id": "302e8b4b54454bf39fe90e1eae0ff357", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "When was Barak Obama born?", "0_output": [["When was Barak Obama born?", "Barack Obama\n\nBarack Hussein Obama II (born August 4, 1961) is an American politician who served as the 44th president of the United States from 2009 to 2017. As a member of the Democratic Party, he was the first African-American  president in U.S. history. Obama previously served as a U.S. senator representing Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.\nObama was born in Honolulu, Hawaii. He graduated from Columbia University in 1983 with a Bachelor of Arts degree in political science and later worked as a community organizer in Chicago. In 1988, Obama enrolled in Harvard Law School, where he was the first black president of the Harvard Law Review. He became a civil rights attorney and an academic, teaching constitutional law at the University of Chicago Law School from 1992 to 2004. He also went into elective politics; Obama represented the 13th district in the Illinois Senate from 1997 until 2004, when he successfully ran for the U.S. Senate. In the 2008 presidential election, after a close primary campaign against Hillary Clinton, he was nominated by the Democratic Party for president. Obama selected Joe Biden as his running mate and defeated Republican nominee John McCain."]], "0_corpus": "wikipedia", "1_conv_id": "bb784d4759d840059823dfe465d8781d", "1_model_name": "voyage-multilingual-2", "1_prompt": "When was Barak Obama born?", "1_output": [["When was Barak Obama born?", "Barack Obama\n\nEarly life and career Obama was born on August 4, 1961, at Kapiolani Medical Center for Women and Children in Honolulu, Hawaii. He is the only president born outside the contiguous 48 states. He was born to an 18-year-old American mother and a 27-year-old Kenyan father. His mother, Ann Dunham (1942–1995), was born in Wichita, Kansas, and was of English, Welsh, German, Swiss, and Irish descent. In 2007 it was discovered her great-great-grandfather Falmouth Kearney emigrated from the village of Moneygall, Ireland to the US in 1850. In July 2012, Ancestry.com found a strong likelihood that Dunham was descended from John Punch, an enslaved African man who lived in the Colony of Virginia during the seventeenth century. Obama's father, Barack Obama Sr. (1934–1982), was a married Luo Kenyan from Nyang'oma Kogelo. His last name, Obama, was derived from his Luo descent. Obama's parents met in 1960 in a Russian language class at the University of Hawaiʻi at Mānoa, where his father was a foreign student on a scholarship. The couple married in Wailuku, Hawaii, on February 2, 1961, six months before Obama was born.\nIn late August 1961, a few weeks after he was born, Barack and his mother moved to the University of Washington in Seattle, where they lived for a year. During that time, Barack's father completed his undergraduate degree in economics in Hawaii, graduating in June 1962. He left to attend graduate school on a scholarship at Harvard University, where he earned an M.A. in economics. Obama's parents divorced in March 1964. Obama Sr. returned to Kenya in 1964, where he married for a third time and worked for the Kenyan government as the Senior Economic Analyst in the Ministry of Finance. He visited his son in Hawaii only once, at Christmas 1971, before he was killed in an automobile accident in 1982, when Obama was 21 years old. Recalling his early childhood, Obama said: \"That my father looked nothing like the people around me—that he was black as pitch, my mother white as milk—barely registered in my mind.\" He described his struggles as a young adult to reconcile social perceptions of his multiracial heritage."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722373132.0707, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "embed-english-v3.0"], "ip": "", "0_conv_id": "6597798995654ab3bb3360e3a5aef206", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "who won the men's single title of australia open on 1 february 2015", "0_output": [["who won the men's single title of australia open on 1 february 2015", "2015 Australian Open – Men's singles\n\nNovak Djokovic defeated Andy Murray in the final, 7–6(7–5), 6–7(4–7), 6–3, 6–0 to win the men's singles tennis title at the 2015 Australian Open. It was his fifth Australian Open title and his eighth major title overall. It was also Murray's fourth runner-up finish at the event, the most in the Open Era.\nStan Wawrinka was the defending champion, but lost in the semifinals to Djokovic. It was the third consecutive year that the two met at the Australian Open; on each occasion, their encounter went to a fifth set."]], "0_corpus": "wikipedia", "1_conv_id": "b7fb4175ae5c4f0eaf1c60966a9aed9d", "1_model_name": "embed-english-v3.0", "1_prompt": "who won the men's single title of australia open on 1 february 2015", "1_output": [["who won the men's single title of australia open on 1 february 2015", "2015 Australian Open – Men's singles\n\nNovak Djokovic defeated Andy Murray in the final, 7–6(7–5), 6–7(4–7), 6–3, 6–0 to win the men's singles tennis title at the 2015 Australian Open. It was his fifth Australian Open title and his eighth major title overall. It was also Murray's fourth runner-up finish at the event, the most in the Open Era.\nStan Wawrinka was the defending champion, but lost in the semifinals to Djokovic. It was the third consecutive year that the two met at the Australian Open; on each occasion, their encounter went to a fifth set."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722363761.9001, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "voyage-multilingual-2"], "ip": "", "0_conv_id": "302e8b4b54454bf39fe90e1eae0ff357", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "When was Barak Obama born?", "0_output": [["When was Barak Obama born?", "Barack Obama\n\nBarack Hussein Obama II (born August 4, 1961) is an American politician who served as the 44th president of the United States from 2009 to 2017. As a member of the Democratic Party, he was the first African-American  president in U.S. history. Obama previously served as a U.S. senator representing Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.\nObama was born in Honolulu, Hawaii. He graduated from Columbia University in 1983 with a Bachelor of Arts degree in political science and later worked as a community organizer in Chicago. In 1988, Obama enrolled in Harvard Law School, where he was the first black president of the Harvard Law Review. He became a civil rights attorney and an academic, teaching constitutional law at the University of Chicago Law School from 1992 to 2004. He also went into elective politics; Obama represented the 13th district in the Illinois Senate from 1997 until 2004, when he successfully ran for the U.S. Senate. In the 2008 presidential election, after a close primary campaign against Hillary Clinton, he was nominated by the Democratic Party for president. Obama selected Joe Biden as his running mate and defeated Republican nominee John McCain."]], "0_corpus": "wikipedia", "1_conv_id": "bb784d4759d840059823dfe465d8781d", "1_model_name": "voyage-multilingual-2", "1_prompt": "When was Barak Obama born?", "1_output": [["When was Barak Obama born?", "Barack Obama\n\nEarly life and career Obama was born on August 4, 1961, at Kapiolani Medical Center for Women and Children in Honolulu, Hawaii. He is the only president born outside the contiguous 48 states. He was born to an 18-year-old American mother and a 27-year-old Kenyan father. His mother, Ann Dunham (1942\u20131995), was born in Wichita, Kansas, and was of English, Welsh, German, Swiss, and Irish descent. In 2007 it was discovered her great-great-grandfather Falmouth Kearney emigrated from the village of Moneygall, Ireland to the US in 1850. In July 2012, Ancestry.com found a strong likelihood that Dunham was descended from John Punch, an enslaved African man who lived in the Colony of Virginia during the seventeenth century. Obama's father, Barack Obama Sr. (1934\u20131982), was a married Luo Kenyan from Nyang'oma Kogelo. His last name, Obama, was derived from his Luo descent. Obama's parents met in 1960 in a Russian language class at the University of Hawai\u02bbi at M\u0101noa, where his father was a foreign student on a scholarship. The couple married in Wailuku, Hawaii, on February 2, 1961, six months before Obama was born.\nIn late August 1961, a few weeks after he was born, Barack and his mother moved to the University of Washington in Seattle, where they lived for a year. During that time, Barack's father completed his undergraduate degree in economics in Hawaii, graduating in June 1962. He left to attend graduate school on a scholarship at Harvard University, where he earned an M.A. in economics. Obama's parents divorced in March 1964. Obama Sr. returned to Kenya in 1964, where he married for a third time and worked for the Kenyan government as the Senior Economic Analyst in the Ministry of Finance. He visited his son in Hawaii only once, at Christmas 1971, before he was killed in an automobile accident in 1982, when Obama was 21 years old. Recalling his early childhood, Obama said: \"That my father looked nothing like the people around me\u2014that he was black as pitch, my mother white as milk\u2014barely registered in my mind.\" He described his struggles as a young adult to reconcile social perceptions of his multiracial heritage."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722373132.0707, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "embed-english-v3.0"], "ip": "", "0_conv_id": "6597798995654ab3bb3360e3a5aef206", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "who won the men's single title of australia open on 1 february 2015", "0_output": [["who won the men's single title of australia open on 1 february 2015", "2015 Australian Open \u2013 Men's singles\n\nNovak Djokovic defeated Andy Murray in the final, 7\u20136(7\u20135), 6\u20137(4\u20137), 6\u20133, 6\u20130 to win the men's singles tennis title at the 2015 Australian Open. It was his fifth Australian Open title and his eighth major title overall. It was also Murray's fourth runner-up finish at the event, the most in the Open Era.\nStan Wawrinka was the defending champion, but lost in the semifinals to Djokovic. It was the third consecutive year that the two met at the Australian Open; on each occasion, their encounter went to a fifth set."]], "0_corpus": "wikipedia", "1_conv_id": "b7fb4175ae5c4f0eaf1c60966a9aed9d", "1_model_name": "embed-english-v3.0", "1_prompt": "who won the men's single title of australia open on 1 february 2015", "1_output": [["who won the men's single title of australia open on 1 february 2015", "2015 Australian Open \u2013 Men's singles\n\nNovak Djokovic defeated Andy Murray in the final, 7\u20136(7\u20135), 6\u20137(4\u20137), 6\u20133, 6\u20130 to win the men's singles tennis title at the 2015 Australian Open. It was his fifth Australian Open title and his eighth major title overall. It was also Murray's fourth runner-up finish at the event, the most in the Open Era.\nStan Wawrinka was the defending champion, but lost in the semifinals to Djokovic. It was the third consecutive year that the two met at the Australian Open; on each occasion, their encounter went to a fifth set."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722376485.6374, "task_type": "retrieval", "type": "share", "models": ["### Model A: mixedbread-ai/mxbai-embed-large-v1", "### Model B: jinaai/jina-embeddings-v2-base-en"], "ip": "", "0_conv_id": "e1b3df376a404d4aa80430ef73cc4996", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "What is the capital of zimbabwe?", "0_output": [["What is the capital of zimbabwe?", "Harare\n\nHarare ( ), formerly known as Salisbury ( ), is the capital and largest city of Zimbabwe. The city proper has an area of , a population of 1,849,600 as of the 2022 census and an estimated 2,487,209 people in its metropolitan province. The city is situated in north-eastern Zimbabwe in the country's Mashonaland region. Harare is a metropolitan province which also incorporates the municipalities of Chitungwiza and Epworth. The city sits on a plateau at an elevation of  above sea level, and its climate falls into the subtropical highland category."]], "0_corpus": "wikipedia", "1_conv_id": "894fded54c5d4565a77a64a5c139d079", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "What is the capital of zimbabwe?", "1_output": [["What is the capital of zimbabwe?", "Zimbabwe\n\nZimbabwe (; ), officially the Republic of Zimbabwe, is a landlocked country in Southern Africa, between the Zambezi and Limpopo Rivers, bordered by South Africa to the south, Botswana to the southwest, Zambia to the north, and Mozambique to the east. The capital and largest city is Harare, and the second largest is Bulawayo.\nA country of roughly 15 million people as per 2022 census, Zimbabwe's largest ethnic group are the Shona, who make up 80% of the population, followed by the Northern Ndebele and other smaller minorities. Zimbabwe has 16 official languages, with English, Shona, and Ndebele the most common. Zimbabwe is a member of the United Nations, the Southern African Development Community, the African Union, and the Common Market for Eastern and Southern Africa."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722381995.4412, "task_type": "retrieval", "type": "tievote", "models": ["text-embedding-3-large", "mixedbread-ai/mxbai-embed-large-v1"], "ip": "", "0_conv_id": "23bd3f591b1742f2b7559671a7eccc77", "0_model_name": "text-embedding-3-large", "0_prompt": "REAL ESTATE TRANSFER DISCLOSURE STATEMENT", "0_output": [["REAL ESTATE TRANSFER DISCLOSURE STATEMENT", "Real estate business\n\nTransactions A real estate transaction is the process whereby rights in a unit of property (or designated real estate) is transferred between two or more parties, e.g. in case of conveyance one party being the seller(s) and the other being the buyer(s). It can often be quite complicated due to the complexity of the property rights being transferred, the amount of money being exchanged, and government regulations. Conventions and requirements also vary considerably among different countries of the world and among smaller legal entities (jurisdictions).\nIn more abstract terms, a real estate transaction, like other financial transactions, causes transaction costs. To identify and possibly reduce these transaction costs, the Organisation for Economic Co-operation and Development (OECD) addressed the issue through a study commissioned by the European Commission,\nand through a research action.\nThe mentioned research action ‘Modelling Real Property Transactions’ investigated methods to describe selected transactions in a formal way, to allow for comparisons across countries / jurisdictions. Descriptions were performed both using a more simple format, a Basic Use Case template, and more advanced applications of the Unified Modelling Language. Process models were compared through an ontology-based methodology, and national property transaction costs were estimated for Finland and Denmark, based on the directions of the United Nations System of National Accounts.\nReal estate transactions: subdivision, conveyance, and mortgaging, as they are performed in the five Nordic countries are described in some detail. A translation into English is available for the Danish part."]], "0_corpus": "wikipedia", "1_conv_id": "c8d7fa69b3684c83a21c4d90706f0b65", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "REAL ESTATE TRANSFER DISCLOSURE STATEMENT", "1_output": [["REAL ESTATE TRANSFER DISCLOSURE STATEMENT", "Real estate business\n\nTransactions A real estate transaction is the process whereby rights in a unit of property (or designated real estate) is transferred between two or more parties, e.g. in case of conveyance one party being the seller(s) and the other being the buyer(s). It can often be quite complicated due to the complexity of the property rights being transferred, the amount of money being exchanged, and government regulations. Conventions and requirements also vary considerably among different countries of the world and among smaller legal entities (jurisdictions).\nIn more abstract terms, a real estate transaction, like other financial transactions, causes transaction costs. To identify and possibly reduce these transaction costs, the Organisation for Economic Co-operation and Development (OECD) addressed the issue through a study commissioned by the European Commission,\nand through a research action.\nThe mentioned research action ‘Modelling Real Property Transactions’ investigated methods to describe selected transactions in a formal way, to allow for comparisons across countries / jurisdictions. Descriptions were performed both using a more simple format, a Basic Use Case template, and more advanced applications of the Unified Modelling Language. Process models were compared through an ontology-based methodology, and national property transaction costs were estimated for Finland and Denmark, based on the directions of the United Nations System of National Accounts.\nReal estate transactions: subdivision, conveyance, and mortgaging, as they are performed in the five Nordic countries are described in some detail. A translation into English is available for the Danish part."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722381995.4412, "task_type": "retrieval", "type": "tievote", "models": ["text-embedding-3-large", "mixedbread-ai/mxbai-embed-large-v1"], "ip": "", "0_conv_id": "23bd3f591b1742f2b7559671a7eccc77", "0_model_name": "text-embedding-3-large", "0_prompt": "REAL ESTATE TRANSFER DISCLOSURE STATEMENT", "0_output": [["REAL ESTATE TRANSFER DISCLOSURE STATEMENT", "Real estate business\n\nTransactions A real estate transaction is the process whereby rights in a unit of property (or designated real estate) is transferred between two or more parties, e.g. in case of conveyance one party being the seller(s) and the other being the buyer(s). It can often be quite complicated due to the complexity of the property rights being transferred, the amount of money being exchanged, and government regulations. Conventions and requirements also vary considerably among different countries of the world and among smaller legal entities (jurisdictions).\nIn more abstract terms, a real estate transaction, like other financial transactions, causes transaction costs. To identify and possibly reduce these transaction costs, the Organisation for Economic Co-operation and Development (OECD) addressed the issue through a study commissioned by the European Commission,\nand through a research action.\nThe mentioned research action \u2018Modelling Real Property Transactions\u2019 investigated methods to describe selected transactions in a formal way, to allow for comparisons across countries / jurisdictions. Descriptions were performed both using a more simple format, a Basic Use Case template, and more advanced applications of the Unified Modelling Language. Process models were compared through an ontology-based methodology, and national property transaction costs were estimated for Finland and Denmark, based on the directions of the United Nations System of National Accounts.\nReal estate transactions: subdivision, conveyance, and mortgaging, as they are performed in the five Nordic countries are described in some detail. A translation into English is available for the Danish part."]], "0_corpus": "wikipedia", "1_conv_id": "c8d7fa69b3684c83a21c4d90706f0b65", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "REAL ESTATE TRANSFER DISCLOSURE STATEMENT", "1_output": [["REAL ESTATE TRANSFER DISCLOSURE STATEMENT", "Real estate business\n\nTransactions A real estate transaction is the process whereby rights in a unit of property (or designated real estate) is transferred between two or more parties, e.g. in case of conveyance one party being the seller(s) and the other being the buyer(s). It can often be quite complicated due to the complexity of the property rights being transferred, the amount of money being exchanged, and government regulations. Conventions and requirements also vary considerably among different countries of the world and among smaller legal entities (jurisdictions).\nIn more abstract terms, a real estate transaction, like other financial transactions, causes transaction costs. To identify and possibly reduce these transaction costs, the Organisation for Economic Co-operation and Development (OECD) addressed the issue through a study commissioned by the European Commission,\nand through a research action.\nThe mentioned research action \u2018Modelling Real Property Transactions\u2019 investigated methods to describe selected transactions in a formal way, to allow for comparisons across countries / jurisdictions. Descriptions were performed both using a more simple format, a Basic Use Case template, and more advanced applications of the Unified Modelling Language. Process models were compared through an ontology-based methodology, and national property transaction costs were estimated for Finland and Denmark, based on the directions of the United Nations System of National Accounts.\nReal estate transactions: subdivision, conveyance, and mortgaging, as they are performed in the five Nordic countries are described in some detail. A translation into English is available for the Danish part."]], "1_corpus": "wikipedia"}

data/retrieval_side_by_side-cc11c9ee-fc93-4cd6-a4b5-246a7f21038c.jsonl

@@ -1,3 +1,3 @@
-{"tstamp": 1722332338.1269, "task_type": "retrieval", "type": "bothbadvote", "models": ["jinaai/jina-embeddings-v2-base-en", "mixedbread-ai/mxbai-embed-large-v1"], "ip": "", "0_conv_id": "804ddfe21ba240c19d8b2b8247e7fe5b", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Why can't my lawn get too tall in the suburbs?", "0_output": [["Why can't my lawn get too tall in the suburbs?", "The Suburbs\n\n#2 – Billboards Top 10 Albums of 2010\n#2 – Magnets Top 20 Albums of 2010\n#2 – NMEs Top 75 Albums of 2010\n#2 – Relevant Magazines Top 10 Albums of 2010\n#2 – Stereogum's Top 50 Albums of 2010\n#2 – Times Top 10 Albums of 2010\n#2 – Under the Radars Top 50 Albums of 2010\n#3 – Spins 40 Best Albums of 2010\n#4 – MTV's 20 Best Albums of 2010\n#4 – Rolling Stones 30 Best Albums of 2010\n#7 – Paste Magazine's 50 Best Albums of 2010\n#9 – American Songwriters Top 50 Albums of 2010\n#11 – Drowned in Sound's Albums of the Year\n#11 – Pitchfork's Top 50 Albums of 2010\n#21 – Rough Trade Shops's Albums of the Year (UK)\n#23 – Robert Christgau's 2010 Dean's List\nGlide Magazines Top 20 Albums of 2010\nNPR's 50 Favorite Albums of 2010\nThe single “Ready to Start” was nominated for the Grammy Award for Best Rock Performance by a Duo or Group with Vocal.\nTrack listingNotes\"Suburban War\" is the second to last track on the vinyl version of the album.\n\"We Used to Wait\" finishes on an infinite loop at the end of Side 3 of the vinyl version."]], "0_corpus": "wikipedia", "1_conv_id": "09f9e3088f8f45be984082f34dd57d21", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Why can't my lawn get too tall in the suburbs?", "1_output": [["Why can't my lawn get too tall in the suburbs?", "Lawn\n\nReplacing turf grass with low-maintenance groundcovers or employing a variety of low-maintenance perennials, trees and shrubs can be a good alternative to traditional lawn spaces, especially in hard-to-grow or hard-to-mow areas, as it can reduce maintenance requirements, associated pollution and offers higher aesthetic and wildlife value. Growing a mixed variety of flowering plants instead of turfgrass is sometimes referred to as meadowscaping.\nNon-productive space\nLawns take up space that could otherwise be used more productively, such as for urban agriculture or home gardening. This is the case in many cities and suburbs in the United States, where open or unused spaces are \"not generally a result of a positive decision to leave room for some use, but rather is an expression of a pastoral aesthetic norm that prizes spacious lawns and the zoning restrictions and neighborhood covenants that give these norms the force of law.\"\nIn urban and suburban spaces, growing food in front yards and parking strips can not only provide fresh produce but also be a source of neighborhood pride. While converting lawn space into strictly utilitarian farms is not common, incorporating edible plants into front yards with sustainable and aesthetically pleasing design is of growing interest in the United States."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722332338.1269, "task_type": "retrieval", "type": "bothbadvote", "models": ["jinaai/jina-embeddings-v2-base-en", "mixedbread-ai/mxbai-embed-large-v1"], "ip": "", "0_conv_id": "804ddfe21ba240c19d8b2b8247e7fe5b", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Why can't my lawn get too tall in the suburbs?", "0_output": [["Why can't my lawn get too tall in the suburbs?", "The Suburbs\n\n#2 \u2013 Billboards Top 10 Albums of 2010\n#2 \u2013 Magnets Top 20 Albums of 2010\n#2 \u2013 NMEs Top 75 Albums of 2010\n#2 \u2013 Relevant Magazines Top 10 Albums of 2010\n#2 \u2013 Stereogum's Top 50 Albums of 2010\n#2 \u2013 Times Top 10 Albums of 2010\n#2 \u2013 Under the Radars Top 50 Albums of 2010\n#3 \u2013 Spins 40 Best Albums of 2010\n#4 \u2013 MTV's 20 Best Albums of 2010\n#4 \u2013 Rolling Stones 30 Best Albums of 2010\n#7 \u2013 Paste Magazine's 50 Best Albums of 2010\n#9 \u2013 American Songwriters Top 50 Albums of 2010\n#11 \u2013 Drowned in Sound's Albums of the Year\n#11 \u2013 Pitchfork's Top 50 Albums of 2010\n#21 \u2013 Rough Trade Shops's Albums of the Year (UK)\n#23 \u2013 Robert Christgau's 2010 Dean's List\nGlide Magazines Top 20 Albums of 2010\nNPR's 50 Favorite Albums of 2010\nThe single \u201cReady to Start\u201d was nominated for the Grammy Award for Best Rock Performance by a Duo or Group with Vocal.\nTrack listingNotes\"Suburban War\" is the second to last track on the vinyl version of the album.\n\"We Used to Wait\" finishes on an infinite loop at the end of Side 3 of the vinyl version."]], "0_corpus": "wikipedia", "1_conv_id": "09f9e3088f8f45be984082f34dd57d21", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Why can't my lawn get too tall in the suburbs?", "1_output": [["Why can't my lawn get too tall in the suburbs?", "Lawn\n\nReplacing turf grass with low-maintenance groundcovers or employing a variety of low-maintenance perennials, trees and shrubs can be a good alternative to traditional lawn spaces, especially in hard-to-grow or hard-to-mow areas, as it can reduce maintenance requirements, associated pollution and offers higher aesthetic and wildlife value. Growing a mixed variety of flowering plants instead of turfgrass is sometimes referred to as meadowscaping.\nNon-productive space\nLawns take up space that could otherwise be used more productively, such as for urban agriculture or home gardening. This is the case in many cities and suburbs in the United States, where open or unused spaces are \"not generally a result of a positive decision to leave room for some use, but rather is an expression of a pastoral aesthetic norm that prizes spacious lawns and the zoning restrictions and neighborhood covenants that give these norms the force of law.\"\nIn urban and suburban spaces, growing food in front yards and parking strips can not only provide fresh produce but also be a source of neighborhood pride. While converting lawn space into strictly utilitarian farms is not common, incorporating edible plants into front yards with sustainable and aesthetically pleasing design is of growing interest in the United States."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722355808.1071, "task_type": "retrieval", "type": "rightvote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "5f22cd5fed984a3094edf1dc060a61ff", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Which test was devised to determine whether robots can think?", "0_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"]], "0_corpus": "wikipedia", "1_conv_id": "9ef794b8e63b463cb3b5ed634b80ed97", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_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)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722355864.8668, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "cf328bd4ce6842b3b75994dc47cf36e3", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "0_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "0_corpus": "arxiv", "1_conv_id": "e612be7a0fc542c981bdede00d9d4ced", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "1_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "1_corpus": "arxiv"}

data/retrieval_side_by_side-dd51f1d2-bf9d-4e42-9a72-5b30dd2bd338.jsonl

@@ -1,3 +1,3 @@
 {"tstamp": 1722357077.601, "task_type": "retrieval", "type": "share", "models": ["### Model A: embed-english-v3.0", "### Model B: Salesforce/SFR-Embedding-2_R"], "ip": "", "0_conv_id": "7c2e24197d4b477499b1f7763539f4a0", "0_model_name": "embed-english-v3.0", "0_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "0_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."]], "0_corpus": "arxiv", "1_conv_id": "d0f4393f2f3b4313a37f5552e2b3c499", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "1_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."]], "1_corpus": "arxiv"}
-{"tstamp": 1722357447.9143, "task_type": "retrieval", "type": "bothbadvote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "a35638792d8b4ff58a9d08bf158b6f19", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Kuhmo", "0_output": [["Kuhmo", "Kajaani\n\nThere is a mosque in the town, which is the center of the regional Kainuu Islamic community, which in 2013 had 174 members. Eino Leno House, containing a cafe, was built in 1978 to commemorate famous poet Eino Leino, a native of the town. Keisarintalli, a wooden stable, was used as a boarding house for Tsar Alexander I of Russia during his tour of Finland in 1819. Also of note is the Urho Kekkonen Memorial,  dedicated to the eighth Finnish president, Urho Kekkonen. The  high monument was carved by sculptor Pekka Kauhanen and was unveiled on 3 September 1990, exactly 90 years after Kekkonen's birth.\nCulture\nThe Kajaani Town Theatre, established in 1969, puts on plays and concerts, typically seen by around 200 people. The Art Museum of Kajaani (), located in a building that was a police station, which is connected to Kajaani's former city hall. was founded in 1993 and centers on Finnish modern art. Kainuu Museum ()  displays information related to the tar industry, the Kalevala (the national epic of Finland) and author Elias Lönnrot and others on the ground floor, and regularly hosts temporary exhibitions upstairs.\nThe most notable annual cultural events in Kajaani are Kajaanin Runoviikko (The Kajaani Poetry Week), originally known as Sana ja Sävel, and Kainuun JazzKevät (The Kainuu Jazz Spring). Kajaani Orchestra began in the 1950s, and was expanded when the Kuopio garrison band moved to Kajaani in 1963.\nRadio Kajaus, one of Finland's oldest local radios, began operations in Kajaani in 1989, and remains one of the few completely independent radio stations in Finland. Kainuu Radio, which belongs to the Yle, broadcasts Monday to Friday from 6.30 to 17.00."]], "0_corpus": "wikipedia", "1_conv_id": "1f75ec295ed747d59915e71ffd709ea4", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Kuhmo", "1_output": [["Kuhmo", "Kumho Asiana Group\n\nKumho Asiana Group is a large South Korean Chaebol (conglomerate), with subsidiaries in the construction, electronics, IT, leisure, logistics, manufacturing, tourism and transportation sectors. The group is headquartered at the Kumho Asiana Main Tower in Sinmunno 1-ga, Jongno-gu, Seoul, South Korea. As of 2014, the largest shareholder is Park Sam-koo, the third son of the company's founder, who stepped down as CEO in 2010.\nHistory\nAfter World War II, Park In-chon began a taxi service, based out of Geumnamno in Seo-gu, Gwangju. By the 1950s, operations had expanded to include bus and coach services, operating as Gwangju Passenger Service (today, Kumho Buslines)."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722357447.9143, "task_type": "retrieval", "type": "bothbadvote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "a35638792d8b4ff58a9d08bf158b6f19", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Kuhmo", "0_output": [["Kuhmo", "Kajaani\n\nThere is a mosque in the town, which is the center of the regional Kainuu Islamic community, which in 2013 had 174 members. Eino Leno House, containing a cafe, was built in 1978 to commemorate famous poet Eino Leino, a native of the town. Keisarintalli, a wooden stable, was used as a boarding house for Tsar Alexander I of Russia during his tour of Finland in 1819. Also of note is the Urho Kekkonen Memorial,  dedicated to the eighth Finnish president, Urho Kekkonen. The  high monument was carved by sculptor Pekka Kauhanen and was unveiled on 3 September 1990, exactly 90 years after Kekkonen's birth.\nCulture\nThe Kajaani Town Theatre, established in 1969, puts on plays and concerts, typically seen by around 200 people. The Art Museum of Kajaani (), located in a building that was a police station, which is connected to Kajaani's former city hall. was founded in 1993 and centers on Finnish modern art. Kainuu Museum ()  displays information related to the tar industry, the Kalevala (the national epic of Finland) and author Elias L\u00f6nnrot and others on the ground floor, and regularly hosts temporary exhibitions upstairs.\nThe most notable annual cultural events in Kajaani are Kajaanin Runoviikko (The Kajaani Poetry Week), originally known as Sana ja S\u00e4vel, and Kainuun JazzKev\u00e4t (The Kainuu Jazz Spring). Kajaani Orchestra began in the 1950s, and was expanded when the Kuopio garrison band moved to Kajaani in 1963.\nRadio Kajaus, one of Finland's oldest local radios, began operations in Kajaani in 1989, and remains one of the few completely independent radio stations in Finland. Kainuu Radio, which belongs to the Yle, broadcasts Monday to Friday from 6.30 to 17.00."]], "0_corpus": "wikipedia", "1_conv_id": "1f75ec295ed747d59915e71ffd709ea4", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Kuhmo", "1_output": [["Kuhmo", "Kumho Asiana Group\n\nKumho Asiana Group is a large South Korean Chaebol (conglomerate), with subsidiaries in the construction, electronics, IT, leisure, logistics, manufacturing, tourism and transportation sectors. The group is headquartered at the Kumho Asiana Main Tower in Sinmunno 1-ga, Jongno-gu, Seoul, South Korea. As of 2014, the largest shareholder is Park Sam-koo, the third son of the company's founder, who stepped down as CEO in 2010.\nHistory\nAfter World War II, Park In-chon began a taxi service, based out of Geumnamno in Seo-gu, Gwangju. By the 1950s, operations had expanded to include bus and coach services, operating as Gwangju Passenger Service (today, Kumho Buslines)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722361147.9222, "task_type": "retrieval", "type": "rightvote", "models": ["voyage-multilingual-2", "BM25"], "ip": "", "0_conv_id": "4536c1b0361243b2a45b4d61de33f9d3", "0_model_name": "voyage-multilingual-2", "0_prompt": "What is inpars?", "0_output": [["What is inpars?", "Title: Leveraging Code to Improve In-context Learning for Semantic Parsing\n\nAbstract: In-context learning (ICL) is an appealing approach for semantic parsing due to its few-shot nature and improved generalization. However, learning to parse to rare domain-specific languages (DSLs) from just a few demonstrations is challenging, limiting the performance of even the most capable LLMs. In this work, we improve the effectiveness of ICL for semantic parsing by (1) using general-purpose programming languages such as Python instead of DSLs, and (2) augmenting prompts with a structured domain description that includes, e.g., the available classes and functions. We show that both these changes significantly improve accuracy across three popular datasets. Combined, they lead to dramatic improvements (e.g. 7.9% to 66.5% on SMCalFlow compositional split), nearly closing the performance gap between easier i.i.d.\\ and harder compositional splits when used with a strong model, and reducing the need for a large number of demonstrations. We find that the resemblance of the target parse language to general-purpose code is a more important factor than the language's popularity in pre-training corpora. Our findings provide an improved methodology for building semantic parsers in the modern context of ICL with LLMs."]], "0_corpus": "arxiv", "1_conv_id": "1f1e888fdbea476ba74cd6b0e0b99c4c", "1_model_name": "BM25", "1_prompt": "What is inpars?", "1_output": [["What is inpars?", "Title: InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval\n\nAbstract: Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents. These synthetic query-document pairs can then be used to train a retriever. However, InPars and, more recently, Promptagator, rely on proprietary LLMs such as GPT-3 and FLAN to generate such datasets. In this work we introduce InPars-v2, a dataset generator that uses open-source LLMs and existing powerful rerankers to select synthetic query-document pairs for training. A simple BM25 retrieval pipeline followed by a monoT5 reranker finetuned on InPars-v2 data achieves new state-of-the-art results on the BEIR benchmark. To allow researchers to further improve our method, we open source the code, synthetic data, and finetuned models: https://github.com/zetaalphavector/inPars/tree/master/tpu"]], "1_corpus": "arxiv"}

data/retrieval_side_by_side-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

@@ -2,5 +2,5 @@
 {"tstamp": 1722405395.3689, "task_type": "retrieval", "type": "rightvote", "models": ["Alibaba-NLP/gte-Qwen2-7B-instruct", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "5eb5421a209846b8aab73cddb8a8c999", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Scene detection from video", "0_output": [["Scene detection from video", "Title: Dealing with sequences in the RGBDT space\n\nAbstract: Most of the current research in computer vision is focused on working with single images without taking in account temporal information. We present a probabilistic non-parametric model that mixes multiple information cues from devices to segment regions that contain moving objects in image sequences. We prepared an experimental setup to show the importance of using previous information for obtaining an accurate segmentation result, using a novel dataset that provides sequences in the RGBDT space. We label the detected regions ts with a state-of-the-art human detector. Each one of the detected regions is at least marked as human once."]], "0_corpus": "arxiv", "1_conv_id": "39376a868a284ddebf65e0e4719b5d4c", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Scene detection from video", "1_output": [["Scene detection from video", "Title: Learnable Optimal Sequential Grouping for Video Scene Detection\n\nAbstract: Video scene detection is the task of dividing videos into temporal semantic chapters. This is an important preliminary step before attempting to analyze heterogeneous video content. Recently, Optimal Sequential Grouping (OSG) was proposed as a powerful unsupervised solution to solve a formulation of the video scene detection problem. In this work, we extend the capabilities of OSG to the learning regime. By giving the capability to both learn from examples and leverage a robust optimization formulation, we can boost performance and enhance the versatility of the technology. We present a comprehensive analysis of incorporating OSG into deep learning neural networks under various configurations. These configurations include learning an embedding in a straight-forward manner, a tailored loss designed to guide the solution of OSG, and an integrated model where the learning is performed through the OSG pipeline. With thorough evaluation and analysis, we assess the benefits and behavior of the various configurations, and show that our learnable OSG approach exhibits desirable behavior and enhanced performance compared to the state of the art."]], "1_corpus": "arxiv"}
 {"tstamp": 1722410176.2348, "task_type": "retrieval", "type": "tievote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "f7cba7820d484a6e9a535e25f2e59795", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "what is a cuticle", "0_output": [["what is a cuticle", "Cuticle\n\nA cuticle (), or cuticula, is any of a variety of tough but flexible, non-mineral outer coverings of an organism, or parts of an organism, that provide protection. Various types of \"cuticle\" are non-homologous, differing in their origin, structure, function, and chemical composition.\nHuman anatomy\nIn human anatomy, \"cuticle\" can refer to several structures, but it is used in general parlance, and even by medical professionals, to refer to the thickened layer of skin surrounding fingernails and toenails (the eponychium), and to refer to the superficial layer of overlapping cells covering the hair shaft (cuticula pili), consisting of dead cells, that locks the hair into its follicle. It can also be used as a synonym for the epidermis, the outer layer of skin.\nCuticle of invertebrates\nIn zoology, the invertebrate cuticle or cuticula is a multi-layered structure outside the epidermis of many invertebrates, notably arthropods and roundworms, in which it forms an exoskeleton (see arthropod exoskeleton).\nThe main structural components of the nematode cuticle are proteins, highly cross-linked collagens and specialised insoluble proteins known as \"cuticlins\", together with glycoproteins and lipids."]], "0_corpus": "wikipedia", "1_conv_id": "502b1819fb7448ee91f9932a135d2bc2", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "what is a cuticle", "1_output": [["what is a cuticle", "Cuticle\n\nA cuticle (), or cuticula, is any of a variety of tough but flexible, non-mineral outer coverings of an organism, or parts of an organism, that provide protection. Various types of \"cuticle\" are non-homologous, differing in their origin, structure, function, and chemical composition.\nHuman anatomy\nIn human anatomy, \"cuticle\" can refer to several structures, but it is used in general parlance, and even by medical professionals, to refer to the thickened layer of skin surrounding fingernails and toenails (the eponychium), and to refer to the superficial layer of overlapping cells covering the hair shaft (cuticula pili), consisting of dead cells, that locks the hair into its follicle. It can also be used as a synonym for the epidermis, the outer layer of skin.\nCuticle of invertebrates\nIn zoology, the invertebrate cuticle or cuticula is a multi-layered structure outside the epidermis of many invertebrates, notably arthropods and roundworms, in which it forms an exoskeleton (see arthropod exoskeleton).\nThe main structural components of the nematode cuticle are proteins, highly cross-linked collagens and specialised insoluble proteins known as \"cuticlins\", together with glycoproteins and lipids."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722413597.7439, "task_type": "retrieval", "type": "rightvote", "models": ["jinaai/jina-embeddings-v2-base-en", "voyage-multilingual-2"], "ip": "", "0_conv_id": "c46babe33e3c40fba0b1cc820ca8384a", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "0_corpus": "wikipedia", "1_conv_id": "b5e90ef4f21148029ef2fc7495e295fb", "1_model_name": "BM25", "1_prompt": "Which test was devised to determine whether robots can think?", "1_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.\""]], "1_corpus": "wikipedia"}
-{"tstamp": 1722432375.9194, "task_type": "retrieval", "type": "tievote", "models": ["jinaai/jina-embeddings-v2-base-en", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "16bc3d67d1af469a9cc0ee66d974af5a", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "What is cosine similarity?", "0_output": [["What is cosine similarity?", "Cosine similarity\n\nIn data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval  For example, two proportional vectors have a cosine similarity of 1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of -1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in .\nFor example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and independently of the length of the documents.\nThe technique is also used to measure cohesion within clusters in the field of data mining.\nOne advantage of cosine similarity is its low complexity, especially for sparse vectors: only the non-zero coordinates need to be considered.\nOther names for cosine similarity include Orchini similarity and Tucker coefficient of congruence; the Otsuka–Ochiai similarity (see below) is cosine similarity applied to binary data."]], "0_corpus": "wikipedia", "1_conv_id": "2ee5241754c8451bb390c3a4c4064166", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "What is cosine similarity?", "1_output": [["What is cosine similarity?", "Cosine similarity\n\nIn data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval  For example, two proportional vectors have a cosine similarity of 1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of -1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in .\nFor example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and independently of the length of the documents.\nThe technique is also used to measure cohesion within clusters in the field of data mining.\nOne advantage of cosine similarity is its low complexity, especially for sparse vectors: only the non-zero coordinates need to be considered.\nOther names for cosine similarity include Orchini similarity and Tucker coefficient of congruence; the Otsuka–Ochiai similarity (see below) is cosine similarity applied to binary data."]], "1_corpus": "wikipedia"}
-{"tstamp": 1722434050.2945, "task_type": "retrieval", "type": "leftvote", "models": ["jinaai/jina-embeddings-v2-base-en", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "1163ab9a31844f4cab143efdd5a4f55e", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "What's the first probe we sent to space?", "0_output": [["What's the first probe we sent to space?", "Interstellar probe\n\nProject Orion (1958–1965) Project Orion was a proposed nuclear pulse propulsion craft that would have used fission or fusion bombs to apply motive force. The design was studied during the 1950s and 1960s in the United States of America, with one variant of the craft capable of interstellar travel.Bracewell probe (1960)\nInterstellar communication via a probe, as opposed to sending an electromagnetic signal.Sanger Photon Rocket (1950s-1964)\nEugene Sanger proposed a spacecraft powered by antimatter in the 1950s. Thrust was intended to come from reflected gamma-rays produced by electron-positron annihilation.\nEnzmann starship (1964/1973)\nProposed by 1964 and examined in an October 1973 issue of Analog, the Enzmann Starship proposed using a 12,000 ton ball of frozen deuterium to power thermonuclear powered pulse propulsion. About twice as long as the Empire State Building and assembled in-orbit, the spacecraft was part of a larger project preceded by large interstellar probes and telescopic observation of target star systems.\nProject Daedalus (1973–1978)\nProject Daedalus was a proposed nuclear pulse propulsion craft that used inertial confinement fusion of small pellets within a magnetic field nozzle to provide motive force. The design was studied during the 1970s by the British Interplanetary Society, and was meant to flyby Barnard's Star in under a century from launch. Plans included mining Helium-3 from Jupiter and a pre-launch mass of over 50 thousand metric tonnes from orbit."]], "0_corpus": "wikipedia", "1_conv_id": "750a9e22a9a2458f9764755b4068b5b6", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "What's the first probe we sent to space?", "1_output": [["What's the first probe we sent to space?", "Voyager 1\n\nVoyager 1 is a space probe launched by NASA on September 5, 1977, as part of the Voyager program to study the outer Solar System and the interstellar space beyond the Sun's heliosphere. It was launched 16 days after its twin, Voyager 2. It communicates through the NASA Deep Space Network (DSN) to receive routine commands and to transmit data to Earth. Real-time distance and velocity data are provided by NASA and JPL. At a distance of  from Earth , it is the most distant human-made object from Earth. The probe made flybys of Jupiter, Saturn, and Saturn's largest moon, Titan. NASA had a choice of either doing a Pluto or Titan flyby; exploration of the moon took priority because it was known to have a substantial atmosphere. Voyager 1 studied the weather, magnetic fields, and rings of the two gas giants and was the first probe to provide detailed images of their moons."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722432375.9194, "task_type": "retrieval", "type": "tievote", "models": ["jinaai/jina-embeddings-v2-base-en", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "16bc3d67d1af469a9cc0ee66d974af5a", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "What is cosine similarity?", "0_output": [["What is cosine similarity?", "Cosine similarity\n\nIn data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval  For example, two proportional vectors have a cosine similarity of 1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of -1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in .\nFor example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and independently of the length of the documents.\nThe technique is also used to measure cohesion within clusters in the field of data mining.\nOne advantage of cosine similarity is its low complexity, especially for sparse vectors: only the non-zero coordinates need to be considered.\nOther names for cosine similarity include Orchini similarity and Tucker coefficient of congruence; the Otsuka\u2013Ochiai similarity (see below) is cosine similarity applied to binary data."]], "0_corpus": "wikipedia", "1_conv_id": "2ee5241754c8451bb390c3a4c4064166", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "What is cosine similarity?", "1_output": [["What is cosine similarity?", "Cosine similarity\n\nIn data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval  For example, two proportional vectors have a cosine similarity of 1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of -1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in .\nFor example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and independently of the length of the documents.\nThe technique is also used to measure cohesion within clusters in the field of data mining.\nOne advantage of cosine similarity is its low complexity, especially for sparse vectors: only the non-zero coordinates need to be considered.\nOther names for cosine similarity include Orchini similarity and Tucker coefficient of congruence; the Otsuka\u2013Ochiai similarity (see below) is cosine similarity applied to binary data."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722434050.2945, "task_type": "retrieval", "type": "leftvote", "models": ["jinaai/jina-embeddings-v2-base-en", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "1163ab9a31844f4cab143efdd5a4f55e", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "What's the first probe we sent to space?", "0_output": [["What's the first probe we sent to space?", "Interstellar probe\n\nProject Orion (1958\u20131965) Project Orion was a proposed nuclear pulse propulsion craft that would have used fission or fusion bombs to apply motive force. The design was studied during the 1950s and 1960s in the United States of America, with one variant of the craft capable of interstellar travel.Bracewell probe (1960)\nInterstellar communication via a probe, as opposed to sending an electromagnetic signal.Sanger Photon Rocket (1950s-1964)\nEugene Sanger proposed a spacecraft powered by antimatter in the 1950s. Thrust was intended to come from reflected gamma-rays produced by electron-positron annihilation.\nEnzmann starship (1964/1973)\nProposed by 1964 and examined in an October 1973 issue of Analog, the Enzmann Starship proposed using a 12,000 ton ball of frozen deuterium to power thermonuclear powered pulse propulsion. About twice as long as the Empire State Building and assembled in-orbit, the spacecraft was part of a larger project preceded by large interstellar probes and telescopic observation of target star systems.\nProject Daedalus (1973\u20131978)\nProject Daedalus was a proposed nuclear pulse propulsion craft that used inertial confinement fusion of small pellets within a magnetic field nozzle to provide motive force. The design was studied during the 1970s by the British Interplanetary Society, and was meant to flyby Barnard's Star in under a century from launch. Plans included mining Helium-3 from Jupiter and a pre-launch mass of over 50 thousand metric tonnes from orbit."]], "0_corpus": "wikipedia", "1_conv_id": "750a9e22a9a2458f9764755b4068b5b6", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "What's the first probe we sent to space?", "1_output": [["What's the first probe we sent to space?", "Voyager 1\n\nVoyager 1 is a space probe launched by NASA on September 5, 1977, as part of the Voyager program to study the outer Solar System and the interstellar space beyond the Sun's heliosphere. It was launched 16 days after its twin, Voyager 2. It communicates through the NASA Deep Space Network (DSN) to receive routine commands and to transmit data to Earth. Real-time distance and velocity data are provided by NASA and JPL. At a distance of  from Earth , it is the most distant human-made object from Earth. The probe made flybys of Jupiter, Saturn, and Saturn's largest moon, Titan. NASA had a choice of either doing a Pluto or Titan flyby; exploration of the moon took priority because it was known to have a substantial atmosphere. Voyager 1 studied the weather, magnetic fields, and rings of the two gas giants and was the first probe to provide detailed images of their moons."]], "1_corpus": "wikipedia"}

data/retrieval_single_choice-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -1,2 +1 @@
 {"tstamp": 1722378514.0602, "task_type": "retrieval", "type": "upvote", "models": "mixedbread-ai/mxbai-embed-large-v1", "ip": "", "conv_id": "cbbfd79357164a9cba29392e970560ba", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Please find me the paper on training code large language models to follow instructions via git commits.", "output": [["Please find me the paper on training code large language models to follow instructions via git commits.", "Title: CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model\n\nAbstract: Commit message is a document that summarizes source code changes in natural language. A good commit message clearly shows the source code changes, so this enhances collaboration between developers. Therefore, our work is to develop a model that automatically writes the commit message. To this end, we release 345K datasets consisting of code modification and commit messages in six programming languages (Python, PHP, Go, Java, JavaScript, and Ruby). Similar to the neural machine translation (NMT) model, using our dataset, we feed the code modification to the encoder input and the commit message to the decoder input and measure the result of the generated commit message with BLEU-4. Also, we propose the following two training methods to improve the result of generating the commit message: (1) A method of preprocessing the input to feed the code modification to the encoder input. (2) A method that uses an initial weight suitable for the code domain to reduce the gap in contextual representation between programming language (PL) and natural language (NL). Training code, dataset, and pre-trained weights are available at https://github.com/graykode/commit-autosuggestions"]], "corpus": "arxiv"}
-{"tstamp": 1722378529.1411, "task_type": "retrieval", "type": "downvote", "models": "nomic-ai/nomic-embed-text-v1.5", "ip": "", "conv_id": "0dd499d0e9ec4382900ea19b243fc32c", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Please find me the paper on training code large language models to follow instructions via git commits.", "output": [["Please find me the paper on training code large language models to follow instructions via git commits.", "Title: Is the Commercial Mass Media Necessary, or Even Desirable, for Liberal Democracy?\n\nAbstract: Is a commercial mass media, dependent on the market for its sustenance, necessary, or even desirable, for liberal democracy? Yochai Benkler has argued that a decentralized, peer-to-peer system of communications and information is both possible with digital technology and preferable to a system based on commercial mass media. He has contended in fact that the presence of politically powerful, copyright-rich mass media imposes significant barriers to the development of peer-to-peer information-sharing networks. In contrast, I have argued that the commercial mass media play an important, and perhaps even vital, role in liberal democracy by galvanizing public opinion, serving as a watchdog against government and corporate wrongdoing, agenda-setting (which enables public discourse), and serving as a relatively trustworthy source of information. This paper seeks to push the ball forward on this issue. It first isolates and enumerates the contributions that the commercial mass media are said to make towards liberal democracy. It then briefly assesses the extent to which the commercial mass media actually fulfills these constitutive functions. It then asks whether alternative institutions might serve some or all of these functions just as well or better. In so doing, it looks both to the past and the future. First, it examines the political party-supported press that thrived in the United States through much of the 19th century. Second, it examines government-funded mass media. Third, it looks, skeptically, at possibilities for peer-to-peer sharing of information and opinion in the digital network environment. I conclude that, despite the weaknesses of commercial mass media, an information policy suitable to liberal democracy should include a plurality of types of voices, including commercial mass media."]], "corpus": "arxiv"}

data/sts_battle-0325b067-7cac-407c-9ffa-76d99f10c9f7.jsonl

@@ -1,8 +1,4 @@
 {"tstamp": 1722262971.6058, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "562b48d88d3840b282ed0bf3c590341a", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_txt0": "hello", "0_txt1": "good morning", "0_txt2": "\u65e9\u4e0a\u597d", "0_output": "", "1_conv_id": "809eb6825e7d4713bc192f9b915fa4e1", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_txt0": "hello", "1_txt1": "good morning", "1_txt2": "\u65e9\u4e0a\u597d", "1_output": ""}
-{"tstamp": 1722262987.4768, "task_type": "sts", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "bc6ecee8d5ac40e68b9dacd2a20cfe23", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_txt0": "hello", "0_txt1": "good morning", "0_txt2": "\u65e9\u4e0a\u597d", "0_output": "", "1_conv_id": "1f510cd7431842a39d227a709709eea1", "1_model_name": "BAAI/bge-large-en-v1.5", "1_txt0": "hello", "1_txt1": "good morning", "1_txt2": "\u65e9\u4e0a\u597d", "1_output": ""}
-{"tstamp": 1722262999.051, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "93a239559da247ada73b2ae014fe136d", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_txt0": "hello", "0_txt1": "good morning", "0_txt2": "\u65e9\u4e0a\u597d", "0_output": "", "1_conv_id": "197a9a12a7d1486da002fd1fbc818f9e", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_txt0": "hello", "1_txt1": "good morning", "1_txt2": "\u65e9\u4e0a\u597d", "1_output": ""}
-{"tstamp": 1722263017.4774, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "71be8fa185fd4523b9ecfa7d61421b75", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_txt0": "Right leg in a brace, a young boy lies on the couch while using his cellphone.", "0_txt1": "The right leg is in a brace", "0_txt2": "The boy is walking around", "0_output": "", "1_conv_id": "7803b4ca22d14bd5b6cac9190af28a6e", "1_model_name": "voyage-multilingual-2", "1_txt0": "Right leg in a brace, a young boy lies on the couch while using his cellphone.", "1_txt1": "The right leg is in a brace", "1_txt2": "The boy is walking around", "1_output": ""}
 {"tstamp": 1722263131.0162, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "da247cb7f544491ea524047da28e0d8b", "0_model_name": "GritLM/GritLM-7B", "0_txt0": "People at an outdoor market", "0_txt1": "A black mother in an orange dress with her two little boys and husband at an outdoor market stand.", "0_txt2": "A family skeet shooting", "0_output": "", "1_conv_id": "9f7c97678ebf44e7814b60495025cff7", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_txt0": "People at an outdoor market", "1_txt1": "A black mother in an orange dress with her two little boys and husband at an outdoor market stand.", "1_txt2": "A family skeet shooting", "1_output": ""}
 {"tstamp": 1722263165.1325, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "644c59f0578b48068f1228870ba757b0", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_txt0": "The dog likes to catch baseballs.", "0_txt1": "a puppy about to jump to intercept a yellow ball", "0_txt2": "The dog is trying to catch a tennis ball.", "0_output": "", "1_conv_id": "43f756f9250e4c2f8f7bd1eaffe3eaf2", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_txt0": "The dog likes to catch baseballs.", "1_txt1": "a puppy about to jump to intercept a yellow ball", "1_txt2": "The dog is trying to catch a tennis ball.", "1_output": ""}
 {"tstamp": 1722263197.3786, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1be218a193ec45689faeb8ff9318688a", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_txt0": "People are shopping.", "0_txt1": "Numerous customers browsing for produce in a market", "0_txt2": "People are showering.", "0_output": "", "1_conv_id": "57ea514b971f492da35c07bdcd7dd4aa", "1_model_name": "BAAI/bge-large-en-v1.5", "1_txt0": "People are shopping.", "1_txt1": "Numerous customers browsing for produce in a market", "1_txt2": "People are showering.", "1_output": ""}
-{"tstamp": 1722263639.6325, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1af541ee9efa4e2da2bd075330225769", "0_model_name": "embed-english-v3.0", "0_txt0": "She saw a bright star in the sky.", "0_txt1": "She saw a bright star at the awards show.", "0_txt2": "She observed a luminous celestial object.", "0_output": "", "1_conv_id": "c7a30e59173240848b00b8b4d734f2c1", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_txt0": "She saw a bright star in the sky.", "1_txt1": "She saw a bright star at the awards show.", "1_txt2": "She observed a luminous celestial object.", "1_output": ""}

data/sts_battle-1fa504af-967a-4ff8-a6ff-4abf5fbdd1c5.jsonl

@@ -1,6 +1,5 @@
 {"tstamp": 1722267323.0629, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "731a92ffa59f49e2baead73762cd8e1d", "0_model_name": "GritLM/GritLM-7B", "0_txt0": "She saw a bright star in the sky.", "0_txt1": "She saw a bright star at the awards show.", "0_txt2": "She observed a luminous celestial object.", "0_output": "", "1_conv_id": "ed3fecd923bb4d28a6f0cbdbcc6c442b", "1_model_name": "embed-english-v3.0", "1_txt0": "She saw a bright star in the sky.", "1_txt1": "She saw a bright star at the awards show.", "1_txt2": "She observed a luminous celestial object.", "1_output": ""}
 {"tstamp": 1722267334.5351, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "56e608f6f92e48b58f42131b823a1990", "0_model_name": "text-embedding-3-large", "0_txt0": "She saw a bright star in the sky.", "0_txt1": "She saw a bright star at the awards show.", "0_txt2": "She observed a luminous celestial object.", "0_output": "", "1_conv_id": "b91a77a4c5554e8bba14d59b57dca829", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_txt0": "She saw a bright star in the sky.", "1_txt1": "She saw a bright star at the awards show.", "1_txt2": "She observed a luminous celestial object.", "1_output": ""}
-{"tstamp": 1722267352.2229, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "cb1bd4e129bd4bd0918b2af0df645788", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_txt0": "She saw a bright star in the sky.", "0_txt1": "She saw a bright star at the awards show.", "0_txt2": "She observed a luminous celestial object.", "0_output": "", "1_conv_id": "546fe5b46c6f44d4b7fe268bcd87bb5f", "1_model_name": "embed-english-v3.0", "1_txt0": "She saw a bright star in the sky.", "1_txt1": "She saw a bright star at the awards show.", "1_txt2": "She observed a luminous celestial object.", "1_output": ""}
 {"tstamp": 1722267368.7996, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "06363134483440f6a546b9811fef364f", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_txt0": "The dog likes to catch baseballs.", "0_txt1": "a puppy about to jump to intercept a yellow ball", "0_txt2": "The dog is trying to catch a tennis ball.", "0_output": "", "1_conv_id": "81a1a75e388c410ba4eaf298c9b2c43b", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_txt0": "The dog likes to catch baseballs.", "1_txt1": "a puppy about to jump to intercept a yellow ball", "1_txt2": "The dog is trying to catch a tennis ball.", "1_output": ""}
 {"tstamp": 1722267402.8543, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "daff31ca24114a54b120a0d0b81e59b0", "0_model_name": "text-embedding-3-large", "0_txt0": "There's a red bus making a left turn into a traffic circle that has a sprinkler system.", "0_txt1": "A red bus making a turn", "0_txt2": "A red bus backing up into a spot", "0_output": "", "1_conv_id": "4e89d9c57add47ee9b46d4e181f64155", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_txt0": "There's a red bus making a left turn into a traffic circle that has a sprinkler system.", "1_txt1": "A red bus making a turn", "1_txt2": "A red bus backing up into a spot", "1_output": ""}
 {"tstamp": 1722267760.4127, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "517b7886203547bfb8f8d483cde6c8c8", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_txt0": "The girl likes to catch baseballs.", "0_txt1": "a puppy about to jump to intercept a yellow ball", "0_txt2": "The dog is trying to catch a tennis ball.", "0_output": "", "1_conv_id": "9b57037fc1314c4e86fd722796a7a6ec", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_txt0": "The girl likes to catch baseballs.", "1_txt1": "a puppy about to jump to intercept a yellow ball", "1_txt2": "The dog is trying to catch a tennis ball.", "1_output": ""}

data/sts_battle-54036c49-65ef-456e-9e49-4434c3385d0a.jsonl

@@ -1,28 +1,20 @@
-{"tstamp": 1722270694.8689, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "2010cb8b73f24327a25dac45b6991eaa", "0_model_name": "text-embedding-004", "0_txt0": "She saw a bright star in the sky.", "0_txt1": "She saw a bright star at the awards show.", "0_txt2": "She observed a luminous celestial object.", "0_output": "", "1_conv_id": "4c7a0f96271c43ca9448b72012fb34b7", "1_model_name": "embed-english-v3.0", "1_txt0": "She saw a bright star in the sky.", "1_txt1": "She saw a bright star at the awards show.", "1_txt2": "She observed a luminous celestial object.", "1_output": ""}
 {"tstamp": 1722270709.3467, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c3adf9d33522438e9d42ac08b324a573", "0_model_name": "BAAI/bge-large-en-v1.5", "0_txt0": "She saw a bright star in the sky.", "0_txt1": "She saw a bright star at the awards show.", "0_txt2": "She observed a luminous celestial object.", "0_output": "", "1_conv_id": "a5894d7304724d89924d74db0d289881", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_txt0": "She saw a bright star in the sky.", "1_txt1": "She saw a bright star at the awards show.", "1_txt2": "She observed a luminous celestial object.", "1_output": ""}
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data/sts_battle-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -1,3 +1,3 @@
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