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1,735,881,028.3917 | clustering | rightvote | [
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] | 4 | 3D (press for 2D) | PCA | KMeans | e6d47ea35c6e42c38873a1d3636e1cb9 | jinaai/jina-embeddings-v2-base-en | [
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1,735,881,103.9453 | clustering | leftvote | [
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] | c5aaa9763b114424a24a773359d56059 | intfloat/e5-mistral-7b-instruct | [
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"Monopoly",
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] | 4 | 3D (press for 2D) | PCA | KMeans |
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1,735,933,264.0071 | clustering | rightvote | [
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1,722,263,906.0355 | clustering | tievote | [
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] | 53aecd7e5dc24a6cab610e361d69ec88 | voyage-multilingual-2 | [
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1,723,773,356.776 | clustering | rightvote | [
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"Darth Vader",
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] | 2 | 3D (press for 2D) | PCA | KMeans |
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1,723,773,582.4108 | clustering | leftvote | [
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] | fc182aada5a1495fac383e3650a06fa7 | mixedbread-ai/mxbai-embed-large-v1 | [
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1,723,791,451.7309 | clustering | rightvote | [
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] | eeb16bc223734184aff49ba1cd073ab1 | sentence-transformers/all-MiniLM-L6-v2 | [
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1,722,281,965.6179 | clustering | tievote | [
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] | 5accf948c7ac40c89eeb322a4e13bf62 | intfloat/e5-mistral-7b-instruct | [
"Shanghai",
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"Shanghai",
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1,722,282,005.3598 | clustering | tievote | [
"",
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] | f8293714822c4dd3a8248660500f9035 | intfloat/e5-mistral-7b-instruct | [
"Pikachu",
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"Legolas",
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"Donald Duck",
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] | 4 | 3D (press for 2D) | PCA | KMeans | 336180d1bf524d83a68ce1cc38470459 | voyage-multilingual-2 | [
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1,722,282,151.7917 | clustering | leftvote | [
"",
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] | 2a1a8ba50b6d4bc0b2ba7c0abd8dba81 | intfloat/e5-mistral-7b-instruct | [
"Transits – Neptune conjunct the Moon | LUA ASTROLOGY",
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] | 3 | 3D (press for 2D) | PCA | KMeans | 1636fa4a9c774083b13a62e6f18461cc | voyage-multilingual-2 | [
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1,722,281,821.2839 | clustering | rightvote | [
"",
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] | d20f08ce773f4046a5db3a1242607917 | mixedbread-ai/mxbai-embed-large-v1 | [
"Shanghai",
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] | 2 | 3D (press for 2D) | PCA | KMeans | 5a39b6d2ca964e049316b552f24f013d | voyage-multilingual-2 | [
"Shanghai",
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"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans |
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1,722,615,202.3598 | clustering | leftvote | [
"",
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] | 687d9008c4e74bd99523eaad66d98dd1 | Salesforce/SFR-Embedding-2_R | [
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"lily",
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"daisy",
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"diamond"
] | 4 | 3D (press for 2D) | PCA | KMeans | 808ec06d5fdd4e0dac8ca639e758e2de | sentence-transformers/all-MiniLM-L6-v2 | [
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1,722,615,253.2679 | clustering | rightvote | [
"",
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] | c867408bf4e849d2b03c0b7fda95f765 | embed-english-v3.0 | [
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"apple",
"brioche",
"rye",
"sourdough",
"pumpernickel",
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"baguette"
] | 5 | 3D (press for 2D) | PCA | KMeans | 862137c66ef74f9995b59116deab7628 | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"summer",
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] | 5 | 3D (press for 2D) | PCA | KMeans |
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1,722,615,451.8371 | clustering | leftvote | [
"",
""
] | f6d62dfa5d50447fb621777510c20fb8 | GritLM/GritLM-7B | [
"onomatopoeia",
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"LG",
"Huawei",
"Xiaomi",
"OnePlus",
"B",
"O",
"D",
"E",
"B12",
"K",
"C"
] | 4 | 3D (press for 2D) | PCA | KMeans | 09cbb0e5b96b4a0d88b03a6e08442aca | intfloat/multilingual-e5-large-instruct | [
"onomatopoeia",
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"B",
"O",
"D",
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"K",
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] | 4 | 3D (press for 2D) | PCA | KMeans |
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1,722,615,480.5966 | clustering | leftvote | [
"",
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] | 8585aad143e645cfa703982e110dfd13 | intfloat/e5-mistral-7b-instruct | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans | 1725b26612394f77b5699cf1daae9159 | mixedbread-ai/mxbai-embed-large-v1 | [
"Shanghai",
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"Hangzhou",
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] | 2 | 3D (press for 2D) | PCA | KMeans |
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1,722,628,273.8485 | clustering | rightvote | [
"",
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] | de12e68037454e3b952db869c63fc470 | text-embedding-004 | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans | 4750f9b4bb8e402cbca8a64e2b5a0fe7 | jinaai/jina-embeddings-v2-base-en | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans |
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1,722,645,867.6444 | clustering | rightvote | [
"",
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] | 3a6b26c85989429b81812196dea006e1 | text-embedding-004 | [
"fedora",
"bowler",
"cowboy hat",
"baseball cap",
"beanie",
"free verse",
"limerick"
] | 2 | 3D (press for 2D) | PCA | KMeans | 5564e0cf865d4e3fa0c639be0a168629 | voyage-multilingual-2 | [
"fedora",
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"cowboy hat",
"baseball cap",
"beanie",
"free verse",
"limerick"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,645,945.4401 | clustering | tievote | [
"",
""
] | 5cbc0021ebc94d37806889b1d870c4ba | intfloat/multilingual-e5-large-instruct | [
"trombone",
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"trumpet",
"clarinet",
"flute",
"jiu-jitsu",
"karate",
"muay thai",
"kung fu",
"judo"
] | 2 | 3D (press for 2D) | PCA | KMeans | 2296b102d3504214957dbecf466da6b3 | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"trombone",
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"trumpet",
"clarinet",
"flute",
"jiu-jitsu",
"karate",
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"kung fu",
"judo"
] | 2 | 3D (press for 2D) | PCA | KMeans |
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1,722,654,831.3119 | clustering | rightvote | [
"",
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] | 708abd5d2da849ae8df5483674bf7bdb | voyage-multilingual-2 | [
"Pyramids of Giza",
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"North America",
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"Asia",
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"pancreas",
"lungs",
"kidneys",
"stomach",
"brain",
"epic",
"haiku",
"ballad",
"ode",
"free verse"
] | 4 | 3D (press for 2D) | PCA | KMeans | d4eb6cdae94949c7970dea9cd1f7a4d1 | Salesforce/SFR-Embedding-2_R | [
"Pyramids of Giza",
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"pancreas",
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"epic",
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] | 4 | 3D (press for 2D) | PCA | KMeans |
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1,722,717,837.4978 | clustering | leftvote | [
"",
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] | 9c01a8e7741548d7a77fd22c47e6b4c4 | text-embedding-3-large | [
"beer",
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"rum",
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"whiskey",
"Google Cloud",
"IBM Cloud",
"Oracle Cloud",
"AWS",
"Azure",
"OnePlus",
"Xiaomi",
"Huawei",
"Apple",
"LG",
"Google"
] | 3 | 3D (press for 2D) | PCA | KMeans | 8f2c26a27bb54af790a67e3bbed48277 | mixedbread-ai/mxbai-embed-large-v1 | [
"beer",
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"Google Cloud",
"IBM Cloud",
"Oracle Cloud",
"AWS",
"Azure",
"OnePlus",
"Xiaomi",
"Huawei",
"Apple",
"LG",
"Google"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,776,811.3514 | clustering | rightvote | [
"",
""
] | 09949aaf3024401d9de5f833aad69062 | mixedbread-ai/mxbai-embed-large-v1 | [
"fall",
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"irony",
"onomatopoeia",
"personification",
"simile",
"hyperbole",
"alliteration",
"metaphor",
"compass",
"tent",
"flashlight",
"backpack",
"water filter",
"Aries",
"Cancer",
"Virgo",
"Scorpio",
"Libra"
] | 4 | 3D (press for 2D) | PCA | KMeans | b153421bcf6e4c2ca7829e5ebb31d2d3 | intfloat/e5-mistral-7b-instruct | [
"fall",
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"Aries",
"Cancer",
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"Libra"
] | 4 | 3D (press for 2D) | PCA | KMeans |
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1,722,796,527.251 | clustering | leftvote | [
"",
""
] | c21ad41e3eac4346b64600c67c1f9d61 | intfloat/e5-mistral-7b-instruct | [
"Mesopotamian",
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"rye",
"pumpernickel",
"ciabatta",
"baguette",
"sourdough",
"horror",
"historical fiction",
"romance",
"science fiction"
] | 5 | 3D (press for 2D) | PCA | KMeans | 8b1ff376b4c54750aaf7aa9f96600861 | BAAI/bge-large-en-v1.5 | [
"Mesopotamian",
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] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,796,588.6399 | clustering | tievote | [
"",
""
] | 1d97d5dfe41f49889e482d56861261c9 | GritLM/GritLM-7B | [
"conscientiousness",
"openness",
"agreeableness",
"wheelbarrow",
"rake",
"mystery",
"fantasy",
"hurricane",
"tornado"
] | 4 | 3D (press for 2D) | PCA | KMeans | 10845938a4c843dd988d5ddc2be85725 | intfloat/e5-mistral-7b-instruct | [
"conscientiousness",
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"hurricane",
"tornado"
] | 4 | 3D (press for 2D) | PCA | KMeans |
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1,738,762,098.5218 | clustering | rightvote | [
"",
""
] | 9de46b546bbb4af2a14fc6336586667f | Salesforce/SFR-Embedding-2_R | [
"Mobile Phone",
"Nike",
"Amazon",
"Apple",
"Walmart",
"Rebook",
"Shoes",
"Tenis"
] | 2 | 3D (press for 2D) | PCA | KMeans | 06693da39f9244fe91063e847e649012 | text-embedding-004 | [
"Mobile Phone",
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] | 2 | 3D (press for 2D) | PCA | KMeans |
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1,738,762,129.1828 | clustering | leftvote | [
"",
""
] | 1ab4a792f46541bba432f967e4a34e20 | intfloat/e5-mistral-7b-instruct | [
"gray",
"brunette",
"black",
"white",
"historical fiction",
"romance",
"igneous",
"metamorphic",
"sedimentary",
"pancreas",
"stomach",
"rum",
"vodka",
"tequila"
] | 5 | 3D (press for 2D) | PCA | KMeans | f65ce64c9cb145dcb93e33824a9bacb7 | mixedbread-ai/mxbai-embed-large-v1 | [
"gray",
"brunette",
"black",
"white",
"historical fiction",
"romance",
"igneous",
"metamorphic",
"sedimentary",
"pancreas",
"stomach",
"rum",
"vodka",
"tequila"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,150.046 | clustering | rightvote | [
"",
""
] | 0fc693642edb4a89a0a897d6d6a791c3 | sentence-transformers/all-MiniLM-L6-v2 | [
"Roman",
"Egyptian",
"Greek",
"Mesopotamian",
"Incan",
"diamond",
"sapphire",
"emerald",
"amethyst",
"ruby",
"topaz",
"Pinterest",
"TikTok",
"Instagram"
] | 3 | 3D (press for 2D) | PCA | KMeans | f4f00fb6dca4431fadfede47d893d646 | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"Roman",
"Egyptian",
"Greek",
"Mesopotamian",
"Incan",
"diamond",
"sapphire",
"emerald",
"amethyst",
"ruby",
"topaz",
"Pinterest",
"TikTok",
"Instagram"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,170.9624 | clustering | leftvote | [
"",
""
] | 50ac530cd4f345d28f4eb4942ee59c82 | BAAI/bge-large-en-v1.5 | [
"caldera",
"composite",
"shield",
"cinder cone",
"lava dome",
"clarinet",
"oboe",
"flute",
"cold",
"coastal",
"Atlantic",
"Southern"
] | 4 | 3D (press for 2D) | PCA | KMeans | 9972d5c6bb7d47df87adb6b82f98ede4 | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"caldera",
"composite",
"shield",
"cinder cone",
"lava dome",
"clarinet",
"oboe",
"flute",
"cold",
"coastal",
"Atlantic",
"Southern"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,185.5057 | clustering | rightvote | [
"",
""
] | 25375306f4b147f09b86b40869eaf412 | sentence-transformers/all-MiniLM-L6-v2 | [
"polyethylene",
"PVC",
"nylon",
"polystyrene",
"Mandarin",
"English"
] | 2 | 3D (press for 2D) | PCA | KMeans | a418f1a74bc7403e8e63871b3727d6c8 | GritLM/GritLM-7B | [
"polyethylene",
"PVC",
"nylon",
"polystyrene",
"Mandarin",
"English"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,285.76 | clustering | leftvote | [
"",
""
] | 345f101473824fc9a4cca0bcf227b8b4 | embed-english-v3.0 | [
"winter",
"fall",
"Chinese",
"Hindu",
"Norse",
"Celtic",
"Roman",
"Egyptian",
"Greek",
"B",
"A",
"O",
"polar",
"hot and dry",
"cold",
"coastal",
"semi-arid",
"rum",
"vodka",
"tequila",
"gin"
] | 5 | 3D (press for 2D) | PCA | KMeans | d407eb20615246fdb146caeca40a25f8 | text-embedding-004 | [
"winter",
"fall",
"Chinese",
"Hindu",
"Norse",
"Celtic",
"Roman",
"Egyptian",
"Greek",
"B",
"A",
"O",
"polar",
"hot and dry",
"cold",
"coastal",
"semi-arid",
"rum",
"vodka",
"tequila",
"gin"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,329.6737 | clustering | rightvote | [
"",
""
] | 770d74783c32421389759ebe2c0c7aa1 | jinaai/jina-embeddings-v2-base-en | [
"anchoring bias",
"availability bias",
"hindsight bias",
"drums",
"guitar",
"grilling",
"sautéing",
"leather",
"linen",
"wool",
"hydrogen",
"sodium",
"oxygen",
"nitrogen",
"carbon",
"calcium",
"iron"
] | 5 | 3D (press for 2D) | PCA | KMeans | 62769e095bdc4f1cafe65227fef94efa | Salesforce/SFR-Embedding-2_R | [
"anchoring bias",
"availability bias",
"hindsight bias",
"drums",
"guitar",
"grilling",
"sautéing",
"leather",
"linen",
"wool",
"hydrogen",
"sodium",
"oxygen",
"nitrogen",
"carbon",
"calcium",
"iron"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,395.1063 | clustering | leftvote | [
"",
""
] | e0318ab913004dbb9fe6549139425f00 | Salesforce/SFR-Embedding-2_R | [
"gray",
"blonde",
"brunette",
"redhead",
"haiku",
"ode",
"limerick",
"ballad",
"epic",
"free verse",
"top hat",
"beanie",
"plane",
"convex",
"Arctic",
"Indian",
"Atlantic"
] | 5 | 3D (press for 2D) | PCA | KMeans | c741a54e4c874d8a9ecaf79e3ddacc5a | GritLM/GritLM-7B | [
"gray",
"blonde",
"brunette",
"redhead",
"haiku",
"ode",
"limerick",
"ballad",
"epic",
"free verse",
"top hat",
"beanie",
"plane",
"convex",
"Arctic",
"Indian",
"Atlantic"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,561.5921 | clustering | leftvote | [
"",
""
] | 2bae9fd3ec534fd0bf8b36069b7f8987 | text-embedding-3-large | [
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such",
"$f(x)$ can be represented by a field. In this scenario, we get $y$ from $x$ by solving an initial value problem:\n$$\\frac{dz}{dt} = g(z, t, \\theta); \\quad z(t=0) \\equiv x, z(t=t_\\text{f})\\equiv y.$$ ",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces.",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`",
"ssh tunnel: `ssh -f [email protected] -L local-port:host:remote-port -N`",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring.",
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections."
] | 3 | 3D (press for 2D) | PCA | KMeans | 22c685d294c64c5b874d444961b1180c | nomic-ai/nomic-embed-text-v1.5 | [
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such",
"$f(x)$ can be represented by a field. In this scenario, we get $y$ from $x$ by solving an initial value problem:\n$$\\frac{dz}{dt} = g(z, t, \\theta); \\quad z(t=0) \\equiv x, z(t=t_\\text{f})\\equiv y.$$ ",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces.",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`",
"ssh tunnel: `ssh -f [email protected] -L local-port:host:remote-port -N`",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring.",
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections."
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,796.7433 | clustering | rightvote | [
"",
""
] | 7e39178a35e4498fb221a9cfdd73dd52 | sentence-transformers/all-MiniLM-L6-v2 | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans | 76b205acad144409af072e87a1514fd7 | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,844.1965 | clustering | rightvote | [
"",
""
] | c4bf4471fbab497db9cf1028f93c0b43 | intfloat/multilingual-e5-large-instruct | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans | 74d97c11190349b3b0eda2fb9beeb195 | text-embedding-004 | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,899.0983 | clustering | rightvote | [
"",
""
] | 23068f3458a94be886de1ce87e1d4b55 | text-embedding-004 | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans | 1e42c9e9a60f4cec8750190c175725d5 | embed-english-v3.0 | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,945.7525 | clustering | tievote | [
"",
""
] | a9499ebb065241e69b6e3e9db7135fe2 | voyage-multilingual-2 | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans | 91c0a61dab794b17b113ce5bc3558348 | intfloat/multilingual-e5-large-instruct | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,762,946.5053 | clustering | rightvote | [
"",
""
] | 9a6cd8222ddc4d8daa02e12053319b87 | text-embedding-004 | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans | 6b0413044e0243489f2de0a7d1ca1c47 | intfloat/e5-mistral-7b-instruct | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,763,021.8863 | clustering | rightvote | [
"",
""
] | a1c304f9cec34aefbe7794b89a6cc580 | text-embedding-3-large | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans | 26356c5cee98456ebe7d2eae1f28a718 | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,763,063.3142 | clustering | rightvote | [
"",
""
] | 56cc0ef844fe49ebab1fa30ecd9a2684 | jinaai/jina-embeddings-v2-base-en | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 1 | 3D (press for 2D) | PCA | KMeans | 364455a975c04bb496842455390b9c02 | text-embedding-3-large | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 1 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,763,104.7858 | clustering | leftvote | [
"",
""
] | 7a4f58a0bb8f46fc901708e1e760dfe9 | Salesforce/SFR-Embedding-2_R | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans | fee74bb17c304a17b14a9378c468ff7e | sentence-transformers/all-MiniLM-L6-v2 | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,738,763,194.4491 | clustering | rightvote | [
"",
""
] | e6c2bf59f2314a979af985433ac13ab5 | voyage-multilingual-2 | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 2D (press for 3D) | PCA | KMeans | 78caf84879f24819a0e48eacb9f401d3 | text-embedding-004 | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 2D (press for 3D) | PCA | KMeans |
|||
1,738,763,225.9459 | clustering | rightvote | [
"",
""
] | 6065a094be19431dbce25d576e8e8116 | GritLM/GritLM-7B | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 2D (press for 3D) | PCA | KMeans | 04224042c9054a48aef54921e4709884 | intfloat/multilingual-e5-large-instruct | [
"I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.",
"We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ",
"Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.",
"Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$",
"Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.",
"When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ",
"Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`",
"Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"
] | 3 | 2D (press for 3D) | PCA | KMeans |
|||
1,738,805,729.5424 | clustering | rightvote | [
"",
""
] | 35d408bf07a74b6aa18a6d1799db891a | sentence-transformers/all-MiniLM-L6-v2 | [
"confirmation bias",
"dunning-kruger effect",
"Uranus",
"Earth",
"Neptune",
"Jupiter",
"Venus",
"apple",
"banana",
"O",
"AB",
"A",
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 2D (press for 3D) | PCA | KMeans | e5fa9b7f331842a797c541faba99c9d9 | Salesforce/SFR-Embedding-2_R | [
"confirmation bias",
"dunning-kruger effect",
"Uranus",
"Earth",
"Neptune",
"Jupiter",
"Venus",
"apple",
"banana",
"O",
"AB",
"A",
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 2D (press for 3D) | PCA | KMeans |
|||
1,738,842,153.4422 | clustering | leftvote | [
"",
""
] | e3e41639f5804092856a06d14b3d0ed0 | embed-english-v3.0 | [
"| 年 | 出来事 | 研究者 |\n| ---- | --------------------------------------------------- | --------------------- |\n| 1890 | アンゴラウサギの4細胞胚をベルギー種のメスに移植し、4匹のベルギー種と2匹のアンゴラ種の子ウサギが誕生 | Walter Heape |\n| 1897 | ウサギ胚を針の先端に刺して受容体に直接移植する技術（培養液を使わない方法）が記述される | Walter Heape |\n| 1926 | イギリスのケンブリッジで採取された精液をスコットランドのエディンバラで人工授精し、ウサギが妊娠 | Walton |\n| 1951 | ウサギで精子の受精能力（capacitation）の必要性が初めて実証される | Chang; Austin |\n| 1959 | ウサギが体外受精（IVF）による生きた子孫を産んだ最初の哺乳類となる | Chang |\n| 1977 | ウサギの子宮または卵管で精子を受精能力化（capacitation）した後に牛の体外受精が成功 | Iritani and Niwa |\n| 1983 | ウサギを含む動物でX染色体とY染色体を持つ精子のDNA含有量の違いが初めて測定される | Garner et al. |\n| 1986 | ウサギの卵管で培養された受精卵を用いて、2頭の子牛が誕生 | Hanada et al. |\n| 1989 | 初めて性選別された精子からウサギの子孫が誕生（94%が雌であることを確認） | Johnson et al. (USDA) |",
"- **1890年**: アンゴラウサギの4細胞胚をベルギー種のメスに移植し、4匹のベルギー種と2匹のアンゴラ種の子ウサギが誕生（Walter Heape） \n- **1897年**: ウサギ胚を針の先端に刺して受容体に直接移植する技術（培養液を使わない方法）が記述される（Walter Heape） \n- **1926年**: イギリスのケンブリッジで採取された精液をスコットランドのエディンバラで人工授精し、ウサギが妊娠（Walton） \n- **1951年**: ウサギで精子の受精能力（capacitation）の必要性が初めて実証される（Chang; Austin） \n- **1959年**: ウサギが体外受精（IVF）による生きた子孫を産んだ最初の哺乳類となる（Chang） \n- **1977年**: ウサギの子宮または卵管で精子を受精能力化（capacitation）した後に牛の体外受精が成功（Iritani and Niwa） \n- **1983年**: ウサギを含む動物でX染色体とY染色体を持つ精子のDNA含有量の違いが初めて測定される（Garner et al.） \n- **1986年**: ウサギの卵管で培養された受精卵を用いて、2頭の子牛が誕生（Hanada et al.） \n- **1989年**: 初めて性選別された精子からウサギの子孫が誕生（94%が雌であることを確認）（Johnson et al. (USDA)） ",
"- 1890年: ウサギの胚を移植（Walter Heape） \n- 1897年: 受容体に移植する技術が登場（Walter Heape） \n- 1926年: 人工授精でウサギが妊娠（Walton） \n- 1951年: 受精能力が必要なことを発見（Chang; Austin） \n- 1959年: ウサギが体外受精で子孫を産む（Chang） \n- 1977年: 牛の体外受精が成功（Iritani and Niwa） \n- 1983年: X染色体とY染色体の違いを測定（Garner et al.） \n- 1986年: 受精卵を使って牛が誕生（Hanada et al.） \n- 1989年: 性選別した精子でウサギが誕生（Johnson et al. (USDA)）",
"- 1891年: アンゴラウサギの胚を移植し、ベルギー種のウサギが誕生（Walter Heape） \n- 1898年: ウサギ胚を針の先に刺して移植する方法が登場（Walter Heape） \n- 1925年: イギリスで採取された精液をスコットランドで人工授精し、ウサギが妊娠（Walton） \n- 1950年: ウサギで精子の受精能力の必要性が確認される（Chang; Austin） \n- 1960年: ウサギが体外受精で子孫を産む（Chang） \n- 1978年: ウサギの精子を受精能力化した後に牛の体外受精が成功（Iritani and Niwa） \n- 1985年: X染色体とY染色体のDNA含有量の違いが測定される（Garner et al.） \n- 1987年: ウサギの卵管で培養された受精卵から牛が誕生（Hanada et al.） \n- 1990年: 性選別された精子でウサギの子孫が誕生（Johnson et al. (USDA)）",
"- 1890年: アンゴラウサギの4細胞胚をベルギー種のメスに移植し、ベルギー種とアンゴラ種の子ウサギが誕生（James Smith） \n- 1897年: ウサギ胚を針の先に刺して受容体に直接移植する技術が登場（Robert Brown） \n- 1926年: 採取された精液を人工授精し、ウサギが妊娠（Walters） \n- 1951年: 精子の受精能力（capacitation）の必要性が初めて実証される（Chang; Anderson） \n- 1959年: ウサギが体外受精（IVF）で生きた子孫を産む（Chang and White） \n- 1977年: ウサギの精子を受精能力化した後に牛の体外受精が成功（Iritani and Sato） \n- 1983年: X染色体とY染色体のDNA含有量の違いが測定される（Garner and Lee） \n- 1986年: ウサギの卵管で培養された受精卵から子牛が誕生（Honda et al.） \n- 1989年: 性選別された精子でウサギの子孫が誕生（Johnston et al. (USDA)）",
"- 1890年: アンゴラウサギとベルギー種の交配で新種が誕生（Walter Heape） \n- 1897年: ウサギの胚を取り出し、体外で成長させる技術が開発（Walter Heape） \n- 1926年: ケンブリッジで採取された卵子をエディンバラで受精（Walton） \n- 1951年: ウサギの精子が体外で成熟することを確認（Chang; Austin） \n- 1959年: ウサギがクローン技術で生まれた最初の哺乳類となる（Chang） \n- 1977年: ウサギの体内で人間の精子を受精能力化し、体外受精が成功（Iritani and Niwa） \n- 1983年: X染色体とY染色体の大きさが異なることを初めて確認（Garner et al.） \n- 1986年: 体外受精した受精卵をウサギに移植し、クローン牛が誕生（Hanada et al.） \n- 1989年: 性選別した精子を使い、全てオスのウサギが誕生（Johnson et al. (USDA)）",
"- **1951年**: ウサギで精子の受精能力（capacitation）の必要性が初めて実証される（Chang; Austin） \n- **1959年**: ウサギが体外受精（IVF）による生きた子孫を産んだ最初の哺乳類となる（Chang） \n- **1977年**: ウサギの子宮または卵管で精子を受精能力化（capacitation）した後に牛の体外受精が成功（Iritani and Niwa） \n- **1983年**: ウサギを含む動物でX染色体とY染色体を持つ精子のDNA含有量の違いが初めて測定される（Garner et al.）",
"- **1897年**: ウサギ胚を針の先端に刺して受容体に直接移植する技術（培養液を使わない方法）が記述される（Walter Heape） \n- **1926年**: イギリスのケンブリッジで採取された精液をスコットランドのエディンバラで人工授精し、ウサギが妊娠（Walton） \n- **1951年**: ウサギで精子の受精能力（capacitation）の必要性が初めて実証される（Chang; Austin） \n- **1959年**: ウサギが体外受精（IVF）による生きた子孫を産んだ最初の哺乳類となる（Chang）\n- 1977年: ウサギの体内で人間の精子を受精能力化し、体外受精が成功（Iritani and Niwa） \n- 1983年: X染色体とY染色体の大きさが異なることを初めて確認（Garner et al.） \n- 1986年: 体外受精した受精卵をウサギに移植し、クローン牛が誕生（Hanada et al.） \n- 1989年: 性選別した精子を使い、全てオスのウサギが誕生（Johnson et al. (USDA)）",
"- **1983年**: ウサギを含む動物でX染色体とY染色体を持つ精子のDNA含有量の違いが初めて測定される（Garner et al.） \n- **1986年**: ウサギの卵管で培養された受精卵を用いて、2頭の子牛が誕生（Hanada et al.） \n- **1989年**: 初めて性選別された精子からウサギの子孫が誕生（94%が雌であることを確認）（Johnson et al. (USDA)）"
] | 3 | 3D (press for 2D) | PCA | KMeans | 185362d59464460f87b500c2c8f1a763 | mixedbread-ai/mxbai-embed-large-v1 | [
"| 年 | 出来事 | 研究者 |\n| ---- | --------------------------------------------------- | --------------------- |\n| 1890 | アンゴラウサギの4細胞胚をベルギー種のメスに移植し、4匹のベルギー種と2匹のアンゴラ種の子ウサギが誕生 | Walter Heape |\n| 1897 | ウサギ胚を針の先端に刺して受容体に直接移植する技術（培養液を使わない方法）が記述される | Walter Heape |\n| 1926 | イギリスのケンブリッジで採取された精液をスコットランドのエディンバラで人工授精し、ウサギが妊娠 | Walton |\n| 1951 | ウサギで精子の受精能力（capacitation）の必要性が初めて実証される | Chang; Austin |\n| 1959 | ウサギが体外受精（IVF）による生きた子孫を産んだ最初の哺乳類となる | Chang |\n| 1977 | ウサギの子宮または卵管で精子を受精能力化（capacitation）した後に牛の体外受精が成功 | Iritani and Niwa |\n| 1983 | ウサギを含む動物でX染色体とY染色体を持つ精子のDNA含有量の違いが初めて測定される | Garner et al. |\n| 1986 | ウサギの卵管で培養された受精卵を用いて、2頭の子牛が誕生 | Hanada et al. |\n| 1989 | 初めて性選別された精子からウサギの子孫が誕生（94%が雌であることを確認） | Johnson et al. (USDA) |",
"- **1890年**: アンゴラウサギの4細胞胚をベルギー種のメスに移植し、4匹のベルギー種と2匹のアンゴラ種の子ウサギが誕生（Walter Heape） \n- **1897年**: ウサギ胚を針の先端に刺して受容体に直接移植する技術（培養液を使わない方法）が記述される（Walter Heape） \n- **1926年**: イギリスのケンブリッジで採取された精液をスコットランドのエディンバラで人工授精し、ウサギが妊娠（Walton） \n- **1951年**: ウサギで精子の受精能力（capacitation）の必要性が初めて実証される（Chang; Austin） \n- **1959年**: ウサギが体外受精（IVF）による生きた子孫を産んだ最初の哺乳類となる（Chang） \n- **1977年**: ウサギの子宮または卵管で精子を受精能力化（capacitation）した後に牛の体外受精が成功（Iritani and Niwa） \n- **1983年**: ウサギを含む動物でX染色体とY染色体を持つ精子のDNA含有量の違いが初めて測定される（Garner et al.） \n- **1986年**: ウサギの卵管で培養された受精卵を用いて、2頭の子牛が誕生（Hanada et al.） \n- **1989年**: 初めて性選別された精子からウサギの子孫が誕生（94%が雌であることを確認）（Johnson et al. (USDA)） ",
"- 1890年: ウサギの胚を移植（Walter Heape） \n- 1897年: 受容体に移植する技術が登場（Walter Heape） \n- 1926年: 人工授精でウサギが妊娠（Walton） \n- 1951年: 受精能力が必要なことを発見（Chang; Austin） \n- 1959年: ウサギが体外受精で子孫を産む（Chang） \n- 1977年: 牛の体外受精が成功（Iritani and Niwa） \n- 1983年: X染色体とY染色体の違いを測定（Garner et al.） \n- 1986年: 受精卵を使って牛が誕生（Hanada et al.） \n- 1989年: 性選別した精子でウサギが誕生（Johnson et al. (USDA)）",
"- 1891年: アンゴラウサギの胚を移植し、ベルギー種のウサギが誕生（Walter Heape） \n- 1898年: ウサギ胚を針の先に刺して移植する方法が登場（Walter Heape） \n- 1925年: イギリスで採取された精液をスコットランドで人工授精し、ウサギが妊娠（Walton） \n- 1950年: ウサギで精子の受精能力の必要性が確認される（Chang; Austin） \n- 1960年: ウサギが体外受精で子孫を産む（Chang） \n- 1978年: ウサギの精子を受精能力化した後に牛の体外受精が成功（Iritani and Niwa） \n- 1985年: X染色体とY染色体のDNA含有量の違いが測定される（Garner et al.） \n- 1987年: ウサギの卵管で培養された受精卵から牛が誕生（Hanada et al.） \n- 1990年: 性選別された精子でウサギの子孫が誕生（Johnson et al. (USDA)）",
"- 1890年: アンゴラウサギの4細胞胚をベルギー種のメスに移植し、ベルギー種とアンゴラ種の子ウサギが誕生（James Smith） \n- 1897年: ウサギ胚を針の先に刺して受容体に直接移植する技術が登場（Robert Brown） \n- 1926年: 採取された精液を人工授精し、ウサギが妊娠（Walters） \n- 1951年: 精子の受精能力（capacitation）の必要性が初めて実証される（Chang; Anderson） \n- 1959年: ウサギが体外受精（IVF）で生きた子孫を産む（Chang and White） \n- 1977年: ウサギの精子を受精能力化した後に牛の体外受精が成功（Iritani and Sato） \n- 1983年: X染色体とY染色体のDNA含有量の違いが測定される（Garner and Lee） \n- 1986年: ウサギの卵管で培養された受精卵から子牛が誕生（Honda et al.） \n- 1989年: 性選別された精子でウサギの子孫が誕生（Johnston et al. (USDA)）",
"- 1890年: アンゴラウサギとベルギー種の交配で新種が誕生（Walter Heape） \n- 1897年: ウサギの胚を取り出し、体外で成長させる技術が開発（Walter Heape） \n- 1926年: ケンブリッジで採取された卵子をエディンバラで受精（Walton） \n- 1951年: ウサギの精子が体外で成熟することを確認（Chang; Austin） \n- 1959年: ウサギがクローン技術で生まれた最初の哺乳類となる（Chang） \n- 1977年: ウサギの体内で人間の精子を受精能力化し、体外受精が成功（Iritani and Niwa） \n- 1983年: X染色体とY染色体の大きさが異なることを初めて確認（Garner et al.） \n- 1986年: 体外受精した受精卵をウサギに移植し、クローン牛が誕生（Hanada et al.） \n- 1989年: 性選別した精子を使い、全てオスのウサギが誕生（Johnson et al. (USDA)）",
"- **1951年**: ウサギで精子の受精能力（capacitation）の必要性が初めて実証される（Chang; Austin） \n- **1959年**: ウサギが体外受精（IVF）による生きた子孫を産んだ最初の哺乳類となる（Chang） \n- **1977年**: ウサギの子宮または卵管で精子を受精能力化（capacitation）した後に牛の体外受精が成功（Iritani and Niwa） \n- **1983年**: ウサギを含む動物でX染色体とY染色体を持つ精子のDNA含有量の違いが初めて測定される（Garner et al.）",
"- **1897年**: ウサギ胚を針の先端に刺して受容体に直接移植する技術（培養液を使わない方法）が記述される（Walter Heape） \n- **1926年**: イギリスのケンブリッジで採取された精液をスコットランドのエディンバラで人工授精し、ウサギが妊娠（Walton） \n- **1951年**: ウサギで精子の受精能力（capacitation）の必要性が初めて実証される（Chang; Austin） \n- **1959年**: ウサギが体外受精（IVF）による生きた子孫を産んだ最初の哺乳類となる（Chang）\n- 1977年: ウサギの体内で人間の精子を受精能力化し、体外受精が成功（Iritani and Niwa） \n- 1983年: X染色体とY染色体の大きさが異なることを初めて確認（Garner et al.） \n- 1986年: 体外受精した受精卵をウサギに移植し、クローン牛が誕生（Hanada et al.） \n- 1989年: 性選別した精子を使い、全てオスのウサギが誕生（Johnson et al. (USDA)）",
"- **1983年**: ウサギを含む動物でX染色体とY染色体を持つ精子のDNA含有量の違いが初めて測定される（Garner et al.） \n- **1986年**: ウサギの卵管で培養された受精卵を用いて、2頭の子牛が誕生（Hanada et al.） \n- **1989年**: 初めて性選別された精子からウサギの子孫が誕生（94%が雌であることを確認）（Johnson et al. (USDA)）"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,266,426.4606 | clustering | tievote | [
"",
""
] | 8231665ecd594c86b9bee0001d5e989c | BAAI/bge-large-en-v1.5 | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans | c2047173c4764d6ebfaf1db67e37ed7d | GritLM/GritLM-7B | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,266,465.3044 | clustering | tievote | [
"",
""
] | e40f45b38d8d4568a17340ab5c831eda | intfloat/multilingual-e5-large-instruct | [
"Tesla’s Model 3 will boast a 200-mile or greater range, cost $35,000",
"How to Buy an Android Car DVD with GPS Navigation?",
"For those considering voting, your most 'progressive' candidate is still a fascist.",
"She's plotting her revenge guys, help!",
"Bandar Bola - Apa Sucker Akan Beli Obligasi Lotus Esprit Submarine Untuk $ 1.000.000?",
"Durch Die Nacht Mit... Henry Rollins und Shirin Neshat (2006) \"Henry Rollins hangs out with Iranian artist Shirin Neshat as part of a Dutch TV show.\"",
"Black Lives Matter protesters block San Francisco's Bay Bridge",
"Ceiling Cat vs. Basement Cat; Arial Image from Syria Shows Biblical Struggle",
"“Allure” by any other name: The double standards of rape culture, racism, and gender in pop music",
"New Zealand's Transgender Prisoners Fear Double Bunking Will Lead to More Rape",
"Homeless return to Sacramento City Hall under political, legal cloud",
"Meet Rogan and Bosley, two senior grumpy Persian brothers"
] | 3 | 3D (press for 2D) | PCA | KMeans | e7b78554b50d469abeebf74e9aef95f0 | text-embedding-3-large | [
"Tesla’s Model 3 will boast a 200-mile or greater range, cost $35,000",
"How to Buy an Android Car DVD with GPS Navigation?",
"For those considering voting, your most 'progressive' candidate is still a fascist.",
"She's plotting her revenge guys, help!",
"Bandar Bola - Apa Sucker Akan Beli Obligasi Lotus Esprit Submarine Untuk $ 1.000.000?",
"Durch Die Nacht Mit... Henry Rollins und Shirin Neshat (2006) \"Henry Rollins hangs out with Iranian artist Shirin Neshat as part of a Dutch TV show.\"",
"Black Lives Matter protesters block San Francisco's Bay Bridge",
"Ceiling Cat vs. Basement Cat; Arial Image from Syria Shows Biblical Struggle",
"“Allure” by any other name: The double standards of rape culture, racism, and gender in pop music",
"New Zealand's Transgender Prisoners Fear Double Bunking Will Lead to More Rape",
"Homeless return to Sacramento City Hall under political, legal cloud",
"Meet Rogan and Bosley, two senior grumpy Persian brothers"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,732,204,561.614 | clustering | tievote | [
"",
""
] | 59607ba49a8b4354af998287f00ed8d2 | text-embedding-004 | [
"dome",
"volcanic",
"fold",
"water filter",
"camping stove",
"sleeping bag",
"backpack"
] | 2 | 3D (press for 2D) | PCA | KMeans | 1d70af55f8ee494b8c9f36a8d4624455 | GritLM/GritLM-7B | [
"dome",
"volcanic",
"fold",
"water filter",
"camping stove",
"sleeping bag",
"backpack"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,732,204,601.987 | clustering | rightvote | [
"",
""
] | 713bf09423a4417a8720caf32aa794ec | embed-english-v3.0 | [
"drought",
"hurricane",
"tornado",
"fog",
"Brachiosaurus",
"Velociraptor",
"Pteranodon",
"Tyrannosaurus",
"B",
"O"
] | 3 | 3D (press for 2D) | PCA | KMeans | a8d8b1a640da47dc9c09ad6051d61127 | intfloat/multilingual-e5-large-instruct | [
"drought",
"hurricane",
"tornado",
"fog",
"Brachiosaurus",
"Velociraptor",
"Pteranodon",
"Tyrannosaurus",
"B",
"O"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,732,238,310.0725 | clustering | rightvote | [
"",
""
] | 0ff0ccb84b894574b425fc722a8543de | voyage-multilingual-2 | [
"Gemini",
"Capricorn",
"Aquarius",
"Virgo",
"Cancer",
"Scorpio",
"Apple",
"Huawei",
"OnePlus",
"Xiaomi",
"fascism",
"conservatism",
"convex",
"prismatic",
"concave",
"progressive"
] | 4 | 3D (press for 2D) | PCA | KMeans | 3ad3bd16adfb4cadb656f11618f4ccd7 | Salesforce/SFR-Embedding-2_R | [
"Gemini",
"Capricorn",
"Aquarius",
"Virgo",
"Cancer",
"Scorpio",
"Apple",
"Huawei",
"OnePlus",
"Xiaomi",
"fascism",
"conservatism",
"convex",
"prismatic",
"concave",
"progressive"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,732,238,378.8878 | clustering | rightvote | [
"",
""
] | 1b4bd59bd371488d9cc89f7a29864095 | BAAI/bge-large-en-v1.5 | [
"grilling",
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"lily",
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"penne",
"lasagna",
"ravioli",
"spaghetti",
"rupee",
"pound",
"euro",
"dollar",
"yen"
] | 4 | 3D (press for 2D) | PCA | KMeans | 501dc8c3ec2f4bdaa5bf0fa17858b20d | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"grilling",
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"ravioli",
"spaghetti",
"rupee",
"pound",
"euro",
"dollar",
"yen"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,732,304,023.1549 | clustering | leftvote | [
"",
""
] | 2abc17a5da724e04a9906b80218d6154 | Salesforce/SFR-Embedding-2_R | [
"Cygnus",
"Cassiopeia",
"Taurus",
"Orion",
"Ursa Major",
"Scorpius",
"Leo",
"Triceratops",
"Tyrannosaurus",
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"orange",
"kiwi",
"apple",
"grape",
"peach",
"mango",
"rupee",
"dollar",
"euro",
"pound",
"yuan"
] | 4 | 3D (press for 2D) | PCA | KMeans | ae1e9e7718c74de598c15e17bd4deb86 | jinaai/jina-embeddings-v2-base-en | [
"Cygnus",
"Cassiopeia",
"Taurus",
"Orion",
"Ursa Major",
"Scorpius",
"Leo",
"Triceratops",
"Tyrannosaurus",
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"orange",
"kiwi",
"apple",
"grape",
"peach",
"mango",
"rupee",
"dollar",
"euro",
"pound",
"yuan"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,730,903,999.328 | clustering | tievote | [
"",
""
] | 03f0f264892c41f280ec2b3492c22ef7 | BAAI/bge-large-en-v1.5 | [
"baseball cap",
"cowboy hat",
"beanie",
"convex",
"parabolic",
"basketball",
"volleyball"
] | 3 | 2D (press for 3D) | PCA | KMeans | 0901c93377d04d3eae507a7f87cddf94 | text-embedding-004 | [
"baseball cap",
"cowboy hat",
"beanie",
"convex",
"parabolic",
"basketball",
"volleyball"
] | 3 | 2D (press for 3D) | PCA | KMeans |
|||
1,731,300,951.1924 | clustering | leftvote | [
"",
""
] | 0d6ab8d6646243afb9034accb33a4512 | mixedbread-ai/mxbai-embed-large-v1 | [
"Kia",
"GMC",
"BMW",
"Toyota",
"Volkswagen",
"griffin",
"werewolf",
"dragon",
"centaur",
"phoenix"
] | 2 | 3D (press for 2D) | PCA | KMeans | c2497af7547e443aacb6db06629c523e | sentence-transformers/all-MiniLM-L6-v2 | [
"Kia",
"GMC",
"BMW",
"Toyota",
"Volkswagen",
"griffin",
"werewolf",
"dragon",
"centaur",
"phoenix"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,731,301,000.4996 | clustering | tievote | [
"",
""
] | 34079265e88c4d67abf89568c6b233e8 | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"Neptune",
"Mars",
"Uranus",
"Jupiter",
"Saturn",
"Volkswagen",
"Honda"
] | 2 | 3D (press for 2D) | UMAP | KMeans | 95c01e08b4924761ba0c3113432f6c4f | text-embedding-004 | [
"Neptune",
"Mars",
"Uranus",
"Jupiter",
"Saturn",
"Volkswagen",
"Honda"
] | 2 | 3D (press for 2D) | UMAP | KMeans |
|||
1,731,301,092.2252 | clustering | bothbadvote | [
"",
""
] | db7ebb41ed5e48dba02c05548506bb6b | GritLM/GritLM-7B | [
"altostratus",
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"nimbus",
"stratus",
"Baroque",
"Impressionism",
"Cubism",
"Renaissance",
"Surrealism",
"giraffe",
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"penguin",
"lion",
"elephant",
"tiger",
"Africa",
"North America",
"Asia",
"Australia",
"Europe",
"Antarctica"
] | 4 | 3D (press for 2D) | UMAP | KMeans | 03b92e5ea84e42c5b3d1858038f22763 | jinaai/jina-embeddings-v2-base-en | [
"altostratus",
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"Baroque",
"Impressionism",
"Cubism",
"Renaissance",
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"penguin",
"lion",
"elephant",
"tiger",
"Africa",
"North America",
"Asia",
"Australia",
"Europe",
"Antarctica"
] | 4 | 3D (press for 2D) | UMAP | KMeans |
|||
1,731,301,170.1285 | clustering | rightvote | [
"",
""
] | 5ac8044066dc4a198ffeb6c351cfc157 | sentence-transformers/all-MiniLM-L6-v2 | [
"pancreas",
"liver",
"brain",
"lungs",
"heart",
"fedora",
"beanie",
"beret",
"bowler",
"cowboy hat",
"fruit",
"mixed",
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"crop",
"Antarctica",
"Africa",
"Europe",
"GPU",
"CPU",
"hard drive",
"motherboard",
"power supply",
"SSD",
"RAM"
] | 5 | 3D (press for 2D) | PCA | KMeans | a7d8f41af3a14ec393a63fca6347223c | voyage-multilingual-2 | [
"pancreas",
"liver",
"brain",
"lungs",
"heart",
"fedora",
"beanie",
"beret",
"bowler",
"cowboy hat",
"fruit",
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"Antarctica",
"Africa",
"Europe",
"GPU",
"CPU",
"hard drive",
"motherboard",
"power supply",
"SSD",
"RAM"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,731,301,280.8331 | clustering | leftvote | [
"",
""
] | 93f6dd066f4a4943ae360f6cfbf5c2c7 | embed-english-v3.0 | [
"trout",
"salmon",
"composite",
"caldera",
"lava dome",
"cinder cone",
"shield",
"roasting",
"steaming",
"baking",
"grilling",
"boiling",
"sedimentary",
"metamorphic",
"igneous",
"Orion",
"Cygnus",
"Taurus"
] | 5 | 3D (press for 2D) | UMAP | KMeans | 895c17ab2a0c4ad1854a659cad74ac8d | intfloat/e5-mistral-7b-instruct | [
"trout",
"salmon",
"composite",
"caldera",
"lava dome",
"cinder cone",
"shield",
"roasting",
"steaming",
"baking",
"grilling",
"boiling",
"sedimentary",
"metamorphic",
"igneous",
"Orion",
"Cygnus",
"Taurus"
] | 5 | 3D (press for 2D) | UMAP | KMeans |
|||
1,731,301,329.166 | clustering | tievote | [
"",
""
] | 0a99cf3700e2435fa79dc830ba66eb00 | intfloat/e5-mistral-7b-instruct | [
"oolong",
"chamomile",
"pu-erh",
"green",
"Cubism",
"Impressionism"
] | 2 | 3D (press for 2D) | PCA | KMeans | 7db21df6d8a54d4dbb6700e5f3c1d93a | intfloat/multilingual-e5-large-instruct | [
"oolong",
"chamomile",
"pu-erh",
"green",
"Cubism",
"Impressionism"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,731,301,369.0921 | clustering | rightvote | [
"",
""
] | 50879395bce243cfa3077859f1a30b19 | GritLM/GritLM-7B | [
"unicorn",
"werewolf",
"griffin",
"centaur",
"redhead",
"white",
"brunette",
"black",
"blonde"
] | 2 | 3D (press for 2D) | PCA | KMeans | 4fecb7a84f064bb4a30d3ea364f865ea | intfloat/multilingual-e5-large-instruct | [
"unicorn",
"werewolf",
"griffin",
"centaur",
"redhead",
"white",
"brunette",
"black",
"blonde"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,724,060,664.0712 | clustering | rightvote | [
"",
""
] | 847b509f5f37472e8d86779d45a32610 | BAAI/bge-large-en-v1.5 | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans | 5adbc43cdcac498eb67c967ad0cfeb0c | sentence-transformers/all-MiniLM-L6-v2 | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,724,098,291.8183 | clustering | rightvote | [
"",
""
] | 09d67d7034814900a1539f51561a0422 | intfloat/multilingual-e5-large-instruct | [
"Quiero renovar mi celular",
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"Haré portabilidad",
"Estoy pensando en cambiarme de operador",
"¿Quiero cambiarme de celular",
"Mañana me cambiaré de plan a uno más bajo"
] | 2 | 3D (press for 2D) | PCA | KMeans | 0feb6b08f3a4471fad07e16412c19b7c | GritLM/GritLM-7B | [
"Quiero renovar mi celular",
"Quiero cambiarme de plan",
"Haré portabilidad",
"Estoy pensando en cambiarme de operador",
"¿Quiero cambiarme de celular",
"Mañana me cambiaré de plan a uno más bajo"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,724,163,958.9231 | clustering | rightvote | [
"",
""
] | 2daa9bdee69c4792aa497adf12b1ab28 | sentence-transformers/all-MiniLM-L6-v2 | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans | 0e189a82702941dba2c8f719d3a986a5 | text-embedding-004 | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,724,164,131.8129 | clustering | leftvote | [
"",
""
] | 73c4af80b98d431eba13a3b40ac9b3d7 | mixedbread-ai/mxbai-embed-large-v1 | [
"It's official! 1 Bitcoin = $10,000 USD",
"Everyone who's trading BTC right now",
"Age reversal not only achievable but also possibly imminent: Retro Biosciences",
"MicroRNA regrows 90% of lost hair, study finds",
"Speech-to-speech translation for a real-world unwritten language",
"Seeking the Best Embedding Model: Experiences with the MTEB Arena?"
] | 3 | 3D (press for 2D) | PCA | KMeans | b45cf608bead4124b0d7596b5384345c | voyage-multilingual-2 | [
"It's official! 1 Bitcoin = $10,000 USD",
"Everyone who's trading BTC right now",
"Age reversal not only achievable but also possibly imminent: Retro Biosciences",
"MicroRNA regrows 90% of lost hair, study finds",
"Speech-to-speech translation for a real-world unwritten language",
"Seeking the Best Embedding Model: Experiences with the MTEB Arena?"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,724,280,294.9014 | clustering | tievote | [
"",
""
] | d9b5e5b41abf426181ad9670782d952e | sentence-transformers/all-MiniLM-L6-v2 | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans | d710f7648b854cf793aef2f3a8c661cb | nomic-ai/nomic-embed-text-v1.5 | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,724,291,653.885 | clustering | leftvote | [
"",
""
] | 400fa553f9094ab9b12e6ea1256ec2bc | jinaai/jina-embeddings-v2-base-en | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans | 83602d74a2cf4a289b5441845f4e176f | Salesforce/SFR-Embedding-2_R | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,724,349,504.3035 | clustering | tievote | [
"",
""
] | f6f2a09e097c42b48b2d7f5dfb6768c8 | sentence-transformers/all-MiniLM-L6-v2 | [
"aap kaise ho",
"How are you",
"I am fine",
"Mai theek hu",
"mai bhi theek hu",
"I am also fine",
"Ye college is a great place for studying",
"This college is good for studying",
"Ye college boht badhiya hai",
"Mujhe khana khana hai",
"This college is very good place"
] | 2 | 2D (press for 3D) | PCA | KMeans | 67375df8d26e4996b43af1c9447756e0 | intfloat/multilingual-e5-large-instruct | [
"aap kaise ho",
"How are you",
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"Mai theek hu",
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"This college is good for studying",
"Ye college boht badhiya hai",
"Mujhe khana khana hai",
"This college is very good place"
] | 2 | 2D (press for 3D) | PCA | KMeans |
|||
1,731,563,878.7289 | clustering | leftvote | [
"",
""
] | 56a92e40c6e34c0c930858252c81c687 | intfloat/multilingual-e5-large-instruct | [
"bus",
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"train",
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"cotton",
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"wool",
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"polyester",
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"biomass",
"geothermal",
"solar",
"tidal",
"cycling",
"gymnastics",
"athletics",
"swimming",
"archery"
] | 2 | 3D (press for 2D) | PCA | KMeans | c81fb92d58324d19b4ee078660730dc1 | Salesforce/SFR-Embedding-2_R | [
"bus",
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"boat",
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"wool",
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"polyester",
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"biomass",
"geothermal",
"solar",
"tidal",
"cycling",
"gymnastics",
"athletics",
"swimming",
"archery"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,731,567,969.971 | clustering | rightvote | [
"",
""
] | 2d154bd54af645d4abb1c33705a2e9d7 | GritLM/GritLM-7B | [
"I like to eat pizza",
"I like to eat hamburgers",
"I like to play soccer",
"I like playing badminton",
"I like to have a hotdog from time to time",
"I like to play football"
] | 2 | 2D (press for 3D) | PCA | KMeans | 085710e48c674d9baacc5543040a3718 | text-embedding-004 | [
"I like to eat pizza",
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"I like playing badminton",
"I like to have a hotdog from time to time",
"I like to play football"
] | 2 | 2D (press for 3D) | PCA | KMeans |
|||
1,731,396,477.6223 | clustering | leftvote | [
"",
""
] | 9acad86d01d4490a8ffd8925fd86f562 | text-embedding-3-large | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans | 14724d659797415fb361ec71152ac256 | BAAI/bge-large-en-v1.5 | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,731,396,509.0838 | clustering | rightvote | [
"",
""
] | 365db30a6de548ab97ec315ecb7d2414 | nomic-ai/nomic-embed-text-v1.5 | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans | c170ecf405414c0a83ef8bb7bcd71cd5 | text-embedding-3-large | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,244,337.8557 | clustering | rightvote | [
"",
""
] | 8ee353339b874131883f4e758c4fb6c2 | intfloat/e5-mistral-7b-instruct | [
"Apple",
"Samsung",
"Huawei",
"LG",
"OnePlus",
"Xiaomi",
"wisdom tooth",
"molar",
"incisor",
"premolar",
"canine",
"Opera",
"Safari",
"Brave",
"Edge",
"Firefox",
"Chrome",
"Capricorn",
"Leo",
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"Aries",
"Scorpio",
"Libra",
"Cancer",
"Gemini",
"canoe",
"motorboat",
"yacht",
"catamaran"
] | 5 | 3D (press for 2D) | PCA | KMeans | dca734cd08a946dc991a76cbd4459fd7 | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"Apple",
"Samsung",
"Huawei",
"LG",
"OnePlus",
"Xiaomi",
"wisdom tooth",
"molar",
"incisor",
"premolar",
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"Opera",
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"Brave",
"Edge",
"Firefox",
"Chrome",
"Capricorn",
"Leo",
"Taurus",
"Aries",
"Scorpio",
"Libra",
"Cancer",
"Gemini",
"canoe",
"motorboat",
"yacht",
"catamaran"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,409,498.4434 | clustering | bothbadvote | [
"",
""
] | 98569eae160342459406a000ac1341bd | GritLM/GritLM-7B | [
"shear wall design",
"window header",
"subfloor spacinng",
"joist design",
"beam clear span",
"anchor bolt design",
"jack stud design"
] | 2 | 3D (press for 2D) | PCA | KMeans | ae75439bc445400696766ec73cdf8625 | sentence-transformers/all-MiniLM-L6-v2 | [
"shear wall design",
"window header",
"subfloor spacinng",
"joist design",
"beam clear span",
"anchor bolt design",
"jack stud design"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,409,942.498 | clustering | tievote | [
"",
""
] | f26db202e63e41289a84e01ff79a1a2e | intfloat/multilingual-e5-large-instruct | [
"I-Joist Spacing",
"Floor span",
"shear wall design",
"opening width",
"jack studs design",
"subfloor thickness",
"header thickness",
"soil capacity",
"footing with stem wall",
"bearing capacity",
"frost depth",
"anchor bolt spacing",
"SEER rating",
"R-value",
"water efficiency",
"energy consumption"
] | 4 | 3D (press for 2D) | PCA | KMeans | eaf2988a721845c695bca77835c10fec | voyage-multilingual-2 | [
"I-Joist Spacing",
"Floor span",
"shear wall design",
"opening width",
"jack studs design",
"subfloor thickness",
"header thickness",
"soil capacity",
"footing with stem wall",
"bearing capacity",
"frost depth",
"anchor bolt spacing",
"SEER rating",
"R-value",
"water efficiency",
"energy consumption"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,410,009.9832 | clustering | tievote | [
"",
""
] | 05acf7d2804248419829bc1737ef03b5 | sentence-transformers/all-MiniLM-L6-v2 | [
"I-Joist Spacing",
"Floor span",
"shear wall design",
"opening width",
"jack studs design",
"subfloor thickness",
"header thickness",
"soil capacity",
"footing with stem wall",
"bearing capacity",
"frost depth",
"anchor bolt spacing",
"SEER rating",
"R-value",
"water efficiency",
"energy consumption"
] | 4 | 3D (press for 2D) | PCA | KMeans | 8c9e106304e643a2b3106f0af3f0c612 | mixedbread-ai/mxbai-embed-large-v1 | [
"I-Joist Spacing",
"Floor span",
"shear wall design",
"opening width",
"jack studs design",
"subfloor thickness",
"header thickness",
"soil capacity",
"footing with stem wall",
"bearing capacity",
"frost depth",
"anchor bolt spacing",
"SEER rating",
"R-value",
"water efficiency",
"energy consumption"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,411,483.7118 | clustering | bothbadvote | [
"",
""
] | 7aa15601a37b4df1aaccd3f1d40df7ec | intfloat/e5-mistral-7b-instruct | [
"Wall framing spacing",
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"Load-bearing wall identification",
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"Bathroom exhaust fan CFM calculation",
"Minimum bedroom size requirements",
"Staircase riser and tread dimensions",
"Egress window sizing for bedrooms",
"Smoke detector placement guidelines",
"Minimum ceiling height in living spaces"
] | 5 | 3D (press for 2D) | PCA | KMeans | 5fe74c2553e8408d8d194a8ca35f2443 | voyage-multilingual-2 | [
"Wall framing spacing",
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"Exterior sheathing installation",
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"Minimum bedroom size requirements",
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"Egress window sizing for bedrooms",
"Smoke detector placement guidelines",
"Minimum ceiling height in living spaces"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,411,538.2593 | clustering | leftvote | [
"",
""
] | 11fc3c2019fc40ad94e7e384bfa06a0e | mixedbread-ai/mxbai-embed-large-v1 | [
"Wall framing spacing",
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"Minimum bedroom size requirements",
"Staircase riser and tread dimensions",
"Egress window sizing for bedrooms",
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] | 5 | 3D (press for 2D) | PCA | KMeans | 181b8a15881e4d2b88392dbf4ca74669 | nomic-ai/nomic-embed-text-v1.5 | [
"Wall framing spacing",
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"Load-bearing wall identification",
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"Minimum bedroom size requirements",
"Staircase riser and tread dimensions",
"Egress window sizing for bedrooms",
"Smoke detector placement guidelines",
"Minimum ceiling height in living spaces"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,734,940,950.346 | clustering | rightvote | [
"",
""
] | 4266516c9b9048d7b28f61e7e68fb5d5 | embed-english-v3.0 | [
"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?"
] | 2 | 3D (press for 2D) | PCA | KMeans | 01d1a51939c14e3e80e64b86215d95df | intfloat/e5-mistral-7b-instruct | [
"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?"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,734,943,387.5305 | clustering | rightvote | [
"",
""
] | 606549ec1b68473ab1ebf4f888a5165f | text-embedding-004 | [
"Shanghai",
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"baseball",
"cricket",
"ruby",
"topaz",
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] | 5 | 3D (press for 2D) | PCA | KMeans | 9f402924b8d9412d9bcdb8e83a97118b | GritLM/GritLM-7B | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
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"soccer",
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"tennis",
"baseball",
"cricket",
"ruby",
"topaz",
"diamond"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,734,943,556.1141 | clustering | bothbadvote | [
"",
""
] | f0aa3487cf6e4746b569d3480e38507c | text-embedding-004 | [
"octagon",
"rectangle",
"Temple of Artemis",
"Colossus of Rhodes",
"Statue of Zeus",
"Lighthouse of Alexandria",
"Hanging Gardens of Babylon",
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"auburn",
"white",
"soccer",
"basketball",
"tennis",
"baseball",
"cricket",
"ruby",
"topaz",
"diamond"
] | 5 | 2D (press for 3D) | PCA | KMeans | 1e29faf0b7364b81ab4cbc78ac47d5d2 | BAAI/bge-large-en-v1.5 | [
"octagon",
"rectangle",
"Temple of Artemis",
"Colossus of Rhodes",
"Statue of Zeus",
"Lighthouse of Alexandria",
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"white",
"soccer",
"basketball",
"tennis",
"baseball",
"cricket",
"ruby",
"topaz",
"diamond"
] | 5 | 2D (press for 3D) | PCA | KMeans |
|||
1,722,612,753.836 | clustering | tievote | [
"",
""
] | b0299402b4ef4942864589df0df92ef3 | text-embedding-004 | [
"violin",
"flute",
"guitar",
"piano",
"drums",
"Apple",
"Google",
"Samsung",
"LG",
"Huawei",
"OnePlus",
"Xiaomi",
"thriller",
"action",
"horror",
"oboe",
"saxophone",
"bassoon",
"trombone",
"clarinet",
"flute",
"Golden Retriever",
"German Shepherd",
"Chihuahua"
] | 5 | 3D (press for 2D) | PCA | KMeans | ff95785f0bf34df7abc09b6b551373de | sentence-transformers/all-MiniLM-L6-v2 | [
"violin",
"flute",
"guitar",
"piano",
"drums",
"Apple",
"Google",
"Samsung",
"LG",
"Huawei",
"OnePlus",
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"oboe",
"saxophone",
"bassoon",
"trombone",
"clarinet",
"flute",
"Golden Retriever",
"German Shepherd",
"Chihuahua"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,612,795.3835 | clustering | rightvote | [
"",
""
] | 3a1048846dc4488bbee85b1bfc332a3e | jinaai/jina-embeddings-v2-base-en | [
"English",
"Russian",
"Mandarin",
"Arabic",
"linguine",
"ravioli",
"Go",
"C++"
] | 3 | 3D (press for 2D) | PCA | KMeans | 7e2f750f3fd84deda84ba242a5cc24bf | GritLM/GritLM-7B | [
"English",
"Russian",
"Mandarin",
"Arabic",
"linguine",
"ravioli",
"Go",
"C++"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,465,648.9957 | clustering | rightvote | [
"",
""
] | 7ffe7dac8e644f5a970652a0d40ebad6 | GritLM/GritLM-7B | [
"North America",
"Antarctica",
"Asia",
"South America",
"Europe",
"Australia",
"Africa",
"top hat",
"cowboy hat",
"beret",
"silk",
"denim",
"linen",
"cotton",
"leather",
"polyester",
"wool"
] | 3 | 3D (press for 2D) | PCA | KMeans | a410501fd5954b768b52dda204d4868d | text-embedding-3-large | [
"North America",
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"Asia",
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"polyester",
"wool"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,504,255.7293 | clustering | leftvote | [
"",
""
] | 0678cdb61fbd432f8587af54f7f6b016 | voyage-multilingual-2 | [
"Monopoly",
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"Risk",
"Catan",
"fedora",
"beret",
"top hat",
"water filter",
"flashlight",
"sleeping bag",
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"backpack",
"prismatic",
"convex",
"concave",
"progressive",
"bifocal",
"toric",
"irony",
"alliteration",
"personification"
] | 3 | 3D (press for 2D) | PCA | KMeans | a0cdb38422ec41bda84f1a8ead861732 | BAAI/bge-large-en-v1.5 | [
"Monopoly",
"Clue",
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"progressive",
"bifocal",
"toric",
"irony",
"alliteration",
"personification"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,504,382.0493 | clustering | rightvote | [
"",
""
] | 36e0e85e768a49518bd2bd1b773d2956 | text-embedding-3-large | [
"Oracle Cloud",
"IBM Cloud",
"Azure",
"AWS",
"Google Cloud",
"pet",
"travel",
"disability",
"auto",
"home",
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"disgust",
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"sadness",
"joy",
"rock",
"reggae",
"country",
"classical",
"electronic",
"hip-hop",
"penne",
"fusilli"
] | 5 | 3D (press for 2D) | PCA | KMeans | 5e139fcf3d994cee84b1031dc93e14eb | jinaai/jina-embeddings-v2-base-en | [
"Oracle Cloud",
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"Azure",
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"country",
"classical",
"electronic",
"hip-hop",
"penne",
"fusilli"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,272,659.3063 | clustering | leftvote | [
"",
""
] | 919c97eb9cf549a1bf4e2f51c6b58fb1 | intfloat/e5-mistral-7b-instruct | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans | 393889c596aa4ae09e16e34862121291 | embed-english-v3.0 | [
"Shanghai",
"Beijing",
"Shenzhen",
"Hangzhou",
"Seattle",
"Boston",
"New York",
"San Francisco"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,272,694.603 | clustering | tievote | [
"",
""
] | a5d37a580c6f482fb943fa3f10c6aa6d | BAAI/bge-large-en-v1.5 | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans | 48fc757308974384a3aeab8df4498fb0 | text-embedding-004 | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,272,781.662 | clustering | tievote | [
"",
""
] | 2fa935503d7444289937354e37d78a32 | intfloat/multilingual-e5-large-instruct | [
"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?"
] | 2 | 3D (press for 2D) | PCA | KMeans | c047cd4aa1044007b410fc06159c0ff2 | jinaai/jina-embeddings-v2-base-en | [
"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?"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,102,070.8179 | clustering | rightvote | [
"",
""
] | 87b468e0cb7f43da86fe40c1fe4af5d7 | jinaai/jina-embeddings-v2-base-en | [
"focaccia",
"pumpernickel",
"rye",
"sourdough",
"ciabatta",
"beam",
"cantilever",
"suspension",
"cable-stayed",
"arch",
"truss",
"wind",
"hydroelectric",
"biomass",
"geothermal",
"solar",
"tidal"
] | 3 | 3D (press for 2D) | PCA | KMeans | 8070b17cad7047f3bc8f9e025100f286 | sentence-transformers/all-MiniLM-L6-v2 | [
"focaccia",
"pumpernickel",
"rye",
"sourdough",
"ciabatta",
"beam",
"cantilever",
"suspension",
"cable-stayed",
"arch",
"truss",
"wind",
"hydroelectric",
"biomass",
"geothermal",
"solar",
"tidal"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,102,117.9081 | clustering | leftvote | [
"",
""
] | 8b25c5b47c67481d95ce75a8e08a7228 | GritLM/GritLM-7B | [
"dairy",
"crop",
"livestock",
"poultry",
"mixed",
"haiku",
"ode",
"sonnet",
"ballad",
"winter",
"fall",
"Subway",
"Burger King",
"McDonald's",
"Pizza Hut",
"KFC",
"Taco Bell"
] | 4 | 3D (press for 2D) | PCA | KMeans | 010c8e7820b14742a4b72655cbcbafec | text-embedding-004 | [
"dairy",
"crop",
"livestock",
"poultry",
"mixed",
"haiku",
"ode",
"sonnet",
"ballad",
"winter",
"fall",
"Subway",
"Burger King",
"McDonald's",
"Pizza Hut",
"KFC",
"Taco Bell"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,134,893.9338 | clustering | tievote | [
"",
""
] | 3e3c8125c1a74295b1f2003dfcd3e96b | voyage-multilingual-2 | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans | a3d7637cfbcc4a82a6cb2152046b5196 | text-embedding-3-large | [
"Pikachu",
"Darth Vader",
"Yoda",
"Squirtle",
"Gandalf",
"Legolas",
"Mickey Mouse",
"Donald Duck",
"Charizard"
] | 4 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,136,681.2304 | clustering | leftvote | [
"",
""
] | 1b178d077fdc430684b789a84ebacd0a | text-embedding-3-large | [
"Indian",
"Pacific",
"Southern",
"Arctic",
"Atlantic",
"rooibos",
"pu-erh",
"chalk",
"fountain pen",
"cirrus",
"nimbus",
"altostratus",
"cumulus",
"stratus",
"flute",
"drums"
] | 5 | 3D (press for 2D) | PCA | KMeans | d185a0b148bd4a59b40c2774e66ec18e | mixedbread-ai/mxbai-embed-large-v1 | [
"Indian",
"Pacific",
"Southern",
"Arctic",
"Atlantic",
"rooibos",
"pu-erh",
"chalk",
"fountain pen",
"cirrus",
"nimbus",
"altostratus",
"cumulus",
"stratus",
"flute",
"drums"
] | 5 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,136,723.7632 | clustering | leftvote | [
"",
""
] | 4feaf1ee0a274ac8a85968a2361f8e54 | sentence-transformers/all-MiniLM-L6-v2 | [
"ciabatta",
"brioche",
"baguette",
"literature",
"biology",
"chemistry",
"history",
"physics"
] | 2 | 3D (press for 2D) | PCA | KMeans | 0bd549df73be4ad68095e81f687bf038 | jinaai/jina-embeddings-v2-base-en | [
"ciabatta",
"brioche",
"baguette",
"literature",
"biology",
"chemistry",
"history",
"physics"
] | 2 | 3D (press for 2D) | PCA | KMeans |
|||
1,723,214,645.1831 | clustering | tievote | [
"",
""
] | fb881f138f0b43cba2ae08f7e3c4f4a8 | intfloat/multilingual-e5-large-instruct | [
"werewolf",
"phoenix",
"mermaid",
"centaur",
"unicorn",
"liberalism",
"anarchism",
"fascism",
"Japanese",
"Mexican",
"Indian"
] | 3 | 3D (press for 2D) | PCA | KMeans | 0f26f47f54dd40819a948e28ece4a83e | Alibaba-NLP/gte-Qwen2-7B-instruct | [
"werewolf",
"phoenix",
"mermaid",
"centaur",
"unicorn",
"liberalism",
"anarchism",
"fascism",
"Japanese",
"Mexican",
"Indian"
] | 3 | 3D (press for 2D) | PCA | KMeans |
|||
1,722,276,839.4005 | clustering | leftvote | [
"",
""
] | 233e8e1ecf804bf680b427d49621a321 | text-embedding-004 | [
"Apples",
"Bananas",
"Oranges",
"Grapes",
"Pears",
"Lemons",
"Hydrogen",
"Helium",
"Lithium",
"Beryllium",
"Boron",
"Carbon",
"Dog",
"Cat",
"Hamster",
"Rabbit",
"Goldfish",
"Parakeet"
] | 3 | 2D (press for 3D) | PCA | KMeans | 6622c39eb5a14ce284aa2ccf3b8fe135 | text-embedding-3-large | [
"Apples",
"Bananas",
"Oranges",
"Grapes",
"Pears",
"Lemons",
"Hydrogen",
"Helium",
"Lithium",
"Beryllium",
"Boron",
"Carbon",
"Dog",
"Cat",
"Hamster",
"Rabbit",
"Goldfish",
"Parakeet"
] | 3 | 2D (press for 3D) | PCA | KMeans |
|||
1,722,276,997.8264 | clustering | rightvote | [
"",
""
] | bd490836af8e4370bf9ffcb7a54ba754 | voyage-multilingual-2 | [
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich",
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich"
] | 3 | 2D (press for 3D) | PCA | KMeans | 3b19aa95ee4f4908bdf23aec8478568a | embed-english-v3.0 | [
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich",
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich"
] | 3 | 2D (press for 3D) | PCA | KMeans |
|||
1,722,277,187.8827 | clustering | tievote | [
"",
""
] | f8c4bf85d67f41de9b8bbfa14f7ebd1b | intfloat/e5-mistral-7b-instruct | [
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich",
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich"
] | 3 | 2D (press for 3D) | PCA | KMeans | 1d4dc82e83524d199f681f7dd61a25f3 | text-embedding-004 | [
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich",
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich"
] | 3 | 2D (press for 3D) | PCA | KMeans |
|||
1,722,277,210.6836 | clustering | rightvote | [
"",
""
] | adafc83b33cc4355aadf6fc9e10cdbcd | GritLM/GritLM-7B | [
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich"
] | 3 | 2D (press for 3D) | PCA | KMeans | 67de54733b554dae8eb10a3fe142bde8 | jinaai/jina-embeddings-v2-base-en | [
"Piano",
"Electron",
"Sushi",
"Violin",
"Proton",
"Pasta",
"Trumpet",
"Neutron",
"Steak",
"Clarinet",
"Quark",
"Salad",
"Harp",
"Photon",
"Soup",
"Cello",
"Neutrino",
"Sandwich"
] | 3 | 2D (press for 3D) | PCA | KMeans |
|||
1,722,277,499.9324 | clustering | rightvote | [
"",
""
] | 0c3b0361b0f14423ae0b8081f781a2ec | BAAI/bge-large-en-v1.5 | [
"Apple",
"Hammer",
"Dog",
"Guitar",
"Banana",
"Screwdriver",
"Cat",
"Piano",
"Orange",
"Wrench",
"Rabbit",
"Violin",
"Apple",
"Hammer",
"Dog",
"Guitar",
"Banana",
"Screwdriver",
"Cat",
"Piano",
"Orange",
"Wrench",
"Rabbit",
"Violin"
] | 4 | 2D (press for 3D) | PCA | KMeans | 6522beccc4b44fffb049889a46d4ecd8 | text-embedding-004 | [
"Apple",
"Hammer",
"Dog",
"Guitar",
"Banana",
"Screwdriver",
"Cat",
"Piano",
"Orange",
"Wrench",
"Rabbit",
"Violin",
"Apple",
"Hammer",
"Dog",
"Guitar",
"Banana",
"Screwdriver",
"Cat",
"Piano",
"Orange",
"Wrench",
"Rabbit",
"Violin"
] | 4 | 2D (press for 3D) | PCA | KMeans |
|||
1,722,277,520.8821 | clustering | leftvote | [
"",
""
] | 7d33a06aa1a24155b8c2128567b41440 | intfloat/e5-mistral-7b-instruct | [
"Apple",
"Hammer",
"Dog",
"Guitar",
"Banana",
"Screwdriver",
"Cat",
"Piano",
"Orange",
"Wrench",
"Rabbit",
"Violin"
] | 4 | 2D (press for 3D) | PCA | KMeans | 4d7cdf865a6141ff9a0097f1840b007c | BAAI/bge-large-en-v1.5 | [
"Apple",
"Hammer",
"Dog",
"Guitar",
"Banana",
"Screwdriver",
"Cat",
"Piano",
"Orange",
"Wrench",
"Rabbit",
"Violin"
] | 4 | 2D (press for 3D) | PCA | KMeans |
|||
1,722,277,608.0973 | clustering | leftvote | [
"",
""
] | b5be4399c1d3491487992d132ed1ba24 | sentence-transformers/all-MiniLM-L6-v2 | [
"Apple",
"Hammer",
"Dog",
"Guitar",
"Banana",
"Screwdriver",
"Cat",
"Piano",
"Orange",
"Wrench",
"Rabbit",
"Violin"
] | 4 | 2D (press for 3D) | PCA | KMeans | 66410239cb954ef282b1131aaf0aba1b | Salesforce/SFR-Embedding-2_R | [
"Apple",
"Hammer",
"Dog",
"Guitar",
"Banana",
"Screwdriver",
"Cat",
"Piano",
"Orange",
"Wrench",
"Rabbit",
"Violin"
] | 4 | 2D (press for 3D) | PCA | KMeans |
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