Scheduled Commit

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

@@ -18,3 +18,7 @@
 {"tstamp": 1723244432.8169, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1723244432.7263, "finish": 1723244432.8169, "ip": "", "conv_id": "2268ca3b3f3c4d5081bf7e038205cea0", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["shovel", "trowel", "rake", "wheelbarrow", "watering can", "pruning shears", "tornado", "hurricane", "volcanic", "fold", "dome", "plateau", "block", "igneous", "metamorphic", "sedimentary", "nitrogen", "oxygen", "sodium", "B1", "D", "C", "B12", "piano", "violin", "drums", "AWS", "Azure", "yuan", "yen", "rupee", "franc", "euro", "pound", "dollar", "lion", "elephant", "giraffe", "dolphin", "tiger", "koala"], "ncluster": 10, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1723244469.0502, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723244468.9508, "finish": 1723244469.0502, "ip": "", "conv_id": "3474bb2758564aa997434314250ddf8d", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["shovel", "trowel", "rake", "wheelbarrow", "watering can", "pruning shears", "tornado", "hurricane", "volcanic", "fold", "dome", "plateau", "block", "igneous", "metamorphic", "sedimentary", "nitrogen", "oxygen", "sodium", "B1", "D", "C", "B12", "piano", "violin", "drums", "AWS", "Azure", "yuan", "yen", "rupee", "franc", "euro", "pound", "dollar", "lion", "elephant", "giraffe", "dolphin", "tiger", "koala", "galaxy", "planet", "nebula", "comet", "linen", "wool", "leather", "dollar", "franc", "rupee", "E", "D", "B12", "C", "K", "Mandarin", "Arabic", "Spanish", "Russian", "English"], "ncluster": 15, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1723244469.0502, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1723244468.9508, "finish": 1723244469.0502, "ip": "", "conv_id": "2268ca3b3f3c4d5081bf7e038205cea0", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["shovel", "trowel", "rake", "wheelbarrow", "watering can", "pruning shears", "tornado", "hurricane", "volcanic", "fold", "dome", "plateau", "block", "igneous", "metamorphic", "sedimentary", "nitrogen", "oxygen", "sodium", "B1", "D", "C", "B12", "piano", "violin", "drums", "AWS", "Azure", "yuan", "yen", "rupee", "franc", "euro", "pound", "dollar", "lion", "elephant", "giraffe", "dolphin", "tiger", "koala", "galaxy", "planet", "nebula", "comet", "linen", "wool", "leather", "dollar", "franc", "rupee", "E", "D", "B12", "C", "K", "Mandarin", "Arabic", "Spanish", "Russian", "English"], "ncluster": 15, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723281313.7359, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1723281313.6899, "finish": 1723281313.7359, "ip": "", "conv_id": "92cd378671ef4ca5999e05ae874f9518", "model_name": "embed-english-v3.0", "prompt": ["Diabetes "], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723281313.7359, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723281313.6899, "finish": 1723281313.7359, "ip": "", "conv_id": "c490ac576f5b4a7abe2a77ad8953a7c1", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Diabetes "], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723281346.3405, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1723281346.2952, "finish": 1723281346.3405, "ip": "", "conv_id": "92cd378671ef4ca5999e05ae874f9518", "model_name": "embed-english-v3.0", "prompt": ["Diabetes "], "ncluster": 1, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723281346.3405, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723281346.2952, "finish": 1723281346.3405, "ip": "", "conv_id": "c490ac576f5b4a7abe2a77ad8953a7c1", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Diabetes "], "ncluster": 1, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}

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

@@ -33,3 +33,4 @@
 {"tstamp": 1723244179.5866, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "f095c576fd2240f3a515c5e1e7cdf7ef", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Why did the machines in The Matrix keep humans around?", "0_output": [["Why did the machines in The Matrix keep humans around?", "Stackoverflow Stackexchange\n\nQ: Can't install gatsby-plugin-sharp.(libpng-dev may not installed) OS: Win 10\nwindows-build-tools is installed.\nWhen I was trying to install gatsby-plugin-sharp with npm install --save gatsby-plugin-sharp. I received two errors.\nI've tried this npm install --save-dev pngquant --msvs_version=2013  as someone mentioned. But it caused the same error with no 'libpng-dev'.\n\\> [email protected] postinstall C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\mozjpeg\n\\> node lib/install.js\n\n‼ getaddrinfo ENOENT raw.githubusercontent.com\n‼ mozjpeg pre-build test failed\ni compiling from source\n× Error: Command failed: C:\\Windows\\system32\\cmd.exe /s /c \"autoreconf -fiv\"\n'autoreconf' �����ڲ����ⲿ���Ҳ���ǿ����еij���\n���������ļ���\n\nat C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\bin-build\\node_modules\\execa\\index.js:231:11\nat runMicrotasks (<anonymous>)\nat processTicksAndRejections (internal/process/task_queues.js:97:5)\nat async Promise.all (index 0)\n\n\\> [email protected] postinstall C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\pngquant-bin\n\\> node lib/install.js\n\n‼ getaddrinfo ENOENT raw.githubusercontent.com\n‼ pngquant pre-build test failed\ni compiling from source\n× Error: pngquant failed to build, make sure that libpng-dev is installed\nat C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\bin-build\\node_modules\\execa\\index.js:231:11\nat processTicksAndRejections (internal/process/task_queues.js:97:5)\nat async Promise.all (index 0)\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "85875ef025614bdcba923a3349cbb1ac", "1_model_name": "voyage-multilingual-2", "1_prompt": "Why did the machines in The Matrix keep humans around?", "1_output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why do the machines allow/initiate the building of a new Zion? In the Matrix Reloaded, the Architect says:\n\nThe function of the One is now to return to the Source, allowing a temporary dissemination of the code you carry, reinserting the prime program. After which, you will be required to select from the Matrix 23 individuals - 16 female, 7 male - to rebuild Zion. Failure to comply with this process will result in a cataclysmic system crash, killing everyone connected to the Matrix, which, coupled with the extermination of Zion, will ultimately result in the extinction of the entire human race.\n\nWhy do the machines initiate the re-building of Zion?\n\nA: As was discussed elsewhere on the site, the machines are not interested in humans being wiped out, they (as shown in Animatrix) just want to co-exist with humans.\nAs such, the machines allow the building of new Zion so that humans would not - as the quote you provided says - would not go extinct.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1723244189.7675, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "d2d08c14b2ca445ebbf5e81ecf0b54c3", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Self-ensemble and self-distillation techniques for enhancing BERT performance", "0_output": [["Self-ensemble and self-distillation techniques for enhancing BERT performance", "Title: Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation\n\nAbstract: Fine-tuning pre-trained language models like BERT has become an effective way in NLP and yields state-of-the-art results on many downstream tasks. Recent studies on adapting BERT to new tasks mainly focus on modifying the model structure, re-designing the pre-train tasks, and leveraging external data and knowledge. The fine-tuning strategy itself has yet to be fully explored. In this paper, we improve the fine-tuning of BERT with two effective mechanisms: self-ensemble and self-distillation. The experiments on text classification and natural language inference tasks show our proposed methods can significantly improve the adaption of BERT without any external data or knowledge."]], "0_corpus": "arxiv", "1_conv_id": "bcbe43adcdf64637853373f624ae96f5", "1_model_name": "BM25", "1_prompt": "Self-ensemble and self-distillation techniques for enhancing BERT performance", "1_output": [["Self-ensemble and self-distillation techniques for enhancing BERT performance", "Title: Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation\n\nAbstract: Fine-tuning pre-trained language models like BERT has become an effective way in NLP and yields state-of-the-art results on many downstream tasks. Recent studies on adapting BERT to new tasks mainly focus on modifying the model structure, re-designing the pre-train tasks, and leveraging external data and knowledge. The fine-tuning strategy itself has yet to be fully explored. In this paper, we improve the fine-tuning of BERT with two effective mechanisms: self-ensemble and self-distillation. The experiments on text classification and natural language inference tasks show our proposed methods can significantly improve the adaption of BERT without any external data or knowledge."]], "1_corpus": "arxiv"}
 {"tstamp": 1723280461.3143, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "cfac6fee7d94495d9cd962a43fa5fecc", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Bäst och billigast kan att beställa från till Sverige ", "0_output": [["Bäst och billigast kan att beställa från till Sverige ", "Ajinomoto\n\nBizimMutfak (Turkey) YumYum (Thailand, Indonesia, Europe, Japan, Philippines)\nVono (Brazil)\nA&M (India)●\nMaDish (Nigeria)\nTasty Boy (Philippines)\nAjinomen (Peru, Colombia, Chile)\nCrispy Fry (Philippines)\nChicken Savor (Philippines)\nPork Savor (Philippines)\nFlavor Up (Philippines)\nJust Deli (Poland)\nRasa Sifu (Malaysia)\nSeri-Aji (Malaysia)\nSajiku (Indonesia)\nSAORI (Indonesia)\nMayumi (Indonesia)\nNIKUPLUS (Indonesia)\nGARLICOPLUS (Indonesia)\nPILIPLUS (Indonesia)\nplus+ (Thailand)\nBanh Ran (Vietnam)\nKho Quet (Vietnam)\nmiskísimoo (Peru)\nFrozen foods\nTaipei (USA)●\nLingLing (USA)●\nJoseOle (USA)\nPosada (USA)\nBernardi (USA)\nFreds (USA)\nWhitley's (USA)\nChilli Bowl (USA)\nGolden Tiger (USA)\nAjinomoto Frozen Foods (USA, Europe, Thailand)\nGyoza (USA, Europe, Brazil, Singapore, Thailand, Philippines, Malaysia, Indonesia, Australia, New Zealand)●\nYakitori (France)\nRamen (France)\nYasaï Men (France)\nSpring Roll (Russia)\nVarious cakes (China)\nCRISPY FRIED CHICKEN\nSHRIMP SHUMAI\nSweetener\nPal Sweet (Japan, Malaysia, Singapore)\nLite Sugar (Thailand)\nCoffee products\nMaxim instant coffee\nBlendy●\nSen\nMarim\nBirdy (Thailand, Indonesia, Vietnam)●\nPowder beverages\nProttie (Thailand)\nFres-C (Philippines)\nFIT (Brazil)\nMid (Brazil)\nProttie (Philippines)\nVtox Vinegar drink (Vietnam)\nKEMAL KÜKRER Vinegar drink (Turkey)\nFertilizer\nAJIFOL (Brazil, Peru, Thailand, Indonesia, Vietnam, USA)\nAgriTecno Biostimulant (EU, China, Africa)\nPEK (Spain)\nAjinomoto animal nutrition\nL-Lysine\nL-Threonine\nL-Tryptophan\nAjiPro-L●\nSports nutrition\namino VITAL[PRO, Gold, Amino Protein, amino shot] (Japan, Thailand, Indonesia, Philippines, Brazil, Malaysia, Taiwan)\nAjiPure (USA)\nFusi-BCAA (USA)\naminoVITAL jelly (Thailand)\nAminoMOF (Thailand)"]], "0_corpus": "wikipedia", "1_conv_id": "744cc4fa2e7d45ed80e1b066a0470a86", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Bäst och billigast kan att beställa från till Sverige ", "1_output": [["Bäst och billigast kan att beställa från till Sverige ", "Ja, vi elsker dette landet\n\nIn the early 19th century, the song \"Norges Skaal\" was regarded by many as a de facto national anthem. From 1820, the song \"Norsk Nationalsang\" () became the most recognised national anthem. It came to be known as \"Sønner av Norge\" (originally \"Sønner af Norge\"), after its first stanza. \"Sønner av Norge\" was written by Henrik Anker Bjerregaard (1792–1842) and the melody by Christian Blom (1782–1861), after the Royal Norwegian Society for Development had announced a competition to write a national anthem for Norway in 1819. \"Norsk Nationalsang\" (\"Sønner af Norge\") was announced as the winner.  (also called \"Nordmandssang\") by Ole Vig has also been used as a national anthem. Henrik Wergeland also wrote an anthem originally titled \"Smaagutternes Nationalsang\" (\"The Young Boys' National Anthem\") and commonly known as \"Vi ere en Nation, vi med\".\n\"Ja, vi elsker dette landet\" was written by Bjørnstjerne Bjørnson and composed by Rikard Nordraak between 1859 and 1868, and gradually came to replace \"Sønner av Norge\" as the most recognised national anthem. Until the early 20th century, \"Sønner av Norge\" and \"Ja, vi elsker dette landet\" were used alongside each other, but \"Sønner av Norge\" was preferred in official settings. Since 2011, the anthem Mitt lille land by Ole Paus has also been called a \"new national anthem\" and notably featured in the memorial ceremonies following the 2011 Norway attacks. On Norwegian Constitution Day in 2012, the NRK broadcast opened with \"Mitt lille land.\"\nIn addition, Norway has an unofficial royal anthem, \"Kongesangen\", based on \"God Save the King\" and written in its modern form by Gustav Jensen. The psalm \"Gud signe vårt dyre fedreland\", written by Elias Blix and with a melody by Christoph Ernst Friedrich Weyse, is often called Norway's \"national psalm\"."]], "1_corpus": "wikipedia"}
+{"tstamp": 1723281233.3892, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "6bbe7d9c5b7c47daaeb8b82129dc475b", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Diabetes ", "0_output": [["Diabetes ", "Diabetes\n\nDiabetes mellitus, often known simply as diabetes, is a group of common endocrine diseases characterized by sustained high blood sugar levels. Diabetes is due to either the pancreas not producing enough insulin, or the cells of the body becoming unresponsive to the hormone's effects. Classic symptoms include thirst, polyuria, weight loss, and blurred vision. If left untreated, the disease can lead to various health complications, including disorders of the cardiovascular system, eye, kidney, and nerves. Diabetes accounts for approximately 4.2 million deaths every year, with an estimated 1.5 million caused by either untreated or poorly treated diabetes."]], "0_corpus": "wikipedia", "1_conv_id": "694e8664bd9a4309b31452bf5dd45086", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Diabetes ", "1_output": [["Diabetes ", "Diabetes\n\nDiabetes mellitus, often known simply as diabetes, is a group of common endocrine diseases characterized by sustained high blood sugar levels. Diabetes is due to either the pancreas not producing enough insulin, or the cells of the body becoming unresponsive to the hormone's effects. Classic symptoms include thirst, polyuria, weight loss, and blurred vision. If left untreated, the disease can lead to various health complications, including disorders of the cardiovascular system, eye, kidney, and nerves. Diabetes accounts for approximately 4.2 million deaths every year, with an estimated 1.5 million caused by either untreated or poorly treated diabetes."]], "1_corpus": "wikipedia"}

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

@@ -112,3 +112,5 @@
 {"tstamp": 1723269555.0888, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1723269552.6562, "finish": 1723269555.0888, "ip": "", "conv_id": "710cd3f1998a4eada99d1346b5c048a2", "model_name": "text-embedding-004", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
 {"tstamp": 1723280442.953, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1723280442.6808, "finish": 1723280442.953, "ip": "", "conv_id": "cfac6fee7d94495d9cd962a43fa5fecc", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Bäst och billigast kan att beställa från till Sverige ", "output": [["Bäst och billigast kan att beställa från till Sverige ", "Ajinomoto\n\nBizimMutfak (Turkey) YumYum (Thailand, Indonesia, Europe, Japan, Philippines)\nVono (Brazil)\nA&M (India)●\nMaDish (Nigeria)\nTasty Boy (Philippines)\nAjinomen (Peru, Colombia, Chile)\nCrispy Fry (Philippines)\nChicken Savor (Philippines)\nPork Savor (Philippines)\nFlavor Up (Philippines)\nJust Deli (Poland)\nRasa Sifu (Malaysia)\nSeri-Aji (Malaysia)\nSajiku (Indonesia)\nSAORI (Indonesia)\nMayumi (Indonesia)\nNIKUPLUS (Indonesia)\nGARLICOPLUS (Indonesia)\nPILIPLUS (Indonesia)\nplus+ (Thailand)\nBanh Ran (Vietnam)\nKho Quet (Vietnam)\nmiskísimoo (Peru)\nFrozen foods\nTaipei (USA)●\nLingLing (USA)●\nJoseOle (USA)\nPosada (USA)\nBernardi (USA)\nFreds (USA)\nWhitley's (USA)\nChilli Bowl (USA)\nGolden Tiger (USA)\nAjinomoto Frozen Foods (USA, Europe, Thailand)\nGyoza (USA, Europe, Brazil, Singapore, Thailand, Philippines, Malaysia, Indonesia, Australia, New Zealand)●\nYakitori (France)\nRamen (France)\nYasaï Men (France)\nSpring Roll (Russia)\nVarious cakes (China)\nCRISPY FRIED CHICKEN\nSHRIMP SHUMAI\nSweetener\nPal Sweet (Japan, Malaysia, Singapore)\nLite Sugar (Thailand)\nCoffee products\nMaxim instant coffee\nBlendy●\nSen\nMarim\nBirdy (Thailand, Indonesia, Vietnam)●\nPowder beverages\nProttie (Thailand)\nFres-C (Philippines)\nFIT (Brazil)\nMid (Brazil)\nProttie (Philippines)\nVtox Vinegar drink (Vietnam)\nKEMAL KÜKRER Vinegar drink (Turkey)\nFertilizer\nAJIFOL (Brazil, Peru, Thailand, Indonesia, Vietnam, USA)\nAgriTecno Biostimulant (EU, China, Africa)\nPEK (Spain)\nAjinomoto animal nutrition\nL-Lysine\nL-Threonine\nL-Tryptophan\nAjiPro-L●\nSports nutrition\namino VITAL[PRO, Gold, Amino Protein, amino shot] (Japan, Thailand, Indonesia, Philippines, Brazil, Malaysia, Taiwan)\nAjiPure (USA)\nFusi-BCAA (USA)\naminoVITAL jelly (Thailand)\nAminoMOF (Thailand)"]], "corpus": "wikipedia"}
 {"tstamp": 1723280442.953, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1723280442.6808, "finish": 1723280442.953, "ip": "", "conv_id": "744cc4fa2e7d45ed80e1b066a0470a86", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Bäst och billigast kan att beställa från till Sverige ", "output": [["Bäst och billigast kan att beställa från till Sverige ", "Ja, vi elsker dette landet\n\nIn the early 19th century, the song \"Norges Skaal\" was regarded by many as a de facto national anthem. From 1820, the song \"Norsk Nationalsang\" () became the most recognised national anthem. It came to be known as \"Sønner av Norge\" (originally \"Sønner af Norge\"), after its first stanza. \"Sønner av Norge\" was written by Henrik Anker Bjerregaard (1792–1842) and the melody by Christian Blom (1782–1861), after the Royal Norwegian Society for Development had announced a competition to write a national anthem for Norway in 1819. \"Norsk Nationalsang\" (\"Sønner af Norge\") was announced as the winner.  (also called \"Nordmandssang\") by Ole Vig has also been used as a national anthem. Henrik Wergeland also wrote an anthem originally titled \"Smaagutternes Nationalsang\" (\"The Young Boys' National Anthem\") and commonly known as \"Vi ere en Nation, vi med\".\n\"Ja, vi elsker dette landet\" was written by Bjørnstjerne Bjørnson and composed by Rikard Nordraak between 1859 and 1868, and gradually came to replace \"Sønner av Norge\" as the most recognised national anthem. Until the early 20th century, \"Sønner av Norge\" and \"Ja, vi elsker dette landet\" were used alongside each other, but \"Sønner av Norge\" was preferred in official settings. Since 2011, the anthem Mitt lille land by Ole Paus has also been called a \"new national anthem\" and notably featured in the memorial ceremonies following the 2011 Norway attacks. On Norwegian Constitution Day in 2012, the NRK broadcast opened with \"Mitt lille land.\"\nIn addition, Norway has an unofficial royal anthem, \"Kongesangen\", based on \"God Save the King\" and written in its modern form by Gustav Jensen. The psalm \"Gud signe vårt dyre fedreland\", written by Elias Blix and with a melody by Christoph Ernst Friedrich Weyse, is often called Norway's \"national psalm\"."]], "corpus": "wikipedia"}
+{"tstamp": 1723281219.6418, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723281217.4654, "finish": 1723281219.6418, "ip": "", "conv_id": "6bbe7d9c5b7c47daaeb8b82129dc475b", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Diabetes ", "output": [["Diabetes ", "Diabetes\n\nDiabetes mellitus, often known simply as diabetes, is a group of common endocrine diseases characterized by sustained high blood sugar levels. Diabetes is due to either the pancreas not producing enough insulin, or the cells of the body becoming unresponsive to the hormone's effects. Classic symptoms include thirst, polyuria, weight loss, and blurred vision. If left untreated, the disease can lead to various health complications, including disorders of the cardiovascular system, eye, kidney, and nerves. Diabetes accounts for approximately 4.2 million deaths every year, with an estimated 1.5 million caused by either untreated or poorly treated diabetes."]], "corpus": "wikipedia"}
+{"tstamp": 1723281219.6418, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1723281217.4654, "finish": 1723281219.6418, "ip": "", "conv_id": "694e8664bd9a4309b31452bf5dd45086", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Diabetes ", "output": [["Diabetes ", "Diabetes\n\nDiabetes mellitus, often known simply as diabetes, is a group of common endocrine diseases characterized by sustained high blood sugar levels. Diabetes is due to either the pancreas not producing enough insulin, or the cells of the body becoming unresponsive to the hormone's effects. Classic symptoms include thirst, polyuria, weight loss, and blurred vision. If left untreated, the disease can lead to various health complications, including disorders of the cardiovascular system, eye, kidney, and nerves. Diabetes accounts for approximately 4.2 million deaths every year, with an estimated 1.5 million caused by either untreated or poorly treated diabetes."]], "corpus": "wikipedia"}

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

@@ -23,3 +23,5 @@
 {"tstamp": 1723265563.0199, "task_type": "sts", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723265562.9917, "finish": 1723265563.0199, "ip": "", "conv_id": "0c83655ca78f4b1fb5b4a7b0d7741958", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "txt0": "A child has no arms.", "txt1": "There is a child and a birdhouse.", "txt2": "A child hugs a birdhouse.", "output": ""}
 {"tstamp": 1723265563.0199, "task_type": "sts", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723265562.9917, "finish": 1723265563.0199, "ip": "", "conv_id": "cfead04ab2494b6c905e33bad10c54b7", "model_name": "BAAI/bge-large-en-v1.5", "txt0": "A child has no arms.", "txt1": "There is a child and a birdhouse.", "txt2": "A child hugs a birdhouse.", "output": ""}
 {"tstamp": 1723265808.2323, "task_type": "sts", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723265808.2177, "finish": 1723265808.2323, "ip": "", "conv_id": "1a20fb358a8b4448aca4f8e3c1adc5bf", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "txt0": "Well, probably the favorite book I had, this is a little different I had The Giant Golden Book of Astronomy, which I got when I was in the second grade and I read it cover to cover till I wore it out.", "txt1": "I didn't read any books on astronomy until I was in the 10th grade.", "txt2": "I loved The Giant Golden Book of Astronomy.", "output": ""}
+{"tstamp": 1723281386.6476, "task_type": "sts", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1723281386.4634, "finish": 1723281386.6476, "ip": "", "conv_id": "572d7ce82e254184a09e75e178d1cf0f", "model_name": "embed-english-v3.0", "txt0": "Diabetes ", "txt1": "Insulin ", "txt2": "Metformin ", "output": ""}
+{"tstamp": 1723281386.6476, "task_type": "sts", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1723281386.4634, "finish": 1723281386.6476, "ip": "", "conv_id": "dbd7ee5e64ec4eca930dfcce7b0a0dbc", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "txt0": "Diabetes ", "txt1": "Insulin ", "txt2": "Metformin ", "output": ""}