Scheduled Commit

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

@@ -22,3 +22,7 @@
 {"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"}
+{"tstamp": 1723366278.5651, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1723366278.5194, "finish": 1723366278.5651, "ip": "", "conv_id": "0cfc2ce80b0e4c7db1b0ad5f10162fb0", "model_name": "embed-english-v3.0", "prompt": ["JT-QP-01 文件控制程序"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723366278.5651, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723366278.5194, "finish": 1723366278.5651, "ip": "", "conv_id": "048c18c5f78944eaa5c9d01d76d57300", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["JT-QP-01 文件控制程序"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723366291.6769, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1723366291.3896, "finish": 1723366291.6769, "ip": "", "conv_id": "0cfc2ce80b0e4c7db1b0ad5f10162fb0", "model_name": "embed-english-v3.0", "prompt": ["JT-QP-01 文件控制程序", "temperate", "tropical", "boreal", "physics", "mathematics", "history", "biology", "rake", "hoe", "watering can", "shovel", "trowel", "wheelbarrow", "cowboy hat", "beanie", "fantasy", "science fiction", "mystery"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723366291.6769, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1723366291.3896, "finish": 1723366291.6769, "ip": "", "conv_id": "048c18c5f78944eaa5c9d01d76d57300", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["JT-QP-01 文件控制程序", "temperate", "tropical", "boreal", "physics", "mathematics", "history", "biology", "rake", "hoe", "watering can", "shovel", "trowel", "wheelbarrow", "cowboy hat", "beanie", "fantasy", "science fiction", "mystery"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

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

@@ -57,3 +57,4 @@
 {"tstamp": 1723365869.0617, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "5c5b50797e6c4a96bf04056830147eaf", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "小明吃了小月的面包后把面包包装还给了小明，请问面包包装现在在谁手上？", "0_output": [["小明吃了小月的面包后把面包包装还给了小明，请问面包包装现在在谁手上？", "Direct function\n\na            {⍵[⍋⍵]}⍤1 ⊢a        ({⍵[⍋⍵]}⍤1 {⊂⍵}⌸ ⊢) a\npats         apst                ┌────┬────┬────┐\nspat         apst                │pats│teas│star│\nteas         aest                │spat│sate│    │\nsate         aest                │taps│etas│    │\ntaps         apst                │past│seat│    │\netas         aest                │    │eats│    │\npast         apst                │    │tase│    │\nseat         aest                │    │east│    │\neats         aest                │    │seta│    │\ntase         aest                └────┴────┴────┘\nstar         arst\neast         aest\nseta         aest"]], "0_corpus": "wikipedia", "1_conv_id": "5b11790537904306952230f8efd92d6f", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "小明吃了小月的面包后把面包包装还给了小明，请问面包包装现在在谁手上？", "1_output": [["小明吃了小月的面包后把面包包装还给了小明，请问面包包装现在在谁手上？", "Xu Xiaodong\n\nXu Xiaodong ( (Pinyin: Xú  Xiǎodōng); born 15 November 1979), is a Chinese mixed martial artist (MMA) who is known for challenging and fighting fraudulent and traditional martial artists. He gained prominence online after he was filmed defeating self-proclaimed Tai chi master Wei Lei in 2017."]], "1_corpus": "wikipedia"}
 {"tstamp": 1723365905.027, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "0941b7c830b8467a998a3502f577a45b", "0_model_name": "text-embedding-004", "0_prompt": "小明吃了小月的面包后把面包包装还给了小明，请问面包包装现在在谁手上？", "0_output": [["小明吃了小月的面包后把面包包装还给了小明，请问面包包装现在在谁手上？", "Chinese pronouns\n\nOther, rarer new written pronouns in the second person are nǐ ( \"you, a deity\"), nǐ ( \"you, a male\"), and nǐ ( \"you, a female\"). In the third person, they are tā ( \"it, an animal\"), tā ( \"it, a deity\"), and tā ( \"it, an inanimate object\"). Among users of traditional Chinese characters, these distinctions are only made in Taiwanese Mandarin; in simplified Chinese, tā () is the only third-person non-human form and nǐ () is the only second person form. The third person distinction between \"he\" () and \"she\" () remain in use in all forms of written standard Mandarin.\nIn the early 21st century, some members of genderfluid and queer Chinese online communities started using X也 and TA to refer to a generic, anonymous, or non-binary third person. As of June 2022, neither have been encoded as a single code point in Unicode, and neither are considered standard usage. Since at least 2014, Bilibili has used TA in its user pages.\nAdditional notes\nThe first-person pronouns  ǎn and  ǒu \"I\" are infrequently used in Mandarin conversation. They are of dialectal origin. However, their usage is gaining popularity among the young, most notably in online communications.\nAccording to Wang Li, the second person formal pronoun nín ( \"you, formal; polite\") is derived from the fusion of the second person plural nǐmen ( \"you, formal; polite\"), making it somewhat analogous to the T-V distinction in Romance languages.  Consistent with this hypothesized origin, *nínmen is traditionally considered to be a grammatically incorrect expression for the formal second person plural.  Instead, the alternative phrases dàjiā (大家, \"you, formal plural\") and gèwèi  (各位, \"you, formal plural\") are used, with the latter being somewhat more formal than the former.  In addition, some dialects use an analogous formal third person pronoun tān (怹, \"he/she, formal; polite\")."]], "0_corpus": "wikipedia", "1_conv_id": "8d239d2ff1114e7cb3a6260e4712393c", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "小明吃了小月的面包后把面包包装还给了小明，请问面包包装现在在谁手上？", "1_output": [["小明吃了小月的面包后把面包包装还给了小明，请问面包包装现在在谁手上？", "Butterbrot\n\nNormally, сэндвич Runglish-y word for sandwich is used in Russian for two slices of bread with some ingredients in between, and the very word \"sandwich\" implies \"flat X between two flat Y\" idea in Runglish (see \"сэндвич-панель\").\nHowever, open sandwich  is a \"бутерброд\" for a Russian.\nUrban legends\nButterbrot is said to always fall to the floor (and especially on carpet) with the buttered side downwards; an example of Murphy's law. A common explanation is that the top side is usually heavier than the bottom side, particularly if the bread has additional toppings such as a spread. Another is tied to the common height of tables. The subject has been researched by various sources, including the German children's series Die Sendung mit der Maus, and the scientific German TV series Quarks & Co.\nIt is often joked about what would happen if Butterbrot is tied to the back of a cat, in the same manner that hypothetical buttered toast attached to the back of a cat is sometimes joked about, with it being debated whether the cat would still honour the popular axiom that a cat \"always lands on its feet\", or if the Butterbrot would be \"stronger\", making the cat fall on its back — alternatively, it is sometimes humorously suggested that the cat would simply levitate, as it would be unable to satisfy both criteria for landing."]], "1_corpus": "wikipedia"}
 {"tstamp": 1723366112.8085, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "7d91350d144543f190c415f6df188816", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "机器学习的主要算法有哪些？", "0_output": [["机器学习的主要算法有哪些？", "Machine learning\n\nMachine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.\nML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. When applied to business problems, it is known under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."]], "0_corpus": "wikipedia", "1_conv_id": "c61618d0f41e4415af9f66d2d3c0165e", "1_model_name": "text-embedding-004", "1_prompt": "机器学习的主要算法有哪些？", "1_output": [["机器学习的主要算法有哪些？", "Interrogative word\n\nA particular type of interrogative word is the interrogative particle, which serves to convert a statement into a yes–no question, without having any other meaning. Examples include est-ce que in French, ли li in Russian, czy in Polish, чи chy in Ukrainian, ĉu in Esperanto, āyā آیا in Persian, কি ki in Bengali, / ma in Mandarin Chinese, ///Finnish and Turkish have vowel harmony, see more here in Turkish, pa in Ladin,  ka in Japanese,  kka in Korean, ko/kö in Finnish, (да) ли (da) li in Serbo-Croatian and  and  in Basque. \"Is it true that...\" and \"... right?\" would be a similar construct in English. Such particles contrast with other interrogative words, which form what are called wh-questions rather than yes–no questions.\nFor more information about the grammatical rules for using formed questions in various languages, see Interrogative.\nIn English\nInterrogative words in English can serve as interrogative determiners, interrogative pronouns, or interrogative adverbs. Certain pronominal adverbs may also be used as interrogative words, such as whereby or wherefore.\nInterrogative determiner\nThe interrogative words which, what, and whose are interrogative determiners when used to prompt the specification of a presented noun or noun phrase such as in the question Which farm is the largest? where the interrogative determiner which prompts specification of the noun farm. In the question Whose gorgeous, pink painting is that?, whose is the interrogative, personal, possessive determiner prompting a specification for the possessor of the noun phrase gorgeous pink painting."]], "1_corpus": "wikipedia"}
+{"tstamp": 1723366146.8638, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "44af829c4c324ab8b7f6e85f17c8c65e", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "机器学习的主要算法有哪些？", "0_output": [["机器学习的主要算法有哪些？", "Machine learning\n\nMachine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.\nML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. When applied to business problems, it is known under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."]], "0_corpus": "wikipedia", "1_conv_id": "6109dc31bdbb4daea28cbf565be982bb", "1_model_name": "text-embedding-3-large", "1_prompt": "机器学习的主要算法有哪些？", "1_output": [["机器学习的主要算法有哪些？", "Outline of machine learning\n\nALOPEX Backpropagation\nBootstrap aggregating\nCN2 algorithm\nConstructing skill trees\nDehaene–Changeux model\nDiffusion map\nDominance-based rough set approach\nDynamic time warping\nError-driven learning\nEvolutionary multimodal optimization\nExpectation–maximization algorithm\nFastICA\nForward–backward algorithm\nGeneRec\nGenetic Algorithm for Rule Set Production\nGrowing self-organizing map\nHyper basis function network\nIDistance\nk-nearest neighbors algorithm\nKernel methods for vector output\nKernel principal component analysis\nLeabra\nLinde–Buzo–Gray algorithm\nLocal outlier factor\nLogic learning machine\nLogitBoost\nManifold alignment\nMarkov chain Monte Carlo (MCMC)\nMinimum redundancy feature selection\nMixture of experts\nMultiple kernel learning\nNon-negative matrix factorization\nOnline machine learning\nOut-of-bag error\nPrefrontal cortex basal ganglia working memory\nPVLV\nQ-learning\nQuadratic unconstrained binary optimization\nQuery-level feature\nQuickprop\nRadial basis function network\nRandomized weighted majority algorithm\nReinforcement learning\nRepeated incremental pruning to produce error reduction (RIPPER)\nRprop\nRule-based machine learning\nSkill chaining\nSparse PCA\nState–action–reward–state–action\nStochastic gradient descent\nStructured kNN\nT-distributed stochastic neighbor embedding\nTemporal difference learning\nWake-sleep algorithm\nWeighted majority algorithm (machine learning)\nMachine learning methods\nInstance-based algorithm\nK-nearest neighbors algorithm (KNN)\nLearning vector quantization (LVQ)\nSelf-organizing map (SOM)\nRegression analysis\nLogistic regression\nOrdinary least squares regression (OLSR)\nLinear regression\nStepwise regression\nMultivariate adaptive regression splines (MARS)\nRegularization algorithm\nRidge regression\nLeast Absolute Shrinkage and Selection Operator (LASSO)\nElastic net\nLeast-angle regression (LARS)\nClassifiers\nProbabilistic classifier\nNaive Bayes classifier\nBinary classifier"]], "1_corpus": "wikipedia"}

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

@@ -8,3 +8,4 @@
 {"tstamp": 1723309085.9513, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "21a53ce0ffee4c2a85b9cf83735b5f14", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_txt0": "ruma::events::poll::start", "0_txt1": "matrix_sdk::executor::JoinHandle", "0_txt2": "matrix poll start", "0_output": "", "1_conv_id": "ab96ab63c0c046859e1f722c8c7e32f0", "1_model_name": "text-embedding-3-large", "1_txt0": "ruma::events::poll::start", "1_txt1": "matrix_sdk::executor::JoinHandle", "1_txt2": "matrix poll start", "1_output": ""}
 {"tstamp": 1723309134.2887, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "919f387d53434fc7a35b3860706e818b", "0_model_name": "BAAI/bge-large-en-v1.5", "0_txt0": "ruma::events::poll::start", "0_txt1": "matrix_sdk::executor::JoinHandle", "0_txt2": "matrix poll start", "0_output": "", "1_conv_id": "0a98d5a4ce0e49e0852cff3341748ad8", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_txt0": "ruma::events::poll::start", "1_txt1": "matrix_sdk::executor::JoinHandle", "1_txt2": "matrix poll start", "1_output": ""}
 {"tstamp": 1723309251.5082, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "8f81e3d470db4c838078dcc0fcd875ef", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_txt0": "ruma::events::poll::start::PollStartEventContent", "0_txt1": "matrix_sdk::executor::JoinHandle", "0_txt2": "matrix poll start", "0_output": "", "1_conv_id": "4b7dfeea57f0465688b319a7720bab5e", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_txt0": "ruma::events::poll::start::PollStartEventContent", "1_txt1": "matrix_sdk::executor::JoinHandle", "1_txt2": "matrix poll start", "1_output": ""}
+{"tstamp": 1723366230.3273, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "77d01e2f4dac48f8a0a497ba81d0cb87", "0_model_name": "text-embedding-3-large", "0_txt0": "JT-QP-01 文件控制程序", "0_txt1": "JT-QP-01", "0_txt2": "文件控制程序", "0_output": "", "1_conv_id": "bb8b2ac1e7b541019eddac0579d54d4f", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_txt0": "JT-QP-01 文件控制程序", "1_txt1": "JT-QP-01", "1_txt2": "文件控制程序", "1_output": ""}

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

@@ -31,3 +31,5 @@
 {"tstamp": 1723309110.6313, "task_type": "sts", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1723309110.5969, "finish": 1723309110.6313, "ip": "", "conv_id": "0a98d5a4ce0e49e0852cff3341748ad8", "model_name": "nomic-ai/nomic-embed-text-v1.5", "txt0": "ruma::events::poll::start", "txt1": "matrix_sdk::executor::JoinHandle", "txt2": "matrix poll start", "output": ""}
 {"tstamp": 1723309241.7853, "task_type": "sts", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1723309241.7401, "finish": 1723309241.7853, "ip": "", "conv_id": "8f81e3d470db4c838078dcc0fcd875ef", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "txt0": "ruma::events::poll::start::PollStartEventContent", "txt1": "matrix_sdk::executor::JoinHandle", "txt2": "matrix poll start", "output": ""}
 {"tstamp": 1723309241.7853, "task_type": "sts", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1723309241.7401, "finish": 1723309241.7853, "ip": "", "conv_id": "4b7dfeea57f0465688b319a7720bab5e", "model_name": "jinaai/jina-embeddings-v2-base-en", "txt0": "ruma::events::poll::start::PollStartEventContent", "txt1": "matrix_sdk::executor::JoinHandle", "txt2": "matrix poll start", "output": ""}
+{"tstamp": 1723366220.1551, "task_type": "sts", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723366219.7078, "finish": 1723366220.1551, "ip": "", "conv_id": "77d01e2f4dac48f8a0a497ba81d0cb87", "model_name": "text-embedding-3-large", "txt0": "JT-QP-01 文件控制程序", "txt1": "JT-QP-01", "txt2": "文件控制程序", "output": ""}
+{"tstamp": 1723366220.1551, "task_type": "sts", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723366219.7078, "finish": 1723366220.1551, "ip": "", "conv_id": "bb8b2ac1e7b541019eddac0579d54d4f", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "txt0": "JT-QP-01 文件控制程序", "txt1": "JT-QP-01", "txt2": "文件控制程序", "output": ""}