Scheduled Commit

data/clustering_battle-928d45d9-19ab-4d05-9aae-4b6574886d0d.jsonl

@@ -1,2 +1,3 @@
 {"tstamp": 1722544433.5079, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "39717c6a32f746e688ce69575be3c8d0", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": ["sandals", "boots", "loafers", "flats", "high heels", "kiwi", "banana", "country", "classical", "hip-hop", "electronic", "jazz", "rock"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "de820e41944e4b648d78b3389cc74685", "1_model_name": "embed-english-v3.0", "1_prompt": ["sandals", "boots", "loafers", "flats", "high heels", "kiwi", "banana", "country", "classical", "hip-hop", "electronic", "jazz", "rock"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1722544559.5543, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "8383f4e86f7e45bf93de79d3afd7e00a", "0_model_name": "text-embedding-3-large", "0_prompt": ["Facebook", "LinkedIn", "TikTok", "Instagram", "Snapchat", "Pinterest", "Twitter", "judo", "karate", "kung fu", "taekwondo", "aikido", "sailboat", "kayak", "fold", "plateau", "volcanic"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "f60ef5656d2346798bc0cba646f63702", "1_model_name": "GritLM/GritLM-7B", "1_prompt": ["Facebook", "LinkedIn", "TikTok", "Instagram", "Snapchat", "Pinterest", "Twitter", "judo", "karate", "kung fu", "taekwondo", "aikido", "sailboat", "kayak", "fold", "plateau", "volcanic"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1722582734.4937, "task_type": "clustering", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "ee3ac4d486804d358dc9e6dd753835c5", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung", "Bild", "Welt", "Frankfurter Allgemeine Zeitung"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "3b2826cf43a0465c81431ee523f768d7", "1_model_name": "text-embedding-3-large", "1_prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung", "Bild", "Welt", "Frankfurter Allgemeine Zeitung"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-928d45d9-19ab-4d05-9aae-4b6574886d0d.jsonl

@@ -30,3 +30,15 @@
 {"tstamp": 1722562846.5316, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722562843.5572, "finish": 1722562846.5316, "ip": "", "conv_id": "7ded3ef937594c3ebed7e322abf0042e", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["whiskey", "beer", "wine", "agreeableness", "openness", "neuroticism", "extroversion", "conscientiousness", "theocracy", "democracy", "republic", "monarchy", "oligarchy", "dictatorship", "star", "planet", "asteroid", "nebula", "black hole", "galaxy", "comet", "SSD", "RAM", "hard drive"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722579826.8082, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722579826.5952, "finish": 1722579826.8082, "ip": "", "conv_id": "9575c61f065747e3b5416b5ce95c3527", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722579826.8082, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722579826.5952, "finish": 1722579826.8082, "ip": "", "conv_id": "b4c033e3f78647a2a936171789b5a583", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582670.2192, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722582670.1678, "finish": 1722582670.2192, "ip": "", "conv_id": "ee3ac4d486804d358dc9e6dd753835c5", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Tagesschau"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582670.2192, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722582670.1678, "finish": 1722582670.2192, "ip": "", "conv_id": "3b2826cf43a0465c81431ee523f768d7", "model_name": "text-embedding-3-large", "prompt": ["Tagesschau"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582677.5667, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722582677.1381, "finish": 1722582677.5667, "ip": "", "conv_id": "ee3ac4d486804d358dc9e6dd753835c5", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Tagesschau", "Spiegel"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582677.5667, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722582677.1381, "finish": 1722582677.5667, "ip": "", "conv_id": "3b2826cf43a0465c81431ee523f768d7", "model_name": "text-embedding-3-large", "prompt": ["Tagesschau", "Spiegel"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582685.5457, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722582685.0232, "finish": 1722582685.5457, "ip": "", "conv_id": "ee3ac4d486804d358dc9e6dd753835c5", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582685.5457, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722582685.0232, "finish": 1722582685.5457, "ip": "", "conv_id": "3b2826cf43a0465c81431ee523f768d7", "model_name": "text-embedding-3-large", "prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582690.9121, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722582690.4345, "finish": 1722582690.9121, "ip": "", "conv_id": "ee3ac4d486804d358dc9e6dd753835c5", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung", "Bild"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582690.9121, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722582690.4345, "finish": 1722582690.9121, "ip": "", "conv_id": "3b2826cf43a0465c81431ee523f768d7", "model_name": "text-embedding-3-large", "prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung", "Bild"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582711.6236, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722582711.0198, "finish": 1722582711.6236, "ip": "", "conv_id": "ee3ac4d486804d358dc9e6dd753835c5", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung", "Bild", "Welt"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582711.6236, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722582711.0198, "finish": 1722582711.6236, "ip": "", "conv_id": "3b2826cf43a0465c81431ee523f768d7", "model_name": "text-embedding-3-large", "prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung", "Bild", "Welt"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582726.9112, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722582726.3223, "finish": 1722582726.9112, "ip": "", "conv_id": "ee3ac4d486804d358dc9e6dd753835c5", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung", "Bild", "Welt", "Frankfurter Allgemeine Zeitung"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722582726.9112, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722582726.3223, "finish": 1722582726.9112, "ip": "", "conv_id": "3b2826cf43a0465c81431ee523f768d7", "model_name": "text-embedding-3-large", "prompt": ["Tagesschau", "Spiegel", "Süddeutsche Zeitung", "Bild", "Welt", "Frankfurter Allgemeine Zeitung"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-928d45d9-19ab-4d05-9aae-4b6574886d0d.jsonl

@@ -305,3 +305,5 @@
 {"tstamp": 1722582384.337, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722582381.9846, "finish": 1722582384.337, "ip": "", "conv_id": "09f738a5f93f485f94f313b688846bbd", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "What distinguished global and specialized tuberculosis strains?", "output": [["What distinguished global and specialized tuberculosis strains?", "Mycobacterium tuberculosis\n\nUntil the early 2000s, M. tuberculosis strains were typed by pulsed field gel electrophoresis. This has now been superseded by variable numbers of tandem repeats (VNTR), which is technically easier to perform and allows better discrimination between strains. This method makes use of the presence of repeated DNA sequences within the M. tuberculosis genome.\nThree generations of VNTR typing for M. tuberculosis are noted. The first scheme, called exact tandem repeat, used only five loci, but the resolution afforded by these five loci was not as good as PFGE. The second scheme, called mycobacterial interspersed repetitive unit, had discrimination as good as PFGE. The third generation (mycobacterial interspersed repetitive unit – 2) added a further nine loci to bring the total to 24. This provides a degree of resolution greater than PFGE and is currently the standard for typing M. tuberculosis. However, with regard to archaeological remains, additional evidence may be required because of possible contamination from related soil bacteria.\nAntibiotic resistance in M. tuberculosis typically occurs due to either the accumulation of mutations in the genes targeted by the antibiotic or a change in titration of the drug. M. tuberculosis is considered to be multidrug-resistant (MDR TB) if it has developed drug resistance to both rifampicin and isoniazid, which are the most important antibiotics used in treatment. Additionally, extensively drug-resistant M. tuberculosis (XDR TB) is characterized by resistance to both isoniazid and rifampin, plus any fluoroquinolone and at least one of three injectable second-line drugs (i.e., amikacin, kanamycin, or capreomycin)."]], "corpus": "wikipedia"}
 {"tstamp": 1722582435.1448, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722582432.9495, "finish": 1722582435.1448, "ip": "", "conv_id": "ee929ecb7cb64c23a3e872496a2b3001", "model_name": "GritLM/GritLM-7B", "prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "corpus": "arxiv"}
 {"tstamp": 1722582435.1448, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722582432.9495, "finish": 1722582435.1448, "ip": "", "conv_id": "0daa498a214247d685dee2a2f7ed1ffa", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "corpus": "arxiv"}
+{"tstamp": 1722582762.6457, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722582760.3864, "finish": 1722582762.6457, "ip": "", "conv_id": "e6690bcbdb8549a597ab43ec67512334", "model_name": "voyage-multilingual-2", "prompt": "Could you tell me what they are Stirling numbers?", "output": [["Could you tell me what they are Stirling numbers?", "Stirling number\n\nAs inverse matrices The Stirling numbers of the first and second kinds can be considered inverses of one another:\nand\nwhere  is the Kronecker delta. These two relationships may be understood to be matrix inverse relationships. That is, let s be the lower triangular matrix of Stirling numbers of the first kind, whose matrix elements\nThe inverse of this matrix is S, the lower triangular matrix of Stirling numbers of the second kind, whose entries are   Symbolically, this is written\nAlthough s and S are infinite, so calculating a product entry involves an infinite sum, the matrix multiplications work because these matrices are lower triangular, so only a finite number of terms in the sum are nonzero.\nLah numbers\nThe Lah numbers  are sometimes called Stirling numbers of the third kind.\nBy convention,  and  if  or .\nThese numbers are coefficients expressing falling factorials in terms of rising factorials and vice versa:\nand\nAs above, this means they express the change of basis between the bases  and , completing the diagram.\nIn particular, one formula is the inverse of the other, thus:\nSimilarly, composing the change of basis from  to  with the change of basis from  to  gives the change of basis directly from  to :"]], "corpus": "wikipedia"}
+{"tstamp": 1722582762.6457, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722582760.3864, "finish": 1722582762.6457, "ip": "", "conv_id": "99818342aae44acc84612118190330f7", "model_name": "GritLM/GritLM-7B", "prompt": "Could you tell me what they are Stirling numbers?", "output": [["Could you tell me what they are Stirling numbers?", "Stirling numbers of the second kind\n\nIn mathematics, particularly in combinatorics, a Stirling number of the second kind (or Stirling partition number) is the number of ways to partition a set of n objects into k non-empty subsets and is denoted by  or . Stirling numbers of the second kind occur in the field of  mathematics called combinatorics and the study of partitions. They are named after James Stirling."]], "corpus": "wikipedia"}

data/sts_battle-928d45d9-19ab-4d05-9aae-4b6574886d0d.jsonl

@@ -27,3 +27,4 @@
 {"tstamp": 1722576843.975, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "6a39c8d33c9a4ff9bc79e49403b6c316", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_txt0": "A fire truck.", "0_txt1": "An ambulance?", "0_txt2": "Is it an ambulance?", "0_output": "", "1_conv_id": "11f4580b248244c7a1f7ff126caef2e0", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_txt0": "A fire truck.", "1_txt1": "An ambulance?", "1_txt2": "Is it an ambulance?", "1_output": ""}
 {"tstamp": 1722576851.1458, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "2d0e612ea2794b90ab7d3ee18e007483", "0_model_name": "text-embedding-004", "0_txt0": "A person is in front of a brick wall.", "0_txt1": "A hairy person naps.", "0_txt2": "A bald person in green clothing stands in front of a brick wall.", "0_output": "", "1_conv_id": "845ed50d10904054905e37e741bd98c1", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_txt0": "A person is in front of a brick wall.", "1_txt1": "A hairy person naps.", "1_txt2": "A bald person in green clothing stands in front of a brick wall.", "1_output": ""}
 {"tstamp": 1722576935.6023, "task_type": "sts", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "8c81665534234cbabf9084e50b3b2576", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_txt0": "Unable to attain the slightest grasp on mental life.", "0_txt1": "Somewhat understanding human thinking.", "0_txt2": "Attaining a Subtle Grasp of Mental Life.", "0_output": "", "1_conv_id": "4b6e45b866f04eeea3f8aac48a5920e4", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_txt0": "Unable to attain the slightest grasp on mental life.", "1_txt1": "Somewhat understanding human thinking.", "1_txt2": "Attaining a Subtle Grasp of Mental Life.", "1_output": ""}
+{"tstamp": 1722582591.093, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "501021605bc1439eb8896f044d59fbc4", "0_model_name": "voyage-multilingual-2", "0_txt0": "Liver cirrhosis ", "0_txt1": "Alcohol", "0_txt2": "Smoking", "0_output": "", "1_conv_id": "1c0a5addc6b34cc0b9da71a2574dc51f", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_txt0": "Liver cirrhosis ", "1_txt1": "Alcohol", "1_txt2": "Smoking", "1_output": ""}

data/sts_individual-928d45d9-19ab-4d05-9aae-4b6574886d0d.jsonl

@@ -152,3 +152,9 @@
 {"tstamp": 1722576909.7926, "task_type": "sts", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722576909.7217, "finish": 1722576909.7926, "ip": "", "conv_id": "6d8035175d054fe5b39945837a4edc5b", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "txt0": "A woman and two children walk along stones to cross a river while a dog swims.", "txt1": "A dog is sleeping.", "txt2": "People are crossing a river.", "output": ""}
 {"tstamp": 1722576914.227, "task_type": "sts", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722576914.1941, "finish": 1722576914.227, "ip": "", "conv_id": "8c81665534234cbabf9084e50b3b2576", "model_name": "intfloat/multilingual-e5-large-instruct", "txt0": "Unable to attain the slightest grasp on mental life.", "txt1": "Somewhat understanding human thinking.", "txt2": "Attaining a Subtle Grasp of Mental Life.", "output": ""}
 {"tstamp": 1722576914.227, "task_type": "sts", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722576914.1941, "finish": 1722576914.227, "ip": "", "conv_id": "4b6e45b866f04eeea3f8aac48a5920e4", "model_name": "jinaai/jina-embeddings-v2-base-en", "txt0": "Unable to attain the slightest grasp on mental life.", "txt1": "Somewhat understanding human thinking.", "txt2": "Attaining a Subtle Grasp of Mental Life.", "output": ""}
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