Scheduled Commit

data/clustering_battle-b05ca3f8-c521-4bfc-a840-ff14f8eda5db.jsonl

@@ -31,3 +31,5 @@
 {"tstamp": 1734526108.1241, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "11ea1d1441bb402cb08cd06c17243b2f", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "1d82f4952d10444cbc8d7da5e3bb74ea", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1734526143.9635, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "880b498635c541799e6a4cf77268bf34", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "c3fb9ff1b61740f48efab6055f723060", "1_model_name": "embed-english-v3.0", "1_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1734526173.1823, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "912460edf89349d9890ef3975036bd9d", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "06631b9a288b4693b74417409f0dc817", "1_model_name": "embed-english-v3.0", "1_prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1734813875.6681, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "32978ba4b5bd4a089ab8fc8ad36b2c29", "0_model_name": "voyage-multilingual-2", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "a131702c38534783a1110baca574e9f6", "1_model_name": "embed-english-v3.0", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1734813927.5487, "task_type": "clustering", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "dd1330d66b4e43738e3bacc061246cf8", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": ["Roman", "Greek", "Egyptian", "white", "chamomile", "wisdom tooth", "incisor", "canine", "Clue", "chess"], "0_ncluster": 4, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "c1924cad33584a5d82c26907cad5a273", "1_model_name": "embed-english-v3.0", "1_prompt": ["Roman", "Greek", "Egyptian", "white", "chamomile", "wisdom tooth", "incisor", "canine", "Clue", "chess"], "1_ncluster": 4, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-b05ca3f8-c521-4bfc-a840-ff14f8eda5db.jsonl

@@ -170,3 +170,9 @@
 {"tstamp": 1734526187.1309, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734526186.8202, "finish": 1734526187.1309, "ip": "", "conv_id": "c9fd7f92671c4a91b49e4f43f54f61a9", "model_name": "GritLM/GritLM-7B", "prompt": ["green", "light green", "red", "blue", "pink", "dark green", "dark blue", "light blue"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1734632196.3855, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1734632196.3341, "finish": 1734632196.3855, "ip": "", "conv_id": "35817e30c06d4e23b3543e5955840a31", "model_name": "embed-english-v3.0", "prompt": ["Adversaries in possession of credentials to Valid Accounts may be unable to complete the login process if they lack access to the 2FA or MFA mechanisms required as an additional credential and security control."], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1734632196.3855, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1734632196.3341, "finish": 1734632196.3855, "ip": "", "conv_id": "e0634f2b761c4366a5bc8a8cc69cb82b", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["Adversaries in possession of credentials to Valid Accounts may be unable to complete the login process if they lack access to the 2FA or MFA mechanisms required as an additional credential and security control."], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734813853.0192, "task_type": "clustering", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1734813852.6655, "finish": 1734813853.0192, "ip": "", "conv_id": "32978ba4b5bd4a089ab8fc8ad36b2c29", "model_name": "voyage-multilingual-2", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734813853.0192, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1734813852.6655, "finish": 1734813853.0192, "ip": "", "conv_id": "a131702c38534783a1110baca574e9f6", "model_name": "embed-english-v3.0", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734813865.1981, "task_type": "clustering", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1734813864.989, "finish": 1734813865.1981, "ip": "", "conv_id": "32978ba4b5bd4a089ab8fc8ad36b2c29", "model_name": "voyage-multilingual-2", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734813865.1981, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1734813864.989, "finish": 1734813865.1981, "ip": "", "conv_id": "a131702c38534783a1110baca574e9f6", "model_name": "embed-english-v3.0", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734813901.3244, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1734813901.1641, "finish": 1734813901.3244, "ip": "", "conv_id": "dd1330d66b4e43738e3bacc061246cf8", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Roman", "Greek", "Egyptian", "white", "chamomile", "wisdom tooth", "incisor", "canine", "Clue", "chess"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734813901.3244, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1734813901.1641, "finish": 1734813901.3244, "ip": "", "conv_id": "c1924cad33584a5d82c26907cad5a273", "model_name": "embed-english-v3.0", "prompt": ["Roman", "Greek", "Egyptian", "white", "chamomile", "wisdom tooth", "incisor", "canine", "Clue", "chess"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-b05ca3f8-c521-4bfc-a840-ff14f8eda5db.jsonl

@@ -560,3 +560,5 @@
 {"tstamp": 1734802440.0647, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1734802439.7848, "finish": 1734802440.0647, "ip": "", "conv_id": "f537e0cbbb6746d89103c69105809dc5", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "corpus": "stackexchange"}
 {"tstamp": 1734802544.7676, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734802544.564, "finish": 1734802544.7676, "ip": "", "conv_id": "ae5e1131226a40e4be35205d32f4a62e", "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": 1734802544.7676, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1734802544.564, "finish": 1734802544.7676, "ip": "", "conv_id": "e414f12638414790a4165dfad18b0b47", "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": 1734813954.2386, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734813952.0268, "finish": 1734813954.2386, "ip": "", "conv_id": "a79c20b17e384f05a591d6cf6c2a3d00", "model_name": "GritLM/GritLM-7B", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "corpus": "stackexchange"}
+{"tstamp": 1734813954.2386, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1734813952.0268, "finish": 1734813954.2386, "ip": "", "conv_id": "811a91f2189c46ca99e41184b63ec051", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Why does it do this ? if - __name__ == '__main__' \nDuplicate of:\nWhat does if __name__== \"__main__\" do? \n\nConsider this code:\nif __name__ == '__main__':\n    import pdb\n    pdb.run(\"interact()\\n\")\n\nWhat does the following line mean?\nif(__name__=='__main__')\n\nI fainted.\n\nA: That is a check to see if you are directly running the script or if it is included in a library.\nWhen you run a python script like this:\npython myScript.py\n\nIt sends a parameter, telling you to run the programs first method, which is widely called \"main\", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.\n"]], "corpus": "stackexchange"}