Scheduled Commit

data/retrieval_battle-c4bdd700-2c9c-4d9b-92d7-63fecd0c1575.jsonl

@@ -22,3 +22,4 @@
 {"tstamp": 1722439356.8617, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "5296aeb678084e99a677d71b97c85d42", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "what is the difference between chkdsk /f and chkdsk /r?", "0_output": [["what is the difference between chkdsk /f and chkdsk /r?", "Superuser Stackexchange\n\nQ: What is the difference between chkdsk /f and chkdsk /r? \n/F              Fixes errors on the disk.\n/R              Locates bad sectors and recovers readable information (implies /F).\n\n(from dir /?)\nIt just sounds like the same thing to me.  I never know which one to use - is there even a difference?  Or is it maybe for backwards compatibility with previous versions of Windows where there was a difference?\n\nA: chkdsk /r does the same thing as chkdsk /f only it also checks for bad sectors on the disk and recovers any readable information. Running chkdsk /r implies that also chkdsk /f is run.\nchkdsk /f only checks for disk errors, not bad sectors.\nMicrosoft has a detailed page for chkdsk. The following is a snippet explaining /f and /r parameters.\n\nFixing disk errors:\nChkdsk corrects disk errors only if you specify the /f command-line\n  option. Chkdsk must be able to lock the drive to correct errors.\n  Because repairs usually change a disk's file allocation table and\n  sometimes cause a loss of data, chkdsk sends a confirmation message.\nFinding physical disk errors:\nUse the /r command-line option to find physical disk errors in the\n  file system.\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "26243994a6974ab099c38f50a8302a08", "1_model_name": "BM25", "1_prompt": "what is the difference between chkdsk /f and chkdsk /r?", "1_output": [["what is the difference between chkdsk /f and chkdsk /r?", "Superuser Stackexchange\n\nQ: What is the difference between chkdsk /f and chkdsk /r? \n/F              Fixes errors on the disk.\n/R              Locates bad sectors and recovers readable information (implies /F).\n\n(from dir /?)\nIt just sounds like the same thing to me.  I never know which one to use - is there even a difference?  Or is it maybe for backwards compatibility with previous versions of Windows where there was a difference?\n\nA: chkdsk /r does the same thing as chkdsk /f only it also checks for bad sectors on the disk and recovers any readable information. Running chkdsk /r implies that also chkdsk /f is run.\nchkdsk /f only checks for disk errors, not bad sectors.\nMicrosoft has a detailed page for chkdsk. The following is a snippet explaining /f and /r parameters.\n\nFixing disk errors:\nChkdsk corrects disk errors only if you specify the /f command-line\n  option. Chkdsk must be able to lock the drive to correct errors.\n  Because repairs usually change a disk's file allocation table and\n  sometimes cause a loss of data, chkdsk sends a confirmation message.\nFinding physical disk errors:\nUse the /r command-line option to find physical disk errors in the\n  file system.\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722439388.9707, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "10ed8e581f62414c872938cd8160de29", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "who does peggy carter marry?", "0_output": [["who does peggy carter marry?", "Scifi Stackexchange\n\nQ: What is the name of Peggy's husband in Captain America: The Winter Soldier? I'm having a hard time hearing the name of Peggy's husband she names. I'm not the greatest at spelling but it sounds like his name is something like: Emanhu Winter?\nHere's the scene if anyone can help, he's named at 1:40:\n\n\n\n\nA: She doesn't give a specific name. The phrase she utters at 1:40 is:\n\nHe saved over 1000 men, including the man who, would ahh, who would become my husband, as it turned out\n\nwhich I guess you hear as Emanhu Winter, because of the slight hesitation and repetition there in the line delivery.\n"]], "0_corpus": "stackexchange", "1_conv_id": "512b56cafcf548dc8e1ba766787e9dee", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "who does peggy carter marry?", "1_output": [["who does peggy carter marry?", "Scifi Stackexchange\n\nQ: When did Captain America and Peggy Carter dance in the comics? Sometime this year, I read a comic book or graphic novel in which Steve Rogers (Captain America) returned from the ice, and gets to dance at last with an old Peggy Carter.\nDoes anyone know which comic that was? I can't find it anymore.\nThanks for your help!\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722439938.8404, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "d9c71637975b4303bfbcbad02f1bd062", "0_model_name": "voyage-multilingual-2", "0_prompt": "A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "0_output": [["A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "Title: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context\n\nAbstract: Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."]], "0_corpus": "arxiv", "1_conv_id": "1802d7d52d704f829a4f4992ff41fc69", "1_model_name": "text-embedding-3-large", "1_prompt": "A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "1_output": [["A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "Title: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context\n\nAbstract: Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."]], "1_corpus": "arxiv"}
+{"tstamp": 1722440194.3406, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "cef97f776ff242d6ad09eee2574b7864", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "what does it mean if a car is a fleet vehicle", "0_output": [["what does it mean if a car is a fleet vehicle", "Vehicle registration plates of Indonesia\n\nBlack on yellow: Commercial vehicle or public transportation such as buses, taxis, angkot, auto rickshaws and commercial trucks.\nWhite on red: Used by fire departments, government ambulances, government officials and other governmental vehicles administered under their respective local governments.\nBlack on Red: Vehicles belonging to foreign countries. Commonly used by foreign embassies or vehicles belonging to International organizations.\nBlack on White with Black Trim: Vehicles belonging to diplomatic corps of foreign countries. Commonly used by foreign embassies or vehicles belonging to international organizations.\nBlack on green: Free Zone vehicles i.e. Batam (see Indonesia–Malaysia–Singapore Growth Triangle).\nBlue on white: Vehicles belonging to foreign countries, mainly used before the vehicle has been registered.\nElectric vehicles\nThe Indonesian National Police has set a special license plate for electric vehicles with additional blue trim at the expiry date row in accordance with the regulations in the Decree of the Head of the Traffic Corps of the Indonesian National Police in 2020.\nHowever, when personal fossil-fueled vehicle plates have changed the color scheme, personal EV plates are still using the old one (white on black) with blue trim, added with the implementation of FE-Schrift since November 2022.\nWhite on Black with Blue Trim: for privately owned electric vehicles and rental electric vehicles"]], "0_corpus": "wikipedia", "1_conv_id": "8fb7fcd108be4239a41e863c284cde1a", "1_model_name": "text-embedding-004", "1_prompt": "what does it mean if a car is a fleet vehicle", "1_output": [["what does it mean if a car is a fleet vehicle", "Fleet vehicle\n\nA fleet vehicle is a vehicle owned or leased by a business, government agency, or other organization rather than by an individual or family. Typical examples include vehicles operated by car rental companies, taxicab companies, public utilities, public transport, and emergency services.\nMany businesses purchase or lease fleet vehicles to deliver goods to customers, as well as providing vehicles for sales representatives to travel to clients. In some jurisdictions and countries, fleet vehicles can also be privately owned by employees. These vehicles are often called the \"grey fleet\" and are used for work purposes. Fleet vehicles can be managed by a fleet manager or transport manager using fleet management software. Vehicles may be connected to a fleet telematics system by way of a Fleet Management System, also known as an FMS.\nFederal Vehicle Fleet\nIn the United States, \"Federal Vehicle Fleet\" refers to the federal government's vehicles."]], "1_corpus": "wikipedia"}

data/retrieval_individual-c4bdd700-2c9c-4d9b-92d7-63fecd0c1575.jsonl

@@ -64,3 +64,5 @@
 {"tstamp": 1722439917.9043, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722439917.0566, "finish": 1722439917.9043, "ip": "", "conv_id": "1802d7d52d704f829a4f4992ff41fc69", "model_name": "text-embedding-3-large", "prompt": "A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "output": [["A paper that proposes an improved Transformer model capable of learning long-term dependencies beyond fixed-length context in language modeling.", "Title: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context\n\nAbstract: Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."]], "corpus": "arxiv"}
 {"tstamp": 1722439966.3638, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722439964.2272, "finish": 1722439966.3638, "ip": "", "conv_id": "6cc7725c1ba4440f86ef1a316b7d4028", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "how are huey dewey and louie related to scrooge?", "output": [["how are huey dewey and louie related to scrooge?", "Stackoverflow Stackexchange\n\nQ: Self-Contained Application Packaging for java swing maven project in eclipse I want to make a self-contained package (including jre 8).\nI have a Maven project developed in Eclipse. \nI have read that JavaFX has support for building towards different platforms (Windows, Mac, Linux).\nWhat is the best way forward? I know there is an eclipseFx plugin and there is a maven fx plugin as well. This confuses me since JavaFx uses ant script for building.\nMaybe I should use some other tool, like launch4j?\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722439966.3638, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722439964.2272, "finish": 1722439966.3638, "ip": "", "conv_id": "1014e7df503c4ab6bebe1644fa4de01c", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "how are huey dewey and louie related to scrooge?", "output": [["how are huey dewey and louie related to scrooge?", "Stackoverflow Stackexchange\n\nQ: What extensions are there for generated C# files? I'm writing a filter for the C# files that are generated, and want to get rid of the obvious generated files such as .Designer.cs or .g.cs. What other generated extensions are there? Thanks.\nClarification: I'm only looking files that have a .cs extension, but something comes before the .cs. Meaning that C# files that do not end in .cs do not interest me.\n\nA: I might be forgetting many of them, but still:\n\n\n*\n\n**.baml\n\n**.g.cs\n\n**.g.i.cs\n\n**.designer.cs\n\n**.cache\n\n**.tlog\n\n**.g.resources\n\n**.cache\n\n**.lref\n\n**.pdb\n\n**.exe\n\n**.dll (Might well be some outside dll instead of being a generated one!)\n\n**.xml\n\n\nI have only listed solution related extensions and not source control related extensions.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722440165.7223, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722440163.3829, "finish": 1722440165.7223, "ip": "", "conv_id": "cef97f776ff242d6ad09eee2574b7864", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "what does it mean if a car is a fleet vehicle", "output": [["what does it mean if a car is a fleet vehicle", "Vehicle registration plates of Indonesia\n\nBlack on yellow: Commercial vehicle or public transportation such as buses, taxis, angkot, auto rickshaws and commercial trucks.\nWhite on red: Used by fire departments, government ambulances, government officials and other governmental vehicles administered under their respective local governments.\nBlack on Red: Vehicles belonging to foreign countries. Commonly used by foreign embassies or vehicles belonging to International organizations.\nBlack on White with Black Trim: Vehicles belonging to diplomatic corps of foreign countries. Commonly used by foreign embassies or vehicles belonging to international organizations.\nBlack on green: Free Zone vehicles i.e. Batam (see Indonesia–Malaysia–Singapore Growth Triangle).\nBlue on white: Vehicles belonging to foreign countries, mainly used before the vehicle has been registered.\nElectric vehicles\nThe Indonesian National Police has set a special license plate for electric vehicles with additional blue trim at the expiry date row in accordance with the regulations in the Decree of the Head of the Traffic Corps of the Indonesian National Police in 2020.\nHowever, when personal fossil-fueled vehicle plates have changed the color scheme, personal EV plates are still using the old one (white on black) with blue trim, added with the implementation of FE-Schrift since November 2022.\nWhite on Black with Blue Trim: for privately owned electric vehicles and rental electric vehicles"]], "corpus": "wikipedia"}
+{"tstamp": 1722440165.7223, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722440163.3829, "finish": 1722440165.7223, "ip": "", "conv_id": "8fb7fcd108be4239a41e863c284cde1a", "model_name": "text-embedding-004", "prompt": "what does it mean if a car is a fleet vehicle", "output": [["what does it mean if a car is a fleet vehicle", "Fleet vehicle\n\nA fleet vehicle is a vehicle owned or leased by a business, government agency, or other organization rather than by an individual or family. Typical examples include vehicles operated by car rental companies, taxicab companies, public utilities, public transport, and emergency services.\nMany businesses purchase or lease fleet vehicles to deliver goods to customers, as well as providing vehicles for sales representatives to travel to clients. In some jurisdictions and countries, fleet vehicles can also be privately owned by employees. These vehicles are often called the \"grey fleet\" and are used for work purposes. Fleet vehicles can be managed by a fleet manager or transport manager using fleet management software. Vehicles may be connected to a fleet telematics system by way of a Fleet Management System, also known as an FMS.\nFederal Vehicle Fleet\nIn the United States, \"Federal Vehicle Fleet\" refers to the federal government's vehicles."]], "corpus": "wikipedia"}