Scheduled Commit

data/retrieval_battle-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -10,3 +10,5 @@
 {"tstamp": 1722364308.3175, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "863d8484113b41f3ad486bce94296929", "0_model_name": "text-embedding-004", "0_prompt": "do you have to have a gun permit to shoot at a range", "0_output": [["do you have to have a gun permit to shoot at a range", "Shooting range\n\nA shooting range, firing range, gun range or shooting ground is a specialized facility, venue, or field designed specifically for firearm usage qualifications, training, practice, or competitions. Some shooting ranges are operated by military or law enforcement agencies, though the majority of ranges are privately owned by civilians and sporting clubs and cater mostly to recreational shooters. Each facility is typically overseen by one or more supervisory personnel, known as a Range Officer (RO), or sometimes a range master in the United States. Supervisory personnel are responsible for ensuring that all safety rules and relevant laws are followed at all times."]], "0_corpus": "wikipedia", "1_conv_id": "2cadf86189454fcc929249ad0f97a89a", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "do you have to have a gun permit to shoot at a range", "1_output": [["do you have to have a gun permit to shoot at a range", "Overview of gun laws by nation\n\nPoland Gun ownership in Poland is regulated by the Weapons and Munitions Act of 1999 as further amended. A permit is required to own or possess firearms. Permits are granted for specific purposes, such as self-defense, sport, hunting, collecting etc. and are  except the ones for self-defense, which are . Institutional permits allow for firearm ownership by private security companies, shooting ranges, sport clubs etc.\nPermits are issued to permanent residents over 21, not addicted to alcohol or other substances, having no criminal record, being in good physical and mental health, who passed an exam before sporting association, hunting association or the Police. Permits specify the purposes, categories and quantities of allowed guns; categories depend on permit purpose while quantities are assigned at the discretion of the Police, based on documentation provided. Quantities can be increased when one uses up all available slots.\nCarrying loaded guns is allowed on all types of permits except permits for collecting and memorial purposes. Hunters can carry loaded only on hunting grounds. Guns shall be carried concealed in a holster close to the body. Carrying in public transport is limited only to self-defense permit holders. It is prohibited to carry while intoxicated, and when attending public gatherings or mass events.\nEveryone, including foreigners, is allowed to use legal guns at registered shooting ranges, regardless of actual gun's ownership, e.g. rented from the range or shared by a legal owner present. Discharging a firearm for training or leisure out of a registered shooting range (even on a large private property) is prohibited."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722364851.6531, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8c8e65f41c2245b29e485dbd910fd7b6", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Where is university of waterloo", "0_output": [["Where is university of waterloo", "University of Waterloo\n\nCoat of arms The university's coat of arms was first used by the university in October 1961, but was only officially granted by the Lord Lyon King of Arms in August 1987. It was not registered with the Canadian Heraldic Authority until 15 February 2001. Four variations of the coat of arms existed. The first was used from 1961 to 1996, when the second bright-yellow shield using slightly different shaped lions was introduced. The yellow background was dulled in 2000, and finally, the original lions were reintroduced in 2010 in conjunction with the attempt to replace the use of the coat of arms with a futuristic W logo. The new logo was eventually rejected after student opposition.\nThe red-on-gold lions on the university's arms were adopted from those of Arthur Wellesley, 1st Duke of Wellington, who defeated Napoleon at the Battle of Waterloo. The City of Waterloo was named after an area just south of Brussels, Belgium, where the battle occurred. The chevron on the arms was taken from the arms of Herbert Kitchener, 1st Earl Kitchener, Field Marshal with the British Army during World War I. The black and white pattern used on the chevron was based on the colours of Prussia, as homage to the German heritage of the area. The City of Kitchener was originally known as Berlin, but was renamed after Earl Kitchener in 1916 during World War I."]], "0_corpus": "wikipedia", "1_conv_id": "1947b916cfc64cc2a34fa5c4baf1cb63", "1_model_name": "text-embedding-004", "1_prompt": "Where is university of waterloo", "1_output": [["Where is university of waterloo", "University of Waterloo\n\nThe University of Waterloo (UWaterloo, UW, or Waterloo) is a public research university with a main campus in Waterloo, Ontario, Canada. The main campus is on  of land adjacent to uptown Waterloo and Waterloo Park. The university also operates three satellite campuses and four affiliated university colleges. The university offers academic programs administered by six faculties and thirteen faculty-based schools. Waterloo operates the largest post-secondary co-operative education program in the world, with over 20,000 undergraduate students enrolled in the university's co-op program. Waterloo is a member of the U15, a group of research-intensive universities in Canada."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722365106.2273, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d6d55602c73c4ae5bdcca137fa7bdfba", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "what is the difference between service and services?", "0_output": [["what is the difference between service and services?", "Stackoverflow Stackexchange\n\nQ: Spring RestTemplate POST Request with URL encoded data I'm new to Spring and trying to do a rest request with RestTemplate. The Java code should do the same as below curl command:\ncurl --data \"name=feature&color=#5843AD\" --header \"PRIVATE-TOKEN: xyz\" \"https://someserver.com/api/v3/projects/1/labels\"\n\nBut the server rejects the RestTemplate with a 400 Bad Request\nRestTemplate restTemplate = new RestTemplate();\nHttpHeaders headers = new HttpHeaders();\nheaders.add(\"PRIVATE-TOKEN\", \"xyz\");\nHttpEntity<String> entity = new HttpEntity<String>(\"name=feature&color=#5843AD\", headers);\nResponseEntity<LabelCreationResponse> response = restTemplate.exchange(\"https://someserver.com/api/v3/projects/1/labels\", HttpMethod.POST, entity, LabelCreationResponse.class);\n\nCan somebody tell me what I'm doing wrong?\n\nA: I think the problem is that when you try to send data to server didn't set the content type header which should be one of the two: \"application/json\" or \"application/x-www-form-urlencoded\" . In your case is: \"application/x-www-form-urlencoded\" based on your sample params (name and color). This header means \"what type of data my client sends to server\".\nRestTemplate restTemplate = new RestTemplate();\n\nHttpHeaders headers = new HttpHeaders();\nheaders.setContentType(MediaType.APPLICATION_FORM_URLENCODED);\nheaders.add(\"PRIVATE-TOKEN\", \"xyz\");\n\nMultiValueMap<String, String> map = new LinkedMultiValueMap<>();\nmap.add(\"name\",\"feature\");\nmap.add(\"color\",\"#5843AD\");\n\nHttpEntity<MultiValueMap<String, String>> entity = new HttpEntity<>(map, headers);\n\nResponseEntity<LabelCreationResponse> response =\n    restTemplate.exchange(\"https://foo/api/v3/projects/1/labels\",\n                          HttpMethod.POST,\n                          entity,\n                          LabelCreationResponse.class);\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "139ac954b90f43fa8538ac61d8e18970", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "what is the difference between service and services?", "1_output": [["what is the difference between service and services?", "Stackoverflow Stackexchange\n\nQ: What is a service? I keep hearing the term \"service\" a lot, but I have a vague understanding of it. Could someone explain what a service is citing some practical examples? \n\nA: It depends on the context. Very abstractly speaking, a service is some sort of program that offers some function, but the internals of which are completely opaque. I.e., a web service is something running somewhere on the web, having some sort of API which you can contact to get or submit some data. What exactly it does is none of your business, it just offers its service to you. On a system, for example a Windows Service may be a program running persistently in the background, doing something. Inside an application, you may have a service layer, which offers some functionality which you can use in the rest of the application through an API, but how exactly it works is irrelevant.\nThat's in contrast to, e.g., a function or library or class, which you usually import, manipulate, use more directly. A service is more self-contained, offering only its functionality with nothing much in the way of introspecting it.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722365346.4281, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "c225cc29368447dca969428826fd3660", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?\n\nA: Avoid tags that will inject content into the page:\n\n\n*\n\n*applet\n\n*script\n\n*style\n\n*link\n\n*iframe\n\n\nOther tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.\n"]], "0_corpus": "stackexchange", "1_conv_id": "95f3ec488c314e2b81635422386eedb6", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_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"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722365495.8143, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "a4c7486a646e42299791238df7dd3ffd", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "what is dense retrieval", "0_output": [["what is dense retrieval", "Latent semantic analysis\n\nSemantic hashing In semantic hashing  documents are mapped to memory addresses by means of a neural network in such a way that semantically similar documents are located at nearby addresses. Deep neural network essentially builds a graphical model of the word-count vectors obtained from a large set of documents. Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method.\nLatent semantic indexing\nLatent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text.  LSI is based on the principle that words that are used in the same contexts tend to have similar meanings.  A key feature of LSI is its ability to extract the conceptual content of a body of text by establishing associations between those terms that occur in similar contexts.\nLSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s, to a contingency table built from word counts in documents."]], "0_corpus": "wikipedia", "1_conv_id": "960bea43fd02452d9429b486a6719b0e", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "what is dense retrieval", "1_output": [["what is dense retrieval", "Information retrieval\n\nInformation retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need.  The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.\nAutomated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications."]], "1_corpus": "wikipedia"}

data/retrieval_individual-8d38bb89-1e1a-471a-8b9e-35c1f784690e.jsonl

@@ -64,3 +64,5 @@
 {"tstamp": 1722365245.4663, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722365243.2369, "finish": 1722365245.4663, "ip": "", "conv_id": "87e5c862e1cb4f4c9a47d5a1a0a6ec9d", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model Pretraining\n\nAbstract: Large language models exhibit exceptional generalization capabilities, primarily attributed to the utilization of diversely sourced data. However, conventional practices in integrating this diverse data heavily rely on heuristic schemes, lacking theoretical guidance. This research tackles these limitations by investigating strategies based on low-cost proxies for data mixtures, with the aim of streamlining data curation to enhance training efficiency. Specifically, we propose a unified scaling law, termed BiMix, which accurately models the bivariate scaling behaviors of both data quantity and mixing proportions. We conduct systematic experiments and provide empirical evidence for the predictive power and fundamental principles of BiMix. Notably, our findings reveal that entropy-driven training-free data mixtures can achieve comparable or even better performance than more resource-intensive methods. We hope that our quantitative insights can shed light on further judicious research and development in cost-effective language modeling."]], "corpus": "arxiv"}
 {"tstamp": 1722365301.1655, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722365300.9919, "finish": 1722365301.1655, "ip": "", "conv_id": "c225cc29368447dca969428826fd3660", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?\n\nA: Avoid tags that will inject content into the page:\n\n\n*\n\n*applet\n\n*script\n\n*style\n\n*link\n\n*iframe\n\n\nOther tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722365301.1655, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722365300.9919, "finish": 1722365301.1655, "ip": "", "conv_id": "95f3ec488c314e2b81635422386eedb6", "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"}
+{"tstamp": 1722365480.6099, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722365480.2948, "finish": 1722365480.6099, "ip": "", "conv_id": "a4c7486a646e42299791238df7dd3ffd", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "what is dense retrieval", "output": [["what is dense retrieval", "Latent semantic analysis\n\nSemantic hashing In semantic hashing  documents are mapped to memory addresses by means of a neural network in such a way that semantically similar documents are located at nearby addresses. Deep neural network essentially builds a graphical model of the word-count vectors obtained from a large set of documents. Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method.\nLatent semantic indexing\nLatent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text.  LSI is based on the principle that words that are used in the same contexts tend to have similar meanings.  A key feature of LSI is its ability to extract the conceptual content of a body of text by establishing associations between those terms that occur in similar contexts.\nLSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s, to a contingency table built from word counts in documents."]], "corpus": "wikipedia"}
+{"tstamp": 1722365480.6099, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722365480.2948, "finish": 1722365480.6099, "ip": "", "conv_id": "960bea43fd02452d9429b486a6719b0e", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "what is dense retrieval", "output": [["what is dense retrieval", "Information retrieval\n\nInformation retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need.  The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.\nAutomated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications."]], "corpus": "wikipedia"}