Scheduled Commit

data/retrieval_battle-3c73e733-b3f5-49ee-809f-5dd8857c42b8.jsonl

@@ -9,3 +9,6 @@
 {"tstamp": 1728808275.091, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c5d6c5a6d6324670a31821ccb4493b4d", "0_model_name": "voyage-multilingual-2", "0_prompt": "Rust VS C++", "0_output": [["Rust VS C++", "Title: Is Rust C++-fast? Benchmarking System Languages on Everyday Routines\n\nAbstract: Rust is a relatively new system programming language that has been experiencing a rapid adoption in the past 10 years. Rust incorporates a memory ownership model enforced at a compile time. Since this model involves zero runtime overhead, programs written in Rust are not only memory-safe but also fast, leading to performance comparable to C and C++. Multiple existing benchmarks comparing the performance of Rust with other languages focus on rarely used superficial algorithms, leading to somewhat inconclusive results. In this work, we conduct a comparative performance benchmark of Rust and C++ using commonly used algorithms and data structures rather than exotic ones. Our evaluation shows that the overall performance of Rust is similar to C++, with only minor disadvantage. We also demonstrate that in some Rust routines are slightly faster than the ones of C++."]], "0_corpus": "arxiv", "1_conv_id": "899c1ccffe384bacb9c8786cd2f28126", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Rust VS C++", "1_output": [["Rust VS C++", "Title: Is Rust C++-fast? Benchmarking System Languages on Everyday Routines\n\nAbstract: Rust is a relatively new system programming language that has been experiencing a rapid adoption in the past 10 years. Rust incorporates a memory ownership model enforced at a compile time. Since this model involves zero runtime overhead, programs written in Rust are not only memory-safe but also fast, leading to performance comparable to C and C++. Multiple existing benchmarks comparing the performance of Rust with other languages focus on rarely used superficial algorithms, leading to somewhat inconclusive results. In this work, we conduct a comparative performance benchmark of Rust and C++ using commonly used algorithms and data structures rather than exotic ones. Our evaluation shows that the overall performance of Rust is similar to C++, with only minor disadvantage. We also demonstrate that in some Rust routines are slightly faster than the ones of C++."]], "1_corpus": "arxiv"}
 {"tstamp": 1728808314.2248, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "7727016fe46a4edaa506a8d424e2c882", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Reading h264 stream in Python I am trying to read a video stream from a Parrot Bebop 2 drone. \nThe video stream is written as \"socket\" to a file as H264 stream.\n$ ffmpeg -i [STREAM]\nInput #0, h264, from 'stream_h264':\n  Duration: N/A, bitrate: N/A\n  Stream #0:0: Video: h264 (Constrained Baseline), 1 reference frame, yuv420p(progressive, left), 1280x720, 23.98 fps, 23.98 tbr, 1200k tbn, 47.95 tbc\n\nReading the video stream in MPlayer is not a problem using the parameters below. Playing it using VLC or ffmpeg should also not be too hard. For MPlayer the following works:\nmplayer -fs -demuxer h264es -benchmark stream_h264\n\nThis plays the stream in high-res. However my goal is to perform image processing on the frames using Python (mostly OpenCV). Therefore, I would like to read the frames into NumPy arrays. I have already considered using cv2.VideoCapture but this does not seem to work for my stream. Other (somewhat easy) to use options I am not aware of, therefore my question is whether someone recommend me how to read the video frames in Python? \nAll recommendations are more than welcome!\n"]], "0_corpus": "stackexchange", "1_conv_id": "571a51991f0d4bc5b8de1194ec74acd9", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_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"]], "1_corpus": "stackexchange"}
 {"tstamp": 1728808372.0311, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "e329c11d1a1d4173bbc703f36c153a20", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "python `difflib`", "0_output": [["python `difflib`", "Python\n\nPython may refer to:\nSnakes\nPythonidae, a family of nonvenomous snakes found in Africa, Asia, and Australia\nPython (genus), a genus of Pythonidae found in Africa and Asia\nPython (mythology), a mythical serpent\nComputing\nPython (programming language), a widely used high-level programming language\nPython, a native code compiler for CMU Common Lisp\nPython, the internal project name for the PERQ 3 computer workstation\nPeople\nPython of Aenus (4th-century BCE), student of Plato\nPython (painter), (ca. 360–320 BCE) vase painter in Poseidonia\nPython of Byzantium, orator, diplomat of Philip II of Macedon\nPython of Catana, poet who accompanied Alexander the Great\nPython Anghelo (1954–2014) Romanian graphic artist\nRoller coasters\nPython (Efteling), a roller coaster in the Netherlands\nPython (Busch Gardens Tampa Bay), a defunct roller coaster\nPython (Coney Island, Cincinnati, Ohio), a steel roller coaster\nVehicles\nPython (automobile maker), an Australian car company\nPython (Ford prototype), a Ford prototype sports car\nWeaponry\nPython (missile), a series of Israeli air-to-air missiles\nPython (nuclear primary), a gas-boosted fission primary used in thermonuclear weapons\nColt Python, a revolver\nOther uses\nPython (codename), a British nuclear war contingency plan\nPython (film), a 2000 horror film by Richard Clabaugh\nMonty Python or the Pythons, a British comedy group\nPython (Monty) Pictures, a company owned by the troupe's surviving members\nPython, a work written by philosopher Timon of Phlius"]], "0_corpus": "wikipedia", "1_conv_id": "5710fd6a448b4cc8baec063f28c3499d", "1_model_name": "embed-english-v3.0", "1_prompt": "python `difflib`", "1_output": [["python `difflib`", "Diff\n\nAlgorithmic derivatives Utilities that compare source files by their syntactic structure have been built mostly as research tools for some programming languages; some are available as commercial tools. In addition, free tools that perform syntax-aware diff include:\nC++: zograscope, AST-based.\nHTML: Daisydiff, html-differ.\nXML: xmldiffpatch by Microsoft and xmldiffmerge for IBM.\nJavaScript: astii (AST-based).\nMulti-language: Pretty Diff (format code and then diff)\nspiff is a variant of diff that ignores differences in floating point calculations with roundoff errors and whitespace, both of which are generally irrelevant to source code comparison. Bellcore wrote the original version. An HPUX port is the most current public release. spiff does not support binary files. spiff outputs to the standard output in standard diff format and accepts inputs in the C, Bourne shell, Fortran, Modula-2 and Lisp programming languages.\nLibXDiff is an LGPL library that provides an interface to many algorithms from 1998. An improved Myers algorithm with Rabin fingerprint was originally implemented (as of the final release of 2008), but git and libgit2's fork has since expanded the repository with many of its own. One algorithm called \"histogram\" is generally regarded as much better than the original Myers algorithm, both in speed and quality. This is the modern version of LibXDiff used by Vim."]], "1_corpus": "wikipedia"}
+{"tstamp": 1728844571.8699, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "937eb886a854493db82fe81098edaee6", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "0_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: Gauge fixing, BRS invariance and Ward identities for randomly stirred flows\n\nAbstract: The Galilean invariance of the Navier-Stokes equation is shown to be akin to a global gauge symmetry familiar from quantum field theory. This symmetry leads to a multiple counting of infinitely many inertial reference frames in the path integral approach to randomly stirred fluids. This problem is solved by fixing the gauge, i.e., singling out one reference frame. The gauge fixed theory has an underlying Becchi-Rouet-Stora (BRS) symmetry which leads to the Ward identity relating the exact inverse response and vertex functions. This identification of Galilean invariance as a gauge symmetry is explored in detail, for different gauge choices and by performing a rigorous examination of a discretized version of the theory. The Navier-Stokes equation is also invariant under arbitrary rectilinear frame accelerations, known as extended Galilean invariance (EGI). We gauge fix this extended symmetry and derive the generalized Ward identity that follows from the BRS invariance of the gauge-fixed theory. This new Ward identity reduces to the standard one in the limit of zero acceleration. This gauge-fixing approach unambiguously shows that Galilean invariance and EGI constrain only the zero mode of the vertex but none of the higher wavenumber modes."]], "0_corpus": "arxiv", "1_conv_id": "a1a0869b95ea496599857615f3c60278", "1_model_name": "BM25", "1_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "1_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: Text and Code Embeddings by Contrastive Pre-Training\n\nAbstract: Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code. The same unsupervised text embeddings that achieve new state-of-the-art results in linear-probe classification also display impressive semantic search capabilities and sometimes even perform competitively with fine-tuned models. On linear-probe classification accuracy averaging over 7 tasks, our best unsupervised model achieves a relative improvement of 4% and 1.8% over previous best unsupervised and supervised text embedding models respectively. The same text embeddings when evaluated on large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and 10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.8% relative improvement over prior best work on code search."]], "1_corpus": "arxiv"}
+{"tstamp": 1728844658.1904, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "fa877bb4df294938a14edfa07cd7a9e2", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_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"]], "0_corpus": "stackexchange", "1_conv_id": "4ef9e9b1bc71482eb11dc10533d2d421", "1_model_name": "BM25", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What is the purpose of __name__? What does __name__ do? I have only seen it paired with __main__ and nothing else.\nI know that the classic if __name__ == __main__: defines the behavior when acting as a package vs running as stand-alone.\nHowever what other usages are there for __name__?\n\nA: __name__ is \"__main__\" if you're executing the script directly. If you're importing a module, __name__ is the name of the module.\nfoo.py:\nprint(__name__)\n\nbar.py \nimport foo\n\nRun the scripts:\n$ python foo.py\n__main__\n$ python bar.py \nfoo\n\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1728844695.1577, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "6503d527b2674859b39a0e67bfe4c5cd", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "91c93825dd9d447cb85315ce721872c5", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. "]], "1_corpus": "wikipedia"}

data/retrieval_individual-3c73e733-b3f5-49ee-809f-5dd8857c42b8.jsonl

@@ -47,3 +47,9 @@
 {"tstamp": 1728815344.4438, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1728815343.1313, "finish": 1728815344.4438, "ip": "", "conv_id": "90844bace3fc4550b7f5030877b3ac7a", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "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: How much is enough?: Data requirements for statistical NLP\n\nAbstract: In this paper I explore a number of issues in the analysis of data requirements for statistical NLP systems. A preliminary framework for viewing such systems is proposed and a sample of existing works are compared within this framework. The first steps toward a theory of data requirements are made by establishing some results relevant to bounding the expected error rate of a class of simplified statistical language learners as a function of the volume of training data."]], "corpus": "arxiv"}
 {"tstamp": 1728815409.6903, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1728815409.3181, "finish": 1728815409.6903, "ip": "", "conv_id": "059aa820e0ce477bba63f95209e0c180", "model_name": "embed-english-v3.0", "prompt": "I am looking for papers on how to best extract legal requirements from legal text documents", "output": [["I am looking for papers on how to best extract legal requirements from legal text documents", "Title: An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts\n\nAbstract: Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97.2% and 82.4% and recall scores of 94.9% and 92.4%."]], "corpus": "arxiv"}
 {"tstamp": 1728815409.6903, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1728815409.3181, "finish": 1728815409.6903, "ip": "", "conv_id": "b6e1d8bac23f4f87b2d07dc49f74b401", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "I am looking for papers on how to best extract legal requirements from legal text documents", "output": [["I am looking for papers on how to best extract legal requirements from legal text documents", "Title: An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts\n\nAbstract: Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97.2% and 82.4% and recall scores of 94.9% and 92.4%."]], "corpus": "arxiv"}
+{"tstamp": 1728844548.6807, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1728844545.5524, "finish": 1728844548.6807, "ip": "", "conv_id": "937eb886a854493db82fe81098edaee6", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "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: Gauge fixing, BRS invariance and Ward identities for randomly stirred flows\n\nAbstract: The Galilean invariance of the Navier-Stokes equation is shown to be akin to a global gauge symmetry familiar from quantum field theory. This symmetry leads to a multiple counting of infinitely many inertial reference frames in the path integral approach to randomly stirred fluids. This problem is solved by fixing the gauge, i.e., singling out one reference frame. The gauge fixed theory has an underlying Becchi-Rouet-Stora (BRS) symmetry which leads to the Ward identity relating the exact inverse response and vertex functions. This identification of Galilean invariance as a gauge symmetry is explored in detail, for different gauge choices and by performing a rigorous examination of a discretized version of the theory. The Navier-Stokes equation is also invariant under arbitrary rectilinear frame accelerations, known as extended Galilean invariance (EGI). We gauge fix this extended symmetry and derive the generalized Ward identity that follows from the BRS invariance of the gauge-fixed theory. This new Ward identity reduces to the standard one in the limit of zero acceleration. This gauge-fixing approach unambiguously shows that Galilean invariance and EGI constrain only the zero mode of the vertex but none of the higher wavenumber modes."]], "corpus": "arxiv"}
+{"tstamp": 1728844548.6807, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1728844545.5524, "finish": 1728844548.6807, "ip": "", "conv_id": "a1a0869b95ea496599857615f3c60278", "model_name": "BM25", "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: Text and Code Embeddings by Contrastive Pre-Training\n\nAbstract: Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code. The same unsupervised text embeddings that achieve new state-of-the-art results in linear-probe classification also display impressive semantic search capabilities and sometimes even perform competitively with fine-tuned models. On linear-probe classification accuracy averaging over 7 tasks, our best unsupervised model achieves a relative improvement of 4% and 1.8% over previous best unsupervised and supervised text embedding models respectively. The same text embeddings when evaluated on large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and 10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.8% relative improvement over prior best work on code search."]], "corpus": "arxiv"}
+{"tstamp": 1728844606.2685, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1728844604.4191, "finish": 1728844606.2685, "ip": "", "conv_id": "fa877bb4df294938a14edfa07cd7a9e2", "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": 1728844606.2685, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1728844604.4191, "finish": 1728844606.2685, "ip": "", "conv_id": "4ef9e9b1bc71482eb11dc10533d2d421", "model_name": "BM25", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What is the purpose of __name__? What does __name__ do? I have only seen it paired with __main__ and nothing else.\nI know that the classic if __name__ == __main__: defines the behavior when acting as a package vs running as stand-alone.\nHowever what other usages are there for __name__?\n\nA: __name__ is \"__main__\" if you're executing the script directly. If you're importing a module, __name__ is the name of the module.\nfoo.py:\nprint(__name__)\n\nbar.py \nimport foo\n\nRun the scripts:\n$ python foo.py\n__main__\n$ python bar.py \nfoo\n\n"]], "corpus": "stackexchange"}
+{"tstamp": 1728844686.4475, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1728844684.2875, "finish": 1728844686.4475, "ip": "", "conv_id": "6503d527b2674859b39a0e67bfe4c5cd", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
+{"tstamp": 1728844686.4475, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1728844684.2875, "finish": 1728844686.4475, "ip": "", "conv_id": "91c93825dd9d447cb85315ce721872c5", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. "]], "corpus": "wikipedia"}