Scheduled Commit

data/clustering_battle-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -20,3 +20,4 @@
 {"tstamp": 1727685689.8655, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "9b2726b733b64fde92760b2c7a470a9f", "0_model_name": "text-embedding-3-large", "0_prompt": ["Pack C 920 (Taxi 920, Stripe Simple) declining all credit card transactions with invalid and purchase sequence errors", "Issue: Credit card transaction declined after power loss", "Customer wants a summary of last year's statements with fees removed for the business, Cloudy Transportation Group Inc.", "calls technical support as he is not receiving his credit card money due to a payment not going through", " Customer contacts Sierra, seeking information about a check that has been pending for 7 months.", "reported that their payments were not being processed and deposited into their bank account. The last successful deposit was on June 17th.", "Agent confirms that the account was frozen, but a maintenance change was made to update the bank account information", "Customer inquires about a transaction made on January 3rd and when the funds will be deposited into their bank account.", "Jerry is having trouble connecting his P 8920 pro device to Wi Fi and running a test transaction.\n"], "0_ncluster": 6, "0_output": "", "0_ndim": "2D (press for 3D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "e17087a194244a449b118f2e91c58427", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": ["Pack C 920 (Taxi 920, Stripe Simple) declining all credit card transactions with invalid and purchase sequence errors", "Issue: Credit card transaction declined after power loss", "Customer wants a summary of last year's statements with fees removed for the business, Cloudy Transportation Group Inc.", "calls technical support as he is not receiving his credit card money due to a payment not going through", " Customer contacts Sierra, seeking information about a check that has been pending for 7 months.", "reported that their payments were not being processed and deposited into their bank account. The last successful deposit was on June 17th.", "Agent confirms that the account was frozen, but a maintenance change was made to update the bank account information", "Customer inquires about a transaction made on January 3rd and when the funds will be deposited into their bank account.", "Jerry is having trouble connecting his P 8920 pro device to Wi Fi and running a test transaction.\n"], "1_ncluster": 6, "1_output": "", "1_ndim": "2D (press for 3D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1727946015.7299, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "a71d87523d6f4fecbd5f141f32d4f9ad", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": ["plane", "parabolic", "concave", "oboe", "trumpet", "trombone", "saxophone", "flute"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "6477c3e7b5c243a89a74a48c2defc4de", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": ["plane", "parabolic", "concave", "oboe", "trumpet", "trombone", "saxophone", "flute"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1727966932.5252, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "012b94091a7741708cf9767775bd8c45", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": ["Yes, yes, yes", "The Americans are cheating", "Long live Egypt", "Egypt is the best military partner for anyone", "Just the fact of briefcases should be a reason to annul that mining contract", "Yes, coz we don't have enough military equipment", "America is the first enemy of Egypt"], "0_ncluster": 1, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "9c257597f8464ed6abbe7dc93d23a4ea", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": ["Yes, yes, yes", "The Americans are cheating", "Long live Egypt", "Egypt is the best military partner for anyone", "Just the fact of briefcases should be a reason to annul that mining contract", "Yes, coz we don't have enough military equipment", "America is the first enemy of Egypt"], "1_ncluster": 1, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1728063234.689, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "411dd2d0ca4749dda51f7cbd113c17b4", "0_model_name": "embed-english-v3.0", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "aaa9378a17fc4b76aaa63eab3d1033bd", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -106,3 +106,5 @@
 {"tstamp": 1727966879.9404, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1727966879.8649, "finish": 1727966879.9404, "ip": "", "conv_id": "9c257597f8464ed6abbe7dc93d23a4ea", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Yes, yes, yes", "The Americans are cheating", "Long live Egypt", "Egypt is the best military partner for anyone", "Just the fact of briefcases should be a reason to annul that mining contract", "Yes, coz we don't have enough military equipment"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1727966896.9608, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1727966896.9008, "finish": 1727966896.9608, "ip": "", "conv_id": "012b94091a7741708cf9767775bd8c45", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Yes, yes, yes", "The Americans are cheating", "Long live Egypt", "Egypt is the best military partner for anyone", "Just the fact of briefcases should be a reason to annul that mining contract", "Yes, coz we don't have enough military equipment", "America is the first enemy of Egypt"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1727966896.9608, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1727966896.9008, "finish": 1727966896.9608, "ip": "", "conv_id": "9c257597f8464ed6abbe7dc93d23a4ea", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Yes, yes, yes", "The Americans are cheating", "Long live Egypt", "Egypt is the best military partner for anyone", "Just the fact of briefcases should be a reason to annul that mining contract", "Yes, coz we don't have enough military equipment", "America is the first enemy of Egypt"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1728063141.2972, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1728063141.0444, "finish": 1728063141.2972, "ip": "", "conv_id": "411dd2d0ca4749dda51f7cbd113c17b4", "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": 1728063141.2972, "task_type": "clustering", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1728063141.0444, "finish": 1728063141.2972, "ip": "", "conv_id": "aaa9378a17fc4b76aaa63eab3d1033bd", "model_name": "nomic-ai/nomic-embed-text-v1.5", "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"}

data/retrieval_battle-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -161,3 +161,5 @@
 {"tstamp": 1728055888.5455, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "aeaf9946eb9b40cb8df74422d910e9fe", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Семантический поиск похожих научных статей на русском языке", "0_output": [["Семантический поиск похожих научных статей на русском языке", "Title: Evaluation experiments on related terms search in Wikipedia: Information Content and Adapted HITS (In Russian)\n\nAbstract: The classification of metrics and algorithms search for related terms via WordNet, Roget's Thesaurus, and Wikipedia was extended to include adapted HITS algorithm. Evaluation experiments on Information Content and adapted HITS algorithm are described. The test collection of Russian word pairs with human-assigned similarity judgments is proposed. ----- Klassifikacija metrik i algoritmov poiska semanticheski blizkih slov v tezaurusah WordNet, Rozhe i jenciklopedii Vikipedija rasshirena adaptirovannym HITS algoritmom. S pomow'ju jeksperimentov v Vikipedii oceneny metrika Information Content i adaptirovannyj algoritm HITS. Predlozhen resurs dlja ocenki semanticheskoj blizosti russkih slov."]], "0_corpus": "arxiv", "1_conv_id": "bd5ae926e2b747b58befb8cf04831a18", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Семантический поиск похожих научных статей на русском языке", "1_output": [["Семантический поиск похожих научных статей на русском языке", "Title: Texts in, meaning out: neural language models in semantic similarity task for Russian\n\nAbstract: Distributed vector representations for natural language vocabulary get a lot of attention in contemporary computational linguistics. This paper summarizes the experience of applying neural network language models to the task of calculating semantic similarity for Russian. The experiments were performed in the course of Russian Semantic Similarity Evaluation track, where our models took from the 2nd to the 5th position, depending on the task. We introduce the tools and corpora used, comment on the nature of the shared task and describe the achieved results. It was found out that Continuous Skip-gram and Continuous Bag-of-words models, previously successfully applied to English material, can be used for semantic modeling of Russian as well. Moreover, we show that texts in Russian National Corpus (RNC) provide an excellent training material for such models, outperforming other, much larger corpora. It is especially true for semantic relatedness tasks (although stacking models trained on larger corpora on top of RNC models improves performance even more). High-quality semantic vectors learned in such a way can be used in a variety of linguistic tasks and promise an exciting field for further study."]], "1_corpus": "arxiv"}
 {"tstamp": 1728056690.1517, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a18383d181684287829141784aefd5a6", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "What is the population of BERLIN ?", "0_output": [["What is the population of BERLIN ?", "Berlin\n\nBerlin is the capital and largest city of Germany, both by area and by population. Its more than 3.85 million inhabitants make it the European Union's most populous city, as measured by population within city limits having gained this status after the United Kingdom's, and thus London's, departure from the European Union. Simultaneously, the city is one of the states of Germany, and is the third smallest state in the country in terms of area. Berlin is surrounded by the state of Brandenburg, and Brandenburg's capital Potsdam is nearby. The urban area of Berlin has a population of over 4.5 million and is therefore the most populous urban area in Germany. The Berlin-Brandenburg capital region has around 6.2 million inhabitants and is Germany's second-largest metropolitan region after the Rhine-Ruhr region, and the sixth-biggest metropolitan region by GDP in the European Union.\nBerlin was built along the banks of the Spree river, which flows into the Havel in the western borough of Spandau. The city incorporates lakes in the western and southeastern boroughs, the largest of which is Müggelsee. About one-third of the city's area is composed of forests, parks and gardens, rivers, canals, and lakes."]], "0_corpus": "wikipedia", "1_conv_id": "3d6513ac1b5d46bdbab2e79d2a15b3c4", "1_model_name": "text-embedding-3-large", "1_prompt": "What is the population of BERLIN ?", "1_output": [["What is the population of BERLIN ?", "Demographics of Berlin\n\nThe industrialisation had brought about a rapid expansion of the suburbs, many of them developed explicitly for workers of specific factories, e.g. Siemensstadt and Borsigwalde. The Greater Berlin Act of 1920 (Groß-Berlin-Gesetz) boosted the population by incorporating many hitherto autonomous towns and cities, e.g. Spandau and Köpenick at the margins of the modern metropolis, but also Charlottenburg, nowadays almost in the heart of the city. The city approximately reached its modern extent, growing from  to . This expansion made Berlin the most populous city proper of Continental Europe in the interwar period (though not the largest agglomeration) and the third-largest in the world behind London and New York.\nThe four-million mark was surpassed in the 1920s, and in 1942, the officially registered population reached its maximum of 4.48 million, although because of the war conditions, this was an overestimation. More likely estimates based on food rationing data show lower numbers of 3.95 million people in February 1942 and only 3.11 million people in February 1944 (incl. 177,000 foreigners) when aerial attacks approached its most intense phase.\nIn the context of the more general huge population movements in immediately post-war Germany, a significant part of Berlin's pre-war population permanently resettled to other parts of Germany or abroad. A 1946 census counted 436,600 Berliners in the western occupation zones and 306,823 in the Soviet zone. In 1950, this number had risen to 518,218 in what had now become the Federal Republic. Since the end of World War II, the city population has been fluctuating between 3 and 3.5 million, with a low of less than 3.1 million from the mid-1970s to mid-1980s. Between 1950 and 1961, so between the establishment of the Soviet-backed German Democratic Republic and the construction of the Berlin Wall, most of the losses were incurred by East Berlin, while West Berlin showed modest growth of 2.3%."]], "1_corpus": "wikipedia"}
 {"tstamp": 1728063013.1007, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "67176d488f2749c899e84f99b8f923f9", "0_model_name": "Salesforce/SFR-Embedding-2_R", "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 Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "4f22b73f78a9421ea2de9704de319aba", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "1_corpus": "wikipedia"}
+{"tstamp": 1728063066.8492, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "ec5172c8c51f47fb87871f7bf3838a45", "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": "edcb56fde75b4f3999ad824083003998", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_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"]], "1_corpus": "stackexchange"}
+{"tstamp": 1728063101.1657, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "8ecd04b5a0534f32881ab2279639134b", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Why did the machines in The Matrix keep humans around?", "0_output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why do the machines allow/initiate the building of a new Zion? In the Matrix Reloaded, the Architect says:\n\nThe function of the One is now to return to the Source, allowing a temporary dissemination of the code you carry, reinserting the prime program. After which, you will be required to select from the Matrix 23 individuals - 16 female, 7 male - to rebuild Zion. Failure to comply with this process will result in a cataclysmic system crash, killing everyone connected to the Matrix, which, coupled with the extermination of Zion, will ultimately result in the extinction of the entire human race.\n\nWhy do the machines initiate the re-building of Zion?\n\nA: As was discussed elsewhere on the site, the machines are not interested in humans being wiped out, they (as shown in Animatrix) just want to co-exist with humans.\nAs such, the machines allow the building of new Zion so that humans would not - as the quote you provided says - would not go extinct.\n"]], "0_corpus": "stackexchange", "1_conv_id": "442e4ecc92e846e0a912f4417ef6a241", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Why did the machines in The Matrix keep humans around?", "1_output": [["Why did the machines in The Matrix keep humans around?", "Gis Stackexchange\n\nQ: Filling voids in DEM SRTM 1-arc second using QGIS by resampling and inserting another DEM I'd like to use the DEM SRTM 1-arc second (Version 4, 30m) from USGS for Europe. Unfortunately there are some voids. My idea is to fill those voids using the void-filled version 3 (90m) by resampling it to 30m and inserting the missing data in the version 4 DEM.\nAs I'm new to QGIS I don't know the proper steps. I already got a 30m map by reprojecting the dimensions times three but I'm not sure if that's the right thing to do and I have no idea how to fill the voids afterwards. \n"]], "1_corpus": "stackexchange"}

data/retrieval_individual-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -735,3 +735,5 @@
 {"tstamp": 1728062985.0282, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1728062982.8897, "finish": 1728062985.0282, "ip": "", "conv_id": "4f22b73f78a9421ea2de9704de319aba", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "corpus": "wikipedia"}
 {"tstamp": 1728063044.184, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1728063042.0057, "finish": 1728063044.184, "ip": "", "conv_id": "ec5172c8c51f47fb87871f7bf3838a45", "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": 1728063044.184, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1728063042.0057, "finish": 1728063044.184, "ip": "", "conv_id": "edcb56fde75b4f3999ad824083003998", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "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"]], "corpus": "stackexchange"}
+{"tstamp": 1728063089.4394, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1728063087.3371, "finish": 1728063089.4394, "ip": "", "conv_id": "8ecd04b5a0534f32881ab2279639134b", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why do the machines allow/initiate the building of a new Zion? In the Matrix Reloaded, the Architect says:\n\nThe function of the One is now to return to the Source, allowing a temporary dissemination of the code you carry, reinserting the prime program. After which, you will be required to select from the Matrix 23 individuals - 16 female, 7 male - to rebuild Zion. Failure to comply with this process will result in a cataclysmic system crash, killing everyone connected to the Matrix, which, coupled with the extermination of Zion, will ultimately result in the extinction of the entire human race.\n\nWhy do the machines initiate the re-building of Zion?\n\nA: As was discussed elsewhere on the site, the machines are not interested in humans being wiped out, they (as shown in Animatrix) just want to co-exist with humans.\nAs such, the machines allow the building of new Zion so that humans would not - as the quote you provided says - would not go extinct.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1728063089.4394, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1728063087.3371, "finish": 1728063089.4394, "ip": "", "conv_id": "442e4ecc92e846e0a912f4417ef6a241", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Gis Stackexchange\n\nQ: Filling voids in DEM SRTM 1-arc second using QGIS by resampling and inserting another DEM I'd like to use the DEM SRTM 1-arc second (Version 4, 30m) from USGS for Europe. Unfortunately there are some voids. My idea is to fill those voids using the void-filled version 3 (90m) by resampling it to 30m and inserting the missing data in the version 4 DEM.\nAs I'm new to QGIS I don't know the proper steps. I already got a 30m map by reprojecting the dimensions times three but I'm not sure if that's the right thing to do and I have no idea how to fill the voids afterwards. \n"]], "corpus": "stackexchange"}