Scheduled Commit

data/retrieval_battle-35e094d9-c3d4-447e-b2f4-7dd3f5d1d585.jsonl

@@ -77,3 +77,7 @@
 {"tstamp": 1723473564.2565, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "c62acc0531384be8844239f57cf6f37e", "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": "390b448d6c6644f7b3d746f71dc20654", "1_model_name": "BAAI/bge-large-en-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: 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": 1723473606.4544, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "1343eeca1e064d2db2e836ee9ffc0273", "0_model_name": "GritLM/GritLM-7B", "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 use full brained humans as batteries? \nPossible Duplicate:\nIs the basic premise of humans as a power source in The Matrix reasonable?\nWhy Did the Machines Even Bother With a Matrix? \n\nThe machines in The Matrix series use humans as batteries. That's OK. But why did the machines not alter their livestock such that the matrix itself would be unnecessary?  \nConsider the recently proposed headless chicken farm. Something similar for the human batteries would seem to be an ideal solution for logical machines. No longer would they need to spend resources maintaining a virtual reality for their batteries. The energy, physical hardware and programs running the matrix could then be put to who knows what other uses.\n\nA: The explanation for the use of human brains would seem to be the fact that our complex thoughts lead to higher level of brain activity, thus resulting in exponentially more synapses firing (i.e. more electrical activity to harvest).\nThat makes human brains far more efficient than the brains of animals, even with the overhead of maintaining the matrix.  Plus, that movie would have really sucked... dumb cows and chickens revolt against the machines!  ;)\n"]], "0_corpus": "stackexchange", "1_conv_id": "6376c813b3a94bb99f23e44460b892d7", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Why did the machines in The Matrix keep humans around?", "1_output": [["Why did the machines in The Matrix keep humans around?", "Stackoverflow Stackexchange\n\nQ: Can't install gatsby-plugin-sharp.(libpng-dev may not installed) OS: Win 10\nwindows-build-tools is installed.\nWhen I was trying to install gatsby-plugin-sharp with npm install --save gatsby-plugin-sharp. I received two errors.\nI've tried this npm install --save-dev pngquant --msvs_version=2013  as someone mentioned. But it caused the same error with no 'libpng-dev'.\n\\> mozjpeg@6.0.1 postinstall C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\mozjpeg\n\\> node lib/install.js\n\n‼ getaddrinfo ENOENT raw.githubusercontent.com\n‼ mozjpeg pre-build test failed\ni compiling from source\n× Error: Command failed: C:\\Windows\\system32\\cmd.exe /s /c \"autoreconf -fiv\"\n'autoreconf' �����ڲ����ⲿ�����Ҳ���ǿ����еij���\n���������ļ���\n\nat C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\bin-build\\node_modules\\execa\\index.js:231:11\nat runMicrotasks (<anonymous>)\nat processTicksAndRejections (internal/process/task_queues.js:97:5)\nat async Promise.all (index 0)\n\n\\> pngquant-bin@5.0.2 postinstall C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\pngquant-bin\n\\> node lib/install.js\n\n‼ getaddrinfo ENOENT raw.githubusercontent.com\n‼ pngquant pre-build test failed\ni compiling from source\n× Error: pngquant failed to build, make sure that libpng-dev is installed\nat C:\\Users\\54391\\Documents\\code\\personal-blog\\node_modules\\bin-build\\node_modules\\execa\\index.js:231:11\nat processTicksAndRejections (internal/process/task_queues.js:97:5)\nat async Promise.all (index 0)\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1723473631.9608, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "ec1508740221411c80ef762aaeb99fcb", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "0_output": [["A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "Title: Is there anything non-classical?\n\nAbstract: It is argued that quantum logic and quantum probability theory are fascinating mathematical theories but without any relevance to our real world."]], "0_corpus": "arxiv", "1_conv_id": "61639fb40f3b49d8856bec9352cf5a13", "1_model_name": "text-embedding-3-large", "1_prompt": "A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "1_output": [["A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "Title: Autoregressive Image Generation using Residual Quantization\n\nAbstract: For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. In this study, we propose the two-stage framework, which consists of Residual-Quantized VAE (RQ-VAE) and RQ-Transformer, to effectively generate high-resolution images. Given a fixed codebook size, RQ-VAE can precisely approximate a feature map of an image and represent the image as a stacked map of discrete codes. Then, RQ-Transformer learns to predict the quantized feature vector at the next position by predicting the next stack of codes. Thanks to the precise approximation of RQ-VAE, we can represent a 256$\\times$256 image as 8$\\times$8 resolution of the feature map, and RQ-Transformer can efficiently reduce the computational costs. Consequently, our framework outperforms the existing AR models on various benchmarks of unconditional and conditional image generation. Our approach also has a significantly faster sampling speed than previous AR models to generate high-quality images."]], "1_corpus": "arxiv"}
+{"tstamp": 1723479366.0776, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "767e8e6e540a4a3484f52959bdca7746", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "theory for functional group test for aldehyde and ketones", "0_output": [["theory for functional group test for aldehyde and ketones", "Aldehyde\n\nEnolization Aldehydes (except those without an alpha carbon, or without protons on the alpha carbon, such as formaldehyde and benzaldehyde) can exist in either the keto or the enol tautomer. Keto–enol tautomerism is catalyzed by either acid or base. In neutral solution, the enol is the minority tautomer, reversing several times per second. But it becomes the dominant tautomer in strong acid or base solutions, and enolized aldehydes undergo nucleophilic attack at the α position.\nReduction\nThe formyl group can be readily reduced to a primary alcohol (). Typically this conversion is accomplished by catalytic hydrogenation either directly or by transfer hydrogenation. Stoichiometric reductions are also popular, as can be effected with sodium borohydride.\nOxidation\nThe formyl group readily oxidizes to the corresponding carboxyl group (). The preferred oxidant in industry is oxygen or air. In the laboratory, popular oxidizing agents include potassium permanganate, nitric acid, chromium(VI) oxide, and chromic acid. The combination of manganese dioxide, cyanide, acetic acid and methanol will convert the aldehyde to a methyl ester.\nAnother oxidation reaction is the basis of the silver-mirror test. In this test, an aldehyde is treated with Tollens' reagent, which is prepared by adding a drop of sodium hydroxide solution into silver nitrate solution to give a precipitate of silver(I) oxide, and then adding just enough dilute ammonia solution to redissolve the precipitate in aqueous ammonia to produce  complex. This reagent converts aldehydes to carboxylic acids without attacking carbon–carbon double bonds. The name silver-mirror test arises because this reaction produces a precipitate of silver, whose presence can be used to test for the presence of an aldehyde."]], "0_corpus": "wikipedia", "1_conv_id": "668f03f91f274000a5ddfe8cee96f8a8", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "theory for functional group test for aldehyde and ketones", "1_output": [["theory for functional group test for aldehyde and ketones", "Aldehyde\n\nIn organic chemistry, an aldehyde () is an organic compound containing a functional group with the structure . The functional group itself (without the \"R\" side chain) can be referred to as an aldehyde but can also be classified as a formyl group. Aldehydes are a common motif in many chemicals important in technology and biology."]], "1_corpus": "wikipedia"}
+{"tstamp": 1723479380.6687, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "5461a54dae0a4279afeedec071c77eae", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "can nikon d70 record video?", "0_output": [["can nikon d70 record video?", "Superuser Stackexchange\n\nQ: How to use my Nikon DSLR with Skype on a Mac I would like to use my Nikon D7000 on a Mac for Skype video calling.\nIs there a way to do this? There is a way, if I was using a Canon, but I'm not.\nAnd no need to tell me that there are webcams for that :). If I have hundreds of dollars worth of equipment, I want to use it.\n\nA: Nikon has \"Camera Control Pro 2\" ($$) and there is a program call Sofortbild (mac, free) which support \"live view\" and remote capture via usb or firewire. I have not used either.\nPerhaps you merely replace the live camera cap software in step 2 of the page which you posted. The free software is easy to test...\n(I use remote capture on a Canon DSLR from Breeze. They don't have Mac software for Nikon unfortunately, but AFAIK they use the Canon API which is what Canon EOS Util uses (the one in your link about doing this))\n"]], "0_corpus": "stackexchange", "1_conv_id": "7325962555ed493892b4b4df9c0c0cd7", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "can nikon d70 record video?", "1_output": [["can nikon d70 record video?", "Superuser Stackexchange\n\nQ: How to use my Nikon DSLR with Skype on a Mac I would like to use my Nikon D7000 on a Mac for Skype video calling.\nIs there a way to do this? There is a way, if I was using a Canon, but I'm not.\nAnd no need to tell me that there are webcams for that :). If I have hundreds of dollars worth of equipment, I want to use it.\n\nA: Nikon has \"Camera Control Pro 2\" ($$) and there is a program call Sofortbild (mac, free) which support \"live view\" and remote capture via usb or firewire. I have not used either.\nPerhaps you merely replace the live camera cap software in step 2 of the page which you posted. The free software is easy to test...\n(I use remote capture on a Canon DSLR from Breeze. They don't have Mac software for Nikon unfortunately, but AFAIK they use the Canon API which is what Canon EOS Util uses (the one in your link about doing this))\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1723479382.5762, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "df91a3f0467445b6a55024d5f159f78b", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Are there any Deberta-V3 pre-trained for other languages than English?", "0_output": [["Are there any Deberta-V3 pre-trained for other languages than English?", "Title: Quantum Surface of Section Method: Decomposition of the Resolvent (E - H)^(-1)\n\nAbstract: The paper presents exact surface of section reduction of quantum mechanics. The main theoretical result is a decomposition of the energy-dependent propagator G(E) = (E - H)^(-1) in terms of the propagators which (also or exclusively) act in Hilbert space of complex-valued functions over the configurational surface of section, which has one dimension less than the original configuration space. These energy-dependent quantum propagators from and/or onto the configurational surface of section can be explicitly constructed as the solutions of the first order nonlinear Riccati-like initial value problems."]], "0_corpus": "arxiv", "1_conv_id": "778f75bda18f460aac0ec0bd0a62c376", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Are there any Deberta-V3 pre-trained for other languages than English?", "1_output": [["Are there any Deberta-V3 pre-trained for other languages than English?", "Title: DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing\n\nAbstract: This paper presents a new pre-trained language model, DeBERTaV3, which improves the original DeBERTa model by replacing mask language modeling (MLM) with replaced token detection (RTD), a more sample-efficient pre-training task. Our analysis shows that vanilla embedding sharing in ELECTRA hurts training efficiency and model performance. This is because the training losses of the discriminator and the generator pull token embeddings in different directions, creating the \"tug-of-war\" dynamics. We thus propose a new gradient-disentangled embedding sharing method that avoids the tug-of-war dynamics, improving both training efficiency and the quality of the pre-trained model. We have pre-trained DeBERTaV3 using the same settings as DeBERTa to demonstrate its exceptional performance on a wide range of downstream natural language understanding (NLU) tasks. Taking the GLUE benchmark with eight tasks as an example, the DeBERTaV3 Large model achieves a 91.37% average score, which is 1.37% over DeBERTa and 1.91% over ELECTRA, setting a new state-of-the-art (SOTA) among the models with a similar structure. Furthermore, we have pre-trained a multi-lingual model mDeBERTa and observed a larger improvement over strong baselines compared to English models. For example, the mDeBERTa Base achieves a 79.8% zero-shot cross-lingual accuracy on XNLI and a 3.6% improvement over XLM-R Base, creating a new SOTA on this benchmark. We have made our pre-trained models and inference code publicly available at https://github.com/microsoft/DeBERTa."]], "1_corpus": "arxiv"}
+{"tstamp": 1723479394.9504, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "74a07111b8134cc09799527e488a6344", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "when did the broncos win the super bowl", "0_output": [["when did the broncos win the super bowl", "Denver Broncos\n\nThe team began play in 1960 as a charter member of the American Football League (AFL) and joined the NFL as part of the merger in 1970. The Broncos are currently owned by the Walton-Penner group. Since 2001, the Broncos have played their regular season home games at Empower Field at Mile High; Denver previously played its home games at Mile High Stadium from its inception in 1960 through the 2000 season.\nThe Broncos were barely competitive during their 10-year run in the AFL and their first three years in the NFL. They did not have a winning season until 1973 and qualified for their first playoffs in 1977, eventually advancing to Super Bowl XII that season. Since 1975, the Broncos have become one of the NFL's most successful teams, having suffered only eleven losing seasons. They have won eight AFC Championships (, , , , , , , ), and three Super Bowl championships ( (XXXII),  (XXXIII),  (50), and share the NFL record for most Super Bowl losses (5 – tied with the New England Patriots). The Broncos have nine primary members enshrined in the Pro Football Hall of Fame: John Elway, Floyd Little, Shannon Sharpe, Gary Zimmerman, Terrell Davis, Champ Bailey, Steve Atwater, and Randy Gradishar, along with late club owner Pat Bowlen."]], "0_corpus": "wikipedia", "1_conv_id": "5ce0883bd69b403eafd37655e748cffb", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "when did the broncos win the super bowl", "1_output": [["when did the broncos win the super bowl", "Super Bowl XXXIII\n\nSuper Bowl XXXIII was an American football game played between the American Football Conference (AFC) champion and defending Super Bowl XXXII champion Denver Broncos and the National Football Conference (NFC) champion Atlanta Falcons to decide the National Football League (NFL) champion for the 1998 season. The Broncos defeated the Falcons by the score of 34–19, winning their second consecutive Super Bowl, and becoming the first franchise to record consecutive Super Bowl victories & defeats. The game was played on January 31, 1999, at Pro Player Stadium in Miami, Florida (now part of the suburb of Miami Gardens, which became a separate city in 2003)."]], "1_corpus": "wikipedia"}

data/retrieval_individual-35e094d9-c3d4-447e-b2f4-7dd3f5d1d585.jsonl

@@ -259,3 +259,11 @@
 {"tstamp": 1723473626.8478, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723473624.3478, "finish": 1723473626.8478, "ip": "", "conv_id": "61639fb40f3b49d8856bec9352cf5a13", "model_name": "text-embedding-3-large", "prompt": "A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "output": [["A paper proposing a model that combines Residual-Quantized VAE and RQ-Transformer for high-resolution image generation.", "Title: Autoregressive Image Generation using Residual Quantization\n\nAbstract: For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. In this study, we propose the two-stage framework, which consists of Residual-Quantized VAE (RQ-VAE) and RQ-Transformer, to effectively generate high-resolution images. Given a fixed codebook size, RQ-VAE can precisely approximate a feature map of an image and represent the image as a stacked map of discrete codes. Then, RQ-Transformer learns to predict the quantized feature vector at the next position by predicting the next stack of codes. Thanks to the precise approximation of RQ-VAE, we can represent a 256$\\times$256 image as 8$\\times$8 resolution of the feature map, and RQ-Transformer can efficiently reduce the computational costs. Consequently, our framework outperforms the existing AR models on various benchmarks of unconditional and conditional image generation. Our approach also has a significantly faster sampling speed than previous AR models to generate high-quality images."]], "corpus": "arxiv"}
 {"tstamp": 1723478511.1514, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1723478508.9234, "finish": 1723478511.1514, "ip": "", "conv_id": "767e8e6e540a4a3484f52959bdca7746", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "theory for functional group test for aldehyde and ketones", "output": [["theory for functional group test for aldehyde and ketones", "Aldehyde\n\nEnolization Aldehydes (except those without an alpha carbon, or without protons on the alpha carbon, such as formaldehyde and benzaldehyde) can exist in either the keto or the enol tautomer. Keto–enol tautomerism is catalyzed by either acid or base. In neutral solution, the enol is the minority tautomer, reversing several times per second. But it becomes the dominant tautomer in strong acid or base solutions, and enolized aldehydes undergo nucleophilic attack at the α position.\nReduction\nThe formyl group can be readily reduced to a primary alcohol (). Typically this conversion is accomplished by catalytic hydrogenation either directly or by transfer hydrogenation. Stoichiometric reductions are also popular, as can be effected with sodium borohydride.\nOxidation\nThe formyl group readily oxidizes to the corresponding carboxyl group (). The preferred oxidant in industry is oxygen or air. In the laboratory, popular oxidizing agents include potassium permanganate, nitric acid, chromium(VI) oxide, and chromic acid. The combination of manganese dioxide, cyanide, acetic acid and methanol will convert the aldehyde to a methyl ester.\nAnother oxidation reaction is the basis of the silver-mirror test. In this test, an aldehyde is treated with Tollens' reagent, which is prepared by adding a drop of sodium hydroxide solution into silver nitrate solution to give a precipitate of silver(I) oxide, and then adding just enough dilute ammonia solution to redissolve the precipitate in aqueous ammonia to produce  complex. This reagent converts aldehydes to carboxylic acids without attacking carbon–carbon double bonds. The name silver-mirror test arises because this reaction produces a precipitate of silver, whose presence can be used to test for the presence of an aldehyde."]], "corpus": "wikipedia"}
 {"tstamp": 1723478511.1514, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723478508.9234, "finish": 1723478511.1514, "ip": "", "conv_id": "668f03f91f274000a5ddfe8cee96f8a8", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "theory for functional group test for aldehyde and ketones", "output": [["theory for functional group test for aldehyde and ketones", "Aldehyde\n\nIn organic chemistry, an aldehyde () is an organic compound containing a functional group with the structure . The functional group itself (without the \"R\" side chain) can be referred to as an aldehyde but can also be classified as a formyl group. Aldehydes are a common motif in many chemicals important in technology and biology."]], "corpus": "wikipedia"}
+{"tstamp": 1723479361.3546, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723479359.1678, "finish": 1723479361.3546, "ip": "", "conv_id": "df91a3f0467445b6a55024d5f159f78b", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Are there any Deberta-V3 pre-trained for other languages than English?", "output": [["Are there any Deberta-V3 pre-trained for other languages than English?", "Title: Quantum Surface of Section Method: Decomposition of the Resolvent (E - H)^(-1)\n\nAbstract: The paper presents exact surface of section reduction of quantum mechanics. The main theoretical result is a decomposition of the energy-dependent propagator G(E) = (E - H)^(-1) in terms of the propagators which (also or exclusively) act in Hilbert space of complex-valued functions over the configurational surface of section, which has one dimension less than the original configuration space. These energy-dependent quantum propagators from and/or onto the configurational surface of section can be explicitly constructed as the solutions of the first order nonlinear Riccati-like initial value problems."]], "corpus": "arxiv"}
+{"tstamp": 1723479361.3546, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723479359.1678, "finish": 1723479361.3546, "ip": "", "conv_id": "778f75bda18f460aac0ec0bd0a62c376", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Are there any Deberta-V3 pre-trained for other languages than English?", "output": [["Are there any Deberta-V3 pre-trained for other languages than English?", "Title: DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing\n\nAbstract: This paper presents a new pre-trained language model, DeBERTaV3, which improves the original DeBERTa model by replacing mask language modeling (MLM) with replaced token detection (RTD), a more sample-efficient pre-training task. Our analysis shows that vanilla embedding sharing in ELECTRA hurts training efficiency and model performance. This is because the training losses of the discriminator and the generator pull token embeddings in different directions, creating the \"tug-of-war\" dynamics. We thus propose a new gradient-disentangled embedding sharing method that avoids the tug-of-war dynamics, improving both training efficiency and the quality of the pre-trained model. We have pre-trained DeBERTaV3 using the same settings as DeBERTa to demonstrate its exceptional performance on a wide range of downstream natural language understanding (NLU) tasks. Taking the GLUE benchmark with eight tasks as an example, the DeBERTaV3 Large model achieves a 91.37% average score, which is 1.37% over DeBERTa and 1.91% over ELECTRA, setting a new state-of-the-art (SOTA) among the models with a similar structure. Furthermore, we have pre-trained a multi-lingual model mDeBERTa and observed a larger improvement over strong baselines compared to English models. For example, the mDeBERTa Base achieves a 79.8% zero-shot cross-lingual accuracy on XNLI and a 3.6% improvement over XLM-R Base, creating a new SOTA on this benchmark. We have made our pre-trained models and inference code publicly available at https://github.com/microsoft/DeBERTa."]], "corpus": "arxiv"}
+{"tstamp": 1723479374.2305, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723479371.9959, "finish": 1723479374.2305, "ip": "", "conv_id": "5461a54dae0a4279afeedec071c77eae", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "can nikon d70 record video?", "output": [["can nikon d70 record video?", "Superuser Stackexchange\n\nQ: How to use my Nikon DSLR with Skype on a Mac I would like to use my Nikon D7000 on a Mac for Skype video calling.\nIs there a way to do this? There is a way, if I was using a Canon, but I'm not.\nAnd no need to tell me that there are webcams for that :). If I have hundreds of dollars worth of equipment, I want to use it.\n\nA: Nikon has \"Camera Control Pro 2\" ($$) and there is a program call Sofortbild (mac, free) which support \"live view\" and remote capture via usb or firewire. I have not used either.\nPerhaps you merely replace the live camera cap software in step 2 of the page which you posted. The free software is easy to test...\n(I use remote capture on a Canon DSLR from Breeze. They don't have Mac software for Nikon unfortunately, but AFAIK they use the Canon API which is what Canon EOS Util uses (the one in your link about doing this))\n"]], "corpus": "stackexchange"}
+{"tstamp": 1723479374.2305, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1723479371.9959, "finish": 1723479374.2305, "ip": "", "conv_id": "7325962555ed493892b4b4df9c0c0cd7", "model_name": "GritLM/GritLM-7B", "prompt": "can nikon d70 record video?", "output": [["can nikon d70 record video?", "Superuser Stackexchange\n\nQ: How to use my Nikon DSLR with Skype on a Mac I would like to use my Nikon D7000 on a Mac for Skype video calling.\nIs there a way to do this? There is a way, if I was using a Canon, but I'm not.\nAnd no need to tell me that there are webcams for that :). If I have hundreds of dollars worth of equipment, I want to use it.\n\nA: Nikon has \"Camera Control Pro 2\" ($$) and there is a program call Sofortbild (mac, free) which support \"live view\" and remote capture via usb or firewire. I have not used either.\nPerhaps you merely replace the live camera cap software in step 2 of the page which you posted. The free software is easy to test...\n(I use remote capture on a Canon DSLR from Breeze. They don't have Mac software for Nikon unfortunately, but AFAIK they use the Canon API which is what Canon EOS Util uses (the one in your link about doing this))\n"]], "corpus": "stackexchange"}
+{"tstamp": 1723479387.8137, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1723479385.6347, "finish": 1723479387.8137, "ip": "", "conv_id": "74a07111b8134cc09799527e488a6344", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "when did the broncos win the super bowl", "output": [["when did the broncos win the super bowl", "Denver Broncos\n\nThe team began play in 1960 as a charter member of the American Football League (AFL) and joined the NFL as part of the merger in 1970. The Broncos are currently owned by the Walton-Penner group. Since 2001, the Broncos have played their regular season home games at Empower Field at Mile High; Denver previously played its home games at Mile High Stadium from its inception in 1960 through the 2000 season.\nThe Broncos were barely competitive during their 10-year run in the AFL and their first three years in the NFL. They did not have a winning season until 1973 and qualified for their first playoffs in 1977, eventually advancing to Super Bowl XII that season. Since 1975, the Broncos have become one of the NFL's most successful teams, having suffered only eleven losing seasons. They have won eight AFC Championships (, , , , , , , ), and three Super Bowl championships ( (XXXII),  (XXXIII),  (50), and share the NFL record for most Super Bowl losses (5 – tied with the New England Patriots). The Broncos have nine primary members enshrined in the Pro Football Hall of Fame: John Elway, Floyd Little, Shannon Sharpe, Gary Zimmerman, Terrell Davis, Champ Bailey, Steve Atwater, and Randy Gradishar, along with late club owner Pat Bowlen."]], "corpus": "wikipedia"}
+{"tstamp": 1723479387.8137, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1723479385.6347, "finish": 1723479387.8137, "ip": "", "conv_id": "5ce0883bd69b403eafd37655e748cffb", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "when did the broncos win the super bowl", "output": [["when did the broncos win the super bowl", "Super Bowl XXXIII\n\nSuper Bowl XXXIII was an American football game played between the American Football Conference (AFC) champion and defending Super Bowl XXXII champion Denver Broncos and the National Football Conference (NFC) champion Atlanta Falcons to decide the National Football League (NFL) champion for the 1998 season. The Broncos defeated the Falcons by the score of 34–19, winning their second consecutive Super Bowl, and becoming the first franchise to record consecutive Super Bowl victories & defeats. The game was played on January 31, 1999, at Pro Player Stadium in Miami, Florida (now part of the suburb of Miami Gardens, which became a separate city in 2003)."]], "corpus": "wikipedia"}
+{"tstamp": 1723479399.1326, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1723479396.9082, "finish": 1723479399.1326, "ip": "", "conv_id": "63017f2afd1d419dabd96baaada8418d", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Are there any Deberta-V3 pre-trained for other languages than English?", "output": [["Are there any Deberta-V3 pre-trained for other languages than English?", "Title: DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing\n\nAbstract: This paper presents a new pre-trained language model, DeBERTaV3, which improves the original DeBERTa model by replacing mask language modeling (MLM) with replaced token detection (RTD), a more sample-efficient pre-training task. Our analysis shows that vanilla embedding sharing in ELECTRA hurts training efficiency and model performance. This is because the training losses of the discriminator and the generator pull token embeddings in different directions, creating the \"tug-of-war\" dynamics. We thus propose a new gradient-disentangled embedding sharing method that avoids the tug-of-war dynamics, improving both training efficiency and the quality of the pre-trained model. We have pre-trained DeBERTaV3 using the same settings as DeBERTa to demonstrate its exceptional performance on a wide range of downstream natural language understanding (NLU) tasks. Taking the GLUE benchmark with eight tasks as an example, the DeBERTaV3 Large model achieves a 91.37% average score, which is 1.37% over DeBERTa and 1.91% over ELECTRA, setting a new state-of-the-art (SOTA) among the models with a similar structure. Furthermore, we have pre-trained a multi-lingual model mDeBERTa and observed a larger improvement over strong baselines compared to English models. For example, the mDeBERTa Base achieves a 79.8% zero-shot cross-lingual accuracy on XNLI and a 3.6% improvement over XLM-R Base, creating a new SOTA on this benchmark. We have made our pre-trained models and inference code publicly available at https://github.com/microsoft/DeBERTa."]], "corpus": "arxiv"}
+{"tstamp": 1723479399.1326, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1723479396.9082, "finish": 1723479399.1326, "ip": "", "conv_id": "8797dc6ee9304811a8eab615b2d260ff", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Are there any Deberta-V3 pre-trained for other languages than English?", "output": [["Are there any Deberta-V3 pre-trained for other languages than English?", "Title: DeBERTinha: A Multistep Approach to Adapt DebertaV3 XSmall for Brazilian Portuguese Natural Language Processing Task\n\nAbstract: This paper presents an approach for adapting the DebertaV3 XSmall model pre-trained in English for Brazilian Portuguese natural language processing (NLP) tasks. A key aspect of the methodology involves a multistep training process to ensure the model is effectively tuned for the Portuguese language. Initial datasets from Carolina and BrWac are preprocessed to address issues like emojis, HTML tags, and encodings. A Portuguese-specific vocabulary of 50,000 tokens is created using SentencePiece. Rather than training from scratch, the weights of the pre-trained English model are used to initialize most of the network, with random embeddings, recognizing the expensive cost of training from scratch. The model is fine-tuned using the replaced token detection task in the same format of DebertaV3 training. The adapted model, called DeBERTinha, demonstrates effectiveness on downstream tasks like named entity recognition, sentiment analysis, and determining sentence relatedness, outperforming BERTimbau-Large in two tasks despite having only 40M parameters."]], "corpus": "arxiv"}