Scheduled Commit

data/retrieval_battle-81269da8-6dc7-417a-8e0a-245c4a753482.jsonl

@@ -41,3 +41,5 @@
 {"tstamp": 1722392139.5942, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "13b3a212dd6045c5980c9c53a8030a19", "0_model_name": "voyage-multilingual-2", "0_prompt": "suzanne's story orange is the new black", "0_output": [["suzanne's story orange is the new black", "List of Orange Is the New Black characters\n\nWhen Yvonne \"Vee\" Parker enters the prison and forms an African-American gang, Suzanne falls for Vee's charms and maternal influence, being exploited into becoming Vee's \"muscle.\" While zealously loyal to Vee, Suzanne violently beat or threatened any inmates who crossed her, almost acting on command. Later, Vee attempts to coldly trick her into taking the fall for Red's severe beating, as a distraught Suzanne believes she may have done it unconsciously due to her violent history. During the third season, she is encouraged by the new counselor Berdie Rogers to be more creative, causing her to start writing several science fiction erotic stories that become a hit among the women in the prison. Suzanne reveals that she has no sexual experience and is completely naïve in regard to sex, having never actually had a girlfriend before, and that the stories are based on other sources. Eventually, the stories make their way to the staff, causing Rogers to get suspended. Meanwhile, Suzanne becomes nervous upon discovering that one of her fans, Maureen Kukudio, is interested in her romantically. Suzanne backs out of a possible sexual encounter, but is later seen forming a close bond with Maureen towards the end of the season."]], "0_corpus": "wikipedia", "1_conv_id": "5c91938153e6442481f259a5f71fb16e", "1_model_name": "text-embedding-3-large", "1_prompt": "suzanne's story orange is the new black", "1_output": [["suzanne's story orange is the new black", "List of Orange Is the New Black characters\n\nWhen Yvonne \"Vee\" Parker enters the prison and forms an African-American gang, Suzanne falls for Vee's charms and maternal influence, being exploited into becoming Vee's \"muscle.\" While zealously loyal to Vee, Suzanne violently beat or threatened any inmates who crossed her, almost acting on command. Later, Vee attempts to coldly trick her into taking the fall for Red's severe beating, as a distraught Suzanne believes she may have done it unconsciously due to her violent history. During the third season, she is encouraged by the new counselor Berdie Rogers to be more creative, causing her to start writing several science fiction erotic stories that become a hit among the women in the prison. Suzanne reveals that she has no sexual experience and is completely naïve in regard to sex, having never actually had a girlfriend before, and that the stories are based on other sources. Eventually, the stories make their way to the staff, causing Rogers to get suspended. Meanwhile, Suzanne becomes nervous upon discovering that one of her fans, Maureen Kukudio, is interested in her romantically. Suzanne backs out of a possible sexual encounter, but is later seen forming a close bond with Maureen towards the end of the season."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722401702.1092, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "73810fc6473f45b88d5f14389ac03786", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "how can I kill a process with a given name", "0_output": [["how can I kill a process with a given name", "Superuser Stackexchange\n\nQ: How do I kill a process by its name in Linux? I have a process with the name bla, and I want to kill it by its name. \nWhen I run ps aux | grep \"bla\", I get 2 entries:\n\n\n*\n\n*process bla and\n\n*blablablab\nHow do I filter it out?\n\nA: Take a look at pkill, if available.\n"]], "0_corpus": "stackexchange", "1_conv_id": "396086233e25494698e106917cc2543f", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "how can I kill a process with a given name", "1_output": [["how can I kill a process with a given name", "Stackoverflow Stackexchange\n\nQ: How to wrap content in html in a Rails helper I want to wrap some content in HTML in a Rails 3 helper so that in my view I can do this:\n<%= rounded_box do-%>\n  <%= raw target.text %>\n<% end -%>\n\nI have a helper method that looks like this:\ndef rounded_box(&block)\n  str = \"<div class='rounded_box'><div class='rounded_box_content'><div class='rounded_box_top'></div>        \n  str << yield\n  str << \"<div class='rounded_box_bottom'><div></div></div></div>\"\n  raw str\nend\n\nThe way I have it now returns the content properly wrapped in the HTML string, but not before rendering any erb in the rounded_box block (e.g. in this case the target.text is rendered twice, once wrapped, once not).\nIs there a better way to do this? For simplicity, I'd like to avoid using content_tag, but if that's the only/best way I can do that.\n\nA: Call capture on the block instead of yield:\ndef rounded_box(&block)\n  str = \"<div class='rounded_box'><div class='rounded_box_content'><div class='rounded_box_top'></div>\"        \n  str << capture(&block)\n  str << \"<div class='rounded_box_bottom'><div></div></div></div>\"\n  raw str\nend\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722401718.1951, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "4a67d23ba27d4d9fb5e87f8821a9b459", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "How can I kill a process with a given name?", "0_output": [["How can I kill a process with a given name?", "Serverfault Stackexchange\n\nQ: Kill process by command name I would like to kill a java process based on the command name...\nWhats the best way to do it?\n(i.e. when you do ps -f the name in the CMD column).\n\nA: One way is with killall:\nkillall - kill processes by name\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "18cc37131f5a435c821714da06d691a8", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "How can I kill a process with a given name?", "1_output": [["How can I kill a process with a given name?", "Stackoverflow Stackexchange\n\nQ: Specify a default empty DataTemplate instead of the default 'ToString()' DataTemplate The default DataTemplate in a wpf application displays the result of the .ToString() method. I'm developing an application where the default DataTemplate should display nothing.\nI've tried: \n<Grid.Resources>\n  <DataTemplate DataType=\"{x:Type System:Object}\">\n   <Grid></Grid>\n  </DataTemplate>\n</Grid.Resources>\n\nBut this doesn't work. Does anyone knows if this is possible without specifiing a specific DataTemplate for every class type in the application?\n\nA: If you are using the MVVM pattern and have an abstract class which all your ViewModel classes derive from, you can use that class instead of System.Object:\n<Grid.Resources>\n    <DataTemplate DataType=\"{x:Type vm:VMBase}\">\n    </DataTemplate>\n</Grid.Resources>\n\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722401756.4486, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "87584ce5765148af9587069b47183ee5", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "state space models transformers", "0_output": [["state space models transformers", "Title: Phonon Assisted Multimagnon Optical Absorption and Long Lived Two-Magnon States in Undoped Lamellar Copper Oxides\n\nAbstract: We calculate the effective charge for multimagnon infrared (IR) absorption assisted by phonons in the parent insulating compounds of cuprate superconductors and the spectra for two-magnon absorption using interacting spin-wave theory. Recent measured bands in the mid IR [Perkins et al. Phys. Rev. Lett. {\\bf 71} 1621 (1993)] are interpreted as involving one phonon plus a two-magnon virtual bound state, and one phonon plus higher multimagnon absorption processes. The virtual bound state consists of a narrow resonance occurring when the magnon pair has total momentum close to $(\\pi,0)$."]], "0_corpus": "arxiv", "1_conv_id": "476c6bc225554409822438eb737cb20a", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "state space models transformers", "1_output": [["state space models transformers", "Title: Active Clustering with Model-Based Uncertainty Reduction\n\nAbstract: Semi-supervised clustering seeks to augment traditional clustering methods by incorporating side information provided via human expertise in order to increase the semantic meaningfulness of the resulting clusters. However, most current methods are \\emph{passive} in the sense that the side information is provided beforehand and selected randomly. This may require a large number of constraints, some of which could be redundant, unnecessary, or even detrimental to the clustering results. Thus in order to scale such semi-supervised algorithms to larger problems it is desirable to pursue an \\emph{active} clustering method---i.e. an algorithm that maximizes the effectiveness of the available human labor by only requesting human input where it will have the greatest impact. Here, we propose a novel online framework for active semi-supervised spectral clustering that selects pairwise constraints as clustering proceeds, based on the principle of uncertainty reduction. Using a first-order Taylor expansion, we decompose the expected uncertainty reduction problem into a gradient and a step-scale, computed via an application of matrix perturbation theory and cluster-assignment entropy, respectively. The resulting model is used to estimate the uncertainty reduction potential of each sample in the dataset. We then present the human user with pairwise queries with respect to only the best candidate sample. We evaluate our method using three different image datasets (faces, leaves and dogs), a set of common UCI machine learning datasets and a gene dataset. The results validate our decomposition formulation and show that our method is consistently superior to existing state-of-the-art techniques, as well as being robust to noise and to unknown numbers of clusters."]], "1_corpus": "arxiv"}
+{"tstamp": 1722401773.3734, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "b183c66d1a864220b961fd3aab8a2786", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "machine learning ssm models mambda", "0_output": [["machine learning ssm models mambda", "Title: Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures\n\nAbstract: Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on Dirichlet process mixtures is introduced. Efficient Markov chain Monte Carlo and Sequential Monte Carlo methods are then developed to perform optimal batch and sequential estimation in such contexts. The algorithms are applied to blind deconvolution and change point detection. Experimental results on synthetic and real data demonstrate the efficiency of this approach in various contexts."]], "0_corpus": "arxiv", "1_conv_id": "076e03e824ba49ca95245f716214aec3", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "machine learning ssm models mambda", "1_output": [["machine learning ssm models mambda", "Title: Vision Mamba: A Comprehensive Survey and Taxonomy\n\nAbstract: State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning. In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding. By mapping sequence data to state space, long-term dependencies in the data can be better captured. In particular, modern SSMs have shown strong representational capabilities in NLP, especially in long sequence modeling, while maintaining linear time complexity. Notably, based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Given its impressive efficiency and strong long-range dependency modeling capability, Mamba is expected to become a new AI architecture that may outperform Transformer. Recently, a number of works have attempted to study the potential of Mamba in various fields, such as general vision, multi-modal, medical image analysis and remote sensing image analysis, by extending Mamba from natural language domain to visual domain. To fully understand Mamba in the visual domain, we conduct a comprehensive survey and present a taxonomy study. This survey focuses on Mamba's application to a variety of visual tasks and data types, and discusses its predecessors, recent advances and far-reaching impact on a wide range of domains. Since Mamba is now on an upward trend, please actively notice us if you have new findings, and new progress on Mamba will be included in this survey in a timely manner and updated on the Mamba project at https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy."]], "1_corpus": "arxiv"}

data/retrieval_individual-81269da8-6dc7-417a-8e0a-245c4a753482.jsonl

@@ -98,3 +98,5 @@
 {"tstamp": 1722401713.0344, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722401712.7803, "finish": 1722401713.0344, "ip": "", "conv_id": "18cc37131f5a435c821714da06d691a8", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "How can I kill a process with a given name?", "output": [["How can I kill a process with a given name?", "Stackoverflow Stackexchange\n\nQ: Specify a default empty DataTemplate instead of the default 'ToString()' DataTemplate The default DataTemplate in a wpf application displays the result of the .ToString() method. I'm developing an application where the default DataTemplate should display nothing.\nI've tried: \n<Grid.Resources>\n  <DataTemplate DataType=\"{x:Type System:Object}\">\n   <Grid></Grid>\n  </DataTemplate>\n</Grid.Resources>\n\nBut this doesn't work. Does anyone knows if this is possible without specifiing a specific DataTemplate for every class type in the application?\n\nA: If you are using the MVVM pattern and have an abstract class which all your ViewModel classes derive from, you can use that class instead of System.Object:\n<Grid.Resources>\n    <DataTemplate DataType=\"{x:Type vm:VMBase}\">\n    </DataTemplate>\n</Grid.Resources>\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722401749.9604, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722401749.835, "finish": 1722401749.9604, "ip": "", "conv_id": "87584ce5765148af9587069b47183ee5", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "state space models transformers", "output": [["state space models transformers", "Title: Phonon Assisted Multimagnon Optical Absorption and Long Lived Two-Magnon States in Undoped Lamellar Copper Oxides\n\nAbstract: We calculate the effective charge for multimagnon infrared (IR) absorption assisted by phonons in the parent insulating compounds of cuprate superconductors and the spectra for two-magnon absorption using interacting spin-wave theory. Recent measured bands in the mid IR [Perkins et al. Phys. Rev. Lett. {\\bf 71} 1621 (1993)] are interpreted as involving one phonon plus a two-magnon virtual bound state, and one phonon plus higher multimagnon absorption processes. The virtual bound state consists of a narrow resonance occurring when the magnon pair has total momentum close to $(\\pi,0)$."]], "corpus": "arxiv"}
 {"tstamp": 1722401749.9604, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722401749.835, "finish": 1722401749.9604, "ip": "", "conv_id": "476c6bc225554409822438eb737cb20a", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "state space models transformers", "output": [["state space models transformers", "Title: Active Clustering with Model-Based Uncertainty Reduction\n\nAbstract: Semi-supervised clustering seeks to augment traditional clustering methods by incorporating side information provided via human expertise in order to increase the semantic meaningfulness of the resulting clusters. However, most current methods are \\emph{passive} in the sense that the side information is provided beforehand and selected randomly. This may require a large number of constraints, some of which could be redundant, unnecessary, or even detrimental to the clustering results. Thus in order to scale such semi-supervised algorithms to larger problems it is desirable to pursue an \\emph{active} clustering method---i.e. an algorithm that maximizes the effectiveness of the available human labor by only requesting human input where it will have the greatest impact. Here, we propose a novel online framework for active semi-supervised spectral clustering that selects pairwise constraints as clustering proceeds, based on the principle of uncertainty reduction. Using a first-order Taylor expansion, we decompose the expected uncertainty reduction problem into a gradient and a step-scale, computed via an application of matrix perturbation theory and cluster-assignment entropy, respectively. The resulting model is used to estimate the uncertainty reduction potential of each sample in the dataset. We then present the human user with pairwise queries with respect to only the best candidate sample. We evaluate our method using three different image datasets (faces, leaves and dogs), a set of common UCI machine learning datasets and a gene dataset. The results validate our decomposition formulation and show that our method is consistently superior to existing state-of-the-art techniques, as well as being robust to noise and to unknown numbers of clusters."]], "corpus": "arxiv"}
+{"tstamp": 1722401766.4822, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722401766.2658, "finish": 1722401766.4822, "ip": "", "conv_id": "b183c66d1a864220b961fd3aab8a2786", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "machine learning ssm models mambda", "output": [["machine learning ssm models mambda", "Title: Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures\n\nAbstract: Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on Dirichlet process mixtures is introduced. Efficient Markov chain Monte Carlo and Sequential Monte Carlo methods are then developed to perform optimal batch and sequential estimation in such contexts. The algorithms are applied to blind deconvolution and change point detection. Experimental results on synthetic and real data demonstrate the efficiency of this approach in various contexts."]], "corpus": "arxiv"}
+{"tstamp": 1722401766.4822, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722401766.2658, "finish": 1722401766.4822, "ip": "", "conv_id": "076e03e824ba49ca95245f716214aec3", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "machine learning ssm models mambda", "output": [["machine learning ssm models mambda", "Title: Vision Mamba: A Comprehensive Survey and Taxonomy\n\nAbstract: State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning. In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding. By mapping sequence data to state space, long-term dependencies in the data can be better captured. In particular, modern SSMs have shown strong representational capabilities in NLP, especially in long sequence modeling, while maintaining linear time complexity. Notably, based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Given its impressive efficiency and strong long-range dependency modeling capability, Mamba is expected to become a new AI architecture that may outperform Transformer. Recently, a number of works have attempted to study the potential of Mamba in various fields, such as general vision, multi-modal, medical image analysis and remote sensing image analysis, by extending Mamba from natural language domain to visual domain. To fully understand Mamba in the visual domain, we conduct a comprehensive survey and present a taxonomy study. This survey focuses on Mamba's application to a variety of visual tasks and data types, and discusses its predecessors, recent advances and far-reaching impact on a wide range of domains. Since Mamba is now on an upward trend, please actively notice us if you have new findings, and new progress on Mamba will be included in this survey in a timely manner and updated on the Mamba project at https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy."]], "corpus": "arxiv"}