Scheduled Commit

data/retrieval_battle-3403941f-f995-496f-9668-ac196a27ebc6.jsonl

@@ -193,3 +193,4 @@
 {"tstamp": 1742724229.0379, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "6254c80467bb4a1f94063231ed88932e", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "iranian cinema", "0_output": [["iranian cinema", "Cinema of Iran\n\nThe cinema of Iran (), or of Persia, refers to the film industry in Iran. In particular, Iranian art films have garnered international recognition. Iranian films are usually written and spoken in the Persian language."]], "0_corpus": "wikipedia", "1_conv_id": "7df65a93ac2f402f8bd708f7b19ef893", "1_model_name": "voyage-multilingual-2", "1_prompt": "iranian cinema", "1_output": [["iranian cinema", "Cinema of Iran\n\nContemporary Iranian cinema Today, the Iranian box office is dominated by commercial Iranian films. Western films are occasionally shown in movie theaters.  and contemporary Hollywood productions are shown on state television. Iranian art films are often not screened officially, and are viewable via unlicensed DVDs which are available. Some of these acclaimed films were screened in Iran and had box office success. Examples include Rassul Sadr Ameli's \"I’m Taraneh, 15\", Rakhshan Bani-Etemad's \"Under the skin of the City\", Bahman Ghobadi's \"Marooned in Iraq\" and Manijeh Hekmat's \"Women's Prison\".\nCommercial cinema in Iran\nThe internationally award-winning cinema of Iran is quite different from the domestically oriented films. The latter caters to an entirely different audience, which is largely under the age of 25. This commercial Iranian cinema genre is largely unknown in the West, as the films are targeted at local audiences. There are Three categories of this type of film:\nFilms before the revolution.\nLor Girl, A Party in Hell, Qeysar, Dar Emtedade Shab, Amir Arsalan, and Ganj-e Qarun.\nFilms about the victory of the Iranian Revolution of 1979 and the ensuing Iran–Iraq war and Action filled with strong religious and national motifs.\nEagles, Barzakhiha, The Viper, Dadshah, Boycott, Duel, Taraj, Ekhrajiha, The Glass Agency, Kani Manga, Ofogh, Bashu, the Little Stranger, Leily Ba Man Ast, M as in Mother and The Night Bus."]], "1_corpus": "wikipedia"}
 {"tstamp": 1742732715.5013, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "ad9466985c9f45a09cf9d6e49a48d6ba", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Getting OOM error without cuda clear cache, why is this the case", "0_output": [["Getting OOM error without cuda clear cache, why is this the case", "Stackoverflow Stackexchange\n\nQ: Check failed: error == cudaSuccess (2 vs. 0) out of memory I am trying to run a neural network with pycaffe on gpu.\nThis works when I call the script for the first time.\nWhen I run the same script for the second time, CUDA throws the error in the title.\nBatch size is 1, image size at this moment is 243x322, the gpu has 8gb RAM.\nI guess I am missing a command that resets the memory?\nThank you very much!\nEDIT: \nMaybe I should clarify a few things: I am running caffe on windows. \nWhen i call the script with python script.py, the process terminates and the gpu memory gets freed, so this works.\nWith ipython, which I use for debugging, the GPU memory indeed does not get freed (after one pass, 6 of the 8 bg are in use, thanks for the nvidia-smi suggestion!)\nSo, what I am looking for is a command I can call from within pyhton, along the lines of:\nrun network\nprocess image output\nfree gpu memory\n\nA: Your GPU memory is not getting freed. This happens when the previous process is stopped but not terminated. See my answer here.\n"]], "0_corpus": "stackexchange", "1_conv_id": "16eeb2ae990441f09bffd52595cba2bf", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Getting OOM error without cuda clear cache, why is this the case", "1_output": [["Getting OOM error without cuda clear cache, why is this the case", "Stackoverflow Stackexchange\n\nQ: Surprising behavior with unicode dict keys Consider the following piece of code:\nIn [1]: a = {'ϵ': 1}\n\nIn [2]: b = dict(ϵ=1)\n\nIn [3]: a == b\nOut[3]: False\n\nIn [4]: print(a, b)\n{'ϵ': 1} {'ε': 1}\n\nI was surprised to find out that a is not equal to b. It appears that the resulting dicts use distinct Unicode symbols for epsilon, despite having similar definitions (I type \\epsilon + tab in my IPython environment).\nI wonder why this happens and if there is a preferred way to handle Unicode keys in this situation.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1742739728.0349, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "6ecbb67b491246a2a49558bf127909a9", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Paper on hierarchical reinforcement learning applied to open-domain dialogue systems.", "0_output": [["Paper on hierarchical reinforcement learning applied to open-domain dialogue systems.", "Title: Continuously Learning Neural Dialogue Management\n\nAbstract: We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems. A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue data and then continuously improve its behaviour via reinforcement learning, all using gradient-based algorithms on one single model. The experiments demonstrate the supervised model's effectiveness in the corpus-based evaluation, with user simulation, and with paid human subjects. The use of reinforcement learning further improves the model's performance in both interactive settings, especially under higher-noise conditions."]], "0_corpus": "arxiv", "1_conv_id": "e18d08b6a97448aca04bdaaf879fbb12", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "Paper on hierarchical reinforcement learning applied to open-domain dialogue systems.", "1_output": [["Paper on hierarchical reinforcement learning applied to open-domain dialogue systems.", "Title: Hierarchical Reinforcement Learning for Open-Domain Dialog\n\nAbstract: Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text. Reinforcement Learning (RL) is a powerful framework that could potentially address these issues, for example by allowing a dialog model to optimize for reducing toxicity and repetitiveness. However, previous approaches which apply RL to open-domain dialog generation do so at the word level, making it difficult for the model to learn proper credit assignment for long-term conversational rewards. In this paper, we propose a novel approach to hierarchical reinforcement learning, VHRL, which uses policy gradients to tune the utterance-level embedding of a variational sequence model. This hierarchical approach provides greater flexibility for learning long-term, conversational rewards. We use self-play and RL to optimize for a set of human-centered conversation metrics, and show that our approach provides significant improvements -- in terms of both human evaluation and automatic metrics -- over state-of-the-art dialog models, including Transformers."]], "1_corpus": "arxiv"}
+{"tstamp": 1742740457.53, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "34c2afa6354045bdaad6ba099e1cc97e", "0_model_name": "BM25", "0_prompt": "Como puedo utilizar agentes inteligentes con la herramienta Ray Tune", "0_output": [["Como puedo utilizar agentes inteligentes con la herramienta Ray Tune", "Title: Estudio de la Experiencia de Usuario mediante un Sistema de Dashboards de An\\'alisis de Aprendizaje Multimodal\n\nAbstract: In the article, we present a Web-based System called M2LADS, which supports the integration and visualization of multimodal data recorded in user experiences (UX) in a Learning Analytics (LA) system in the form of Web-based Dashboards. Based on the edBB platform, the multimodal data gathered contains biometric and behavioral signals including electroencephalogram data to measure learners' cognitive attention, heart rate for affective measures and visual attention from the video recordings. Additionally, learners' static background data and their learning performance measures are tracked using LOGGE tool. M2LADS provides opportunities to capture learners' holistic experience during their interactions with the learning analytic system in order to improve the system and the user experience of the learners. -- En este art\\'iculo, presentamos M2LADS, un sistema que permite la integraci\\'on y visualizaci\\'on de datos multimodales en forma de Dashboards Web. Estos datos provienen de sesiones de experiencia de usuario en un sistema de Learning Analytics (LA) llevadas a cabo por estudiantes de MOOCs. Los datos multimodales incluyen se\\~nales biom\\'etricas y de comportamiento monitorizados por la plataforma edBB, como electroencefalogramas (EEG) de 5 canales, frecuencia card\\'iaca, atenci\\'on visual, videos en el espectro visible y NIR, entre otros. Adem\\'as, se incluyen datos de interacci\\'on de los estudiantes con el sistema de LA a trav\\'es de la herramienta LOGGE. Toda esta informaci\\'on proporciona una comprensi\\'on completa de la experiencia del usuario al utilizar el sistema de LA, lo que ha permitido tanto mejorar el sistema LA como la experiencia de aprendizaje de los estudiantes de MOOCs."]], "0_corpus": "arxiv", "1_conv_id": "241a5baaaeb949d09af0e4aff5cfee27", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Como puedo utilizar agentes inteligentes con la herramienta Ray Tune", "1_output": [["Como puedo utilizar agentes inteligentes con la herramienta Ray Tune", "Title: FOLEY-VAE: Generaci\\'on de efectos de audio para cine con inteligencia artificial\n\nAbstract: In this research, we present an interface based on Variational Autoencoders trained with a wide range of natural sounds for the innovative creation of Foley effects. The model can transfer new sound features to prerecorded audio or microphone-captured speech in real time. In addition, it allows interactive modification of latent variables, facilitating precise and customized artistic adjustments. Taking as a starting point our previous study on Variational Autoencoders presented at this same congress last year, we analyzed an existing implementation: RAVE [1]. This model has been specifically trained for audio effects production. Various audio effects have been successfully generated, ranging from electromagnetic, science fiction, and water sounds, among others published with this work. This innovative approach has been the basis for the artistic creation of the first Spanish short film with sound effects assisted by artificial intelligence. This milestone illustrates palpably the transformative potential of this technology in the film industry, opening the door to new possibilities for sound creation and the improvement of artistic quality in film productions."]], "1_corpus": "arxiv"}

data/retrieval_individual-3403941f-f995-496f-9668-ac196a27ebc6.jsonl

@@ -834,3 +834,7 @@
 {"tstamp": 1742732690.3207, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1742732690.0777, "finish": 1742732690.3207, "ip": "", "conv_id": "16eeb2ae990441f09bffd52595cba2bf", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Getting OOM error without cuda clear cache, why is this the case", "output": [["Getting OOM error without cuda clear cache, why is this the case", "Stackoverflow Stackexchange\n\nQ: Surprising behavior with unicode dict keys Consider the following piece of code:\nIn [1]: a = {'ϵ': 1}\n\nIn [2]: b = dict(ϵ=1)\n\nIn [3]: a == b\nOut[3]: False\n\nIn [4]: print(a, b)\n{'ϵ': 1} {'ε': 1}\n\nI was surprised to find out that a is not equal to b. It appears that the resulting dicts use distinct Unicode symbols for epsilon, despite having similar definitions (I type \\epsilon + tab in my IPython environment).\nI wonder why this happens and if there is a preferred way to handle Unicode keys in this situation.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1742739706.2582, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1742739704.007, "finish": 1742739706.2582, "ip": "", "conv_id": "6ecbb67b491246a2a49558bf127909a9", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Paper on hierarchical reinforcement learning applied to open-domain dialogue systems.", "output": [["Paper on hierarchical reinforcement learning applied to open-domain dialogue systems.", "Title: Continuously Learning Neural Dialogue Management\n\nAbstract: We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems. A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue data and then continuously improve its behaviour via reinforcement learning, all using gradient-based algorithms on one single model. The experiments demonstrate the supervised model's effectiveness in the corpus-based evaluation, with user simulation, and with paid human subjects. The use of reinforcement learning further improves the model's performance in both interactive settings, especially under higher-noise conditions."]], "corpus": "arxiv"}
 {"tstamp": 1742739706.2582, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1742739704.007, "finish": 1742739706.2582, "ip": "", "conv_id": "e18d08b6a97448aca04bdaaf879fbb12", "model_name": "GritLM/GritLM-7B", "prompt": "Paper on hierarchical reinforcement learning applied to open-domain dialogue systems.", "output": [["Paper on hierarchical reinforcement learning applied to open-domain dialogue systems.", "Title: Hierarchical Reinforcement Learning for Open-Domain Dialog\n\nAbstract: Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text. Reinforcement Learning (RL) is a powerful framework that could potentially address these issues, for example by allowing a dialog model to optimize for reducing toxicity and repetitiveness. However, previous approaches which apply RL to open-domain dialog generation do so at the word level, making it difficult for the model to learn proper credit assignment for long-term conversational rewards. In this paper, we propose a novel approach to hierarchical reinforcement learning, VHRL, which uses policy gradients to tune the utterance-level embedding of a variational sequence model. This hierarchical approach provides greater flexibility for learning long-term, conversational rewards. We use self-play and RL to optimize for a set of human-centered conversation metrics, and show that our approach provides significant improvements -- in terms of both human evaluation and automatic metrics -- over state-of-the-art dialog models, including Transformers."]], "corpus": "arxiv"}
+{"tstamp": 1742740393.1, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1742740392.1815, "finish": 1742740393.1, "ip": "", "conv_id": "34c2afa6354045bdaad6ba099e1cc97e", "model_name": "BM25", "prompt": "Como puedo utilizar agentes inteligentes con la herramienta Ray Tune", "output": [["Como puedo utilizar agentes inteligentes con la herramienta Ray Tune", "Title: Estudio de la Experiencia de Usuario mediante un Sistema de Dashboards de An\\'alisis de Aprendizaje Multimodal\n\nAbstract: In the article, we present a Web-based System called M2LADS, which supports the integration and visualization of multimodal data recorded in user experiences (UX) in a Learning Analytics (LA) system in the form of Web-based Dashboards. Based on the edBB platform, the multimodal data gathered contains biometric and behavioral signals including electroencephalogram data to measure learners' cognitive attention, heart rate for affective measures and visual attention from the video recordings. Additionally, learners' static background data and their learning performance measures are tracked using LOGGE tool. M2LADS provides opportunities to capture learners' holistic experience during their interactions with the learning analytic system in order to improve the system and the user experience of the learners. -- En este art\\'iculo, presentamos M2LADS, un sistema que permite la integraci\\'on y visualizaci\\'on de datos multimodales en forma de Dashboards Web. Estos datos provienen de sesiones de experiencia de usuario en un sistema de Learning Analytics (LA) llevadas a cabo por estudiantes de MOOCs. Los datos multimodales incluyen se\\~nales biom\\'etricas y de comportamiento monitorizados por la plataforma edBB, como electroencefalogramas (EEG) de 5 canales, frecuencia card\\'iaca, atenci\\'on visual, videos en el espectro visible y NIR, entre otros. Adem\\'as, se incluyen datos de interacci\\'on de los estudiantes con el sistema de LA a trav\\'es de la herramienta LOGGE. Toda esta informaci\\'on proporciona una comprensi\\'on completa de la experiencia del usuario al utilizar el sistema de LA, lo que ha permitido tanto mejorar el sistema LA como la experiencia de aprendizaje de los estudiantes de MOOCs."]], "corpus": "arxiv"}
+{"tstamp": 1742740393.1, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1742740392.1815, "finish": 1742740393.1, "ip": "", "conv_id": "241a5baaaeb949d09af0e4aff5cfee27", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Como puedo utilizar agentes inteligentes con la herramienta Ray Tune", "output": [["Como puedo utilizar agentes inteligentes con la herramienta Ray Tune", "Title: FOLEY-VAE: Generaci\\'on de efectos de audio para cine con inteligencia artificial\n\nAbstract: In this research, we present an interface based on Variational Autoencoders trained with a wide range of natural sounds for the innovative creation of Foley effects. The model can transfer new sound features to prerecorded audio or microphone-captured speech in real time. In addition, it allows interactive modification of latent variables, facilitating precise and customized artistic adjustments. Taking as a starting point our previous study on Variational Autoencoders presented at this same congress last year, we analyzed an existing implementation: RAVE [1]. This model has been specifically trained for audio effects production. Various audio effects have been successfully generated, ranging from electromagnetic, science fiction, and water sounds, among others published with this work. This innovative approach has been the basis for the artistic creation of the first Spanish short film with sound effects assisted by artificial intelligence. This milestone illustrates palpably the transformative potential of this technology in the film industry, opening the door to new possibilities for sound creation and the improvement of artistic quality in film productions."]], "corpus": "arxiv"}
+{"tstamp": 1742740552.9091, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1742740552.7127, "finish": 1742740552.9091, "ip": "", "conv_id": "322da95bfb4245569375498731087ef1", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Como puedo utilizar los agentes inteligentes junto con una herramienta de hpo como ray tune", "output": [["Como puedo utilizar los agentes inteligentes junto con una herramienta de hpo como ray tune", "Title: Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals\n\nAbstract: Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures. We formalize this method as a dynamic treatment regime which comprises a sequence of decisions, one per stage of intervention, that follow a decision rule which maps current patient information to a recommendation for their next visit time. The dynamics of periodontal health, visit frequency, and patient compliance are complex, yet the estimated optimal regime must be interpretable to domain experts if it is to be integrated into clinical practice. We combine non-parametric Bayesian dynamics modeling with policy-search algorithms to estimate the optimal dynamic treatment regime within an interpretable class of regimes. Both simulation experiments and application to a rich database of electronic dental records from the HealthPartners HMO shows that our proposed method leads to better dental health without increasing the average recommended recall time relative to competing methods."]], "corpus": "arxiv"}
+{"tstamp": 1742740552.9091, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1742740552.7127, "finish": 1742740552.9091, "ip": "", "conv_id": "e90ee067b9704139b836b91d8d10c141", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Como puedo utilizar los agentes inteligentes junto con una herramienta de hpo como ray tune", "output": [["Como puedo utilizar los agentes inteligentes junto con una herramienta de hpo como ray tune", "Title: Tune: A Research Platform for Distributed Model Selection and Training\n\nAbstract: Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonable time frame. Many hyperparameter search algorithms have been proposed for improving the efficiency of model selection, however their adaptation to the distributed compute environment is often ad-hoc. We propose Tune, a unified framework for model selection and training that provides a narrow-waist interface between training scripts and search algorithms. We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation. We demonstrate the implementation of several state-of-the-art hyperparameter search algorithms in Tune. Tune is available at http://ray.readthedocs.io/en/latest/tune.html."]], "corpus": "arxiv"}