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data/retrieval_individual-1948e9c4-e613-4487-b12e-0e988013b994.jsonl CHANGED
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  {"tstamp": 1722868585.0634, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722868582.9404, "finish": 1722868585.0634, "ip": "", "conv_id": "870a0d3050834336bfceff850d3a01d3", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "evolutionary algorithm for XAI biomedical", "output": [["evolutionary algorithm for XAI biomedical", "Title: XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications\n\nAbstract: Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing complex biological data. However, the lack of interpretability and transparency of these models presents challenges in leveraging these models for deeper biological insights and for generating testable hypotheses. Explainable AI (XAI) has emerged as a promising solution to enhance the transparency and interpretability of AI models in bioinformatics. This review provides a comprehensive analysis of various XAI techniques and their applications across various bioinformatics domains including DNA, RNA, and protein sequence analysis, structural analysis, gene expression and genome analysis, and bioimaging analysis. We introduce the most pertinent machine learning and XAI methods, then discuss their diverse applications and address the current limitations of available XAI tools. By offering insights into XAI's potential and challenges, this review aims to facilitate its practical implementation in bioinformatics research and help researchers navigate the landscape of XAI tools."]], "corpus": "arxiv"}
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  {"tstamp": 1722868585.0634, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722868582.9404, "finish": 1722868585.0634, "ip": "", "conv_id": "c7b93351e3cc4d059983c7e27aeb73de", "model_name": "BM25", "prompt": "evolutionary algorithm for XAI biomedical", "output": [["evolutionary algorithm for XAI biomedical", "Title: Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems\n\nAbstract: AI methods are finding an increasing number of applications, but their often black-box nature has raised concerns about accountability and trust. The field of explainable artificial intelligence (XAI) has emerged in response to the need for human understanding of AI models. Evolutionary computation (EC), as a family of powerful optimization and learning tools, has significant potential to contribute to XAI. In this paper, we provide an introduction to XAI and review various techniques in current use for explaining machine learning (ML) models. We then focus on how EC can be used in XAI, and review some XAI approaches which incorporate EC techniques. Additionally, we discuss the application of XAI principles within EC itself, examining how these principles can shed some light on the behavior and outcomes of EC algorithms in general, on the (automatic) configuration of these algorithms, and on the underlying problem landscapes that these algorithms optimize. Finally, we discuss some open challenges in XAI and opportunities for future research in this field using EC. Our aim is to demonstrate that EC is well-suited for addressing current problems in explainability and to encourage further exploration of these methods to contribute to the development of more transparent and trustworthy ML models and EC algorithms."]], "corpus": "arxiv"}
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  {"tstamp": 1722868667.9937, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722868667.7905, "finish": 1722868667.9937, "ip": "", "conv_id": "d62e239fe498444e933065e9b396eb56", "model_name": "GritLM/GritLM-7B", "prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "Kepler-62f\n\nKepler-62f (also known by its Kepler Object of Interest designation KOI-701.04) is a super-Earth exoplanet orbiting within the habitable zone of the star Kepler-62, the outermost of five such planets discovered around the star by NASA's Kepler spacecraft. It is located about from Earth in the constellation of Lyra."]], "corpus": "wikipedia"}
 
 
 
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  {"tstamp": 1722868585.0634, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722868582.9404, "finish": 1722868585.0634, "ip": "", "conv_id": "870a0d3050834336bfceff850d3a01d3", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "evolutionary algorithm for XAI biomedical", "output": [["evolutionary algorithm for XAI biomedical", "Title: XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications\n\nAbstract: Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing complex biological data. However, the lack of interpretability and transparency of these models presents challenges in leveraging these models for deeper biological insights and for generating testable hypotheses. Explainable AI (XAI) has emerged as a promising solution to enhance the transparency and interpretability of AI models in bioinformatics. This review provides a comprehensive analysis of various XAI techniques and their applications across various bioinformatics domains including DNA, RNA, and protein sequence analysis, structural analysis, gene expression and genome analysis, and bioimaging analysis. We introduce the most pertinent machine learning and XAI methods, then discuss their diverse applications and address the current limitations of available XAI tools. By offering insights into XAI's potential and challenges, this review aims to facilitate its practical implementation in bioinformatics research and help researchers navigate the landscape of XAI tools."]], "corpus": "arxiv"}
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  {"tstamp": 1722868585.0634, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722868582.9404, "finish": 1722868585.0634, "ip": "", "conv_id": "c7b93351e3cc4d059983c7e27aeb73de", "model_name": "BM25", "prompt": "evolutionary algorithm for XAI biomedical", "output": [["evolutionary algorithm for XAI biomedical", "Title: Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems\n\nAbstract: AI methods are finding an increasing number of applications, but their often black-box nature has raised concerns about accountability and trust. The field of explainable artificial intelligence (XAI) has emerged in response to the need for human understanding of AI models. Evolutionary computation (EC), as a family of powerful optimization and learning tools, has significant potential to contribute to XAI. In this paper, we provide an introduction to XAI and review various techniques in current use for explaining machine learning (ML) models. We then focus on how EC can be used in XAI, and review some XAI approaches which incorporate EC techniques. Additionally, we discuss the application of XAI principles within EC itself, examining how these principles can shed some light on the behavior and outcomes of EC algorithms in general, on the (automatic) configuration of these algorithms, and on the underlying problem landscapes that these algorithms optimize. Finally, we discuss some open challenges in XAI and opportunities for future research in this field using EC. Our aim is to demonstrate that EC is well-suited for addressing current problems in explainability and to encourage further exploration of these methods to contribute to the development of more transparent and trustworthy ML models and EC algorithms."]], "corpus": "arxiv"}
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  {"tstamp": 1722868667.9937, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722868667.7905, "finish": 1722868667.9937, "ip": "", "conv_id": "d62e239fe498444e933065e9b396eb56", "model_name": "GritLM/GritLM-7B", "prompt": "Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "output": [["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "Kepler-62f\n\nKepler-62f (also known by its Kepler Object of Interest designation KOI-701.04) is a super-Earth exoplanet orbiting within the habitable zone of the star Kepler-62, the outermost of five such planets discovered around the star by NASA's Kepler spacecraft. It is located about from Earth in the constellation of Lyra."]], "corpus": "wikipedia"}
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+ {"tstamp": 1722870822.0663, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722870821.8509, "finish": 1722870822.0663, "ip": "", "conv_id": "7d747b57ccce4c5081699cc9898418ff", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "redactar un informe completo, con introducción, objetivo general, 3 objetivos específicos, desarrollo, 4 conclusiones y 4 recomendaciones para evaluar la propuesta de instalar una marquesina solar para recarga de vehículos eléctricos, en un tono profesional", "output": [["redactar un informe completo, con introducción, objetivo general, 3 objetivos específicos, desarrollo, 4 conclusiones y 4 recomendaciones para evaluar la propuesta de instalar una marquesina solar para recarga de vehículos eléctricos, en un tono profesional", "Stackoverflow Stackexchange\n\nQ: Laravel Valet installation In trying to get Laravel Valet up and running, I have stumbled upon this issue. When running 'valet install' or 'valet park' or any valet-command at all, I get the following error:\n> PHP Fatal error: Cannot redeclare info() (previously declared in /Users/jantore/.composer/vendor/laravel/framework/src/Illuminate/Foundation/helpers.php:454) in /Users/jantore/.composer/vendor/laravel/valet/cli/includes/helpers.php on line 20\n> \n> Fatal error: Cannot redeclare info() (previously declared in\n> /Users/jantore/.composer/vendor/laravel/framework/src/Illuminate/Foundation/helpers.php:454)\n> in\n> /Users/jantore/.composer/vendor/laravel/valet/cli/includes/helpers.php\n> on line 20 eger:~ jantore$\n\nI went through the helpers.php document and tried to comment out the info()-function, but that didn't make a difference.\nI have a fresh Brew install with PHP 7.0 as per the Valet-install instructions. \nAnyone have any clue what this might be?\n\nUpdate: As per Ben Swinbourne's suggestion I tried with composer global dump-autoload. Unfortunately the same result, no change in the error. Thanx to Ben for the suggestion though :)\n\nA: I know this is an old topic, I recently had a similar issue, except mine was around the retry() function.\nto solve it.\ncomposer global remove laravel/valet\ncomposer global require laravel/valet\n\n"]], "corpus": "stackexchange"}
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+ {"tstamp": 1722870822.0663, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722870821.8509, "finish": 1722870822.0663, "ip": "", "conv_id": "7b87d50206af49e784a65b050f598985", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "redactar un informe completo, con introducción, objetivo general, 3 objetivos específicos, desarrollo, 4 conclusiones y 4 recomendaciones para evaluar la propuesta de instalar una marquesina solar para recarga de vehículos eléctricos, en un tono profesional", "output": [["redactar un informe completo, con introducción, objetivo general, 3 objetivos específicos, desarrollo, 4 conclusiones y 4 recomendaciones para evaluar la propuesta de instalar una marquesina solar para recarga de vehículos eléctricos, en un tono profesional", "Apple Stackexchange\n\nQ: Power a MacBook Off-Grid I'm interested in working remotely- very remotely off-grid in fact. How can I power a Macbook Pro & related peripherals using Solar Power?\n\nA: If you don’t want a do it yourself system. And you have a couple of thousand dollars, then you could purchase a couple of Jackery Portable charging stations and power each one with a portable Jackery solar panel array. I use the brand name just to point out that there are integrated off the shelf solutions. \nIt takes a long time to fully charge up a backup battery with a solar panel. So the 2 station setup probably doesn’t support a heavy computer usage scenario. But it’s clean and compact and portable and relatively light weight. And might put you into another path of investigation even if these don’t cover all your usage. \nTwo portable lithium batteries each with a collapsible solar panel array:\nAmazon Jackery Power Station\nAmazon Jackery Solar Panel\n"]], "corpus": "stackexchange"}