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Register a new userEvaluating the carbon footprint of NLP methods: a survey and analysis of existing tools
تقييم بصمة الكربون لأساليب NLP: مسح وتحليل الأدوات الموجودة
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يجعل معالجة اللغة الطبيعية الحديثة (NLP) استخداما مكثفا لأساليب التعلم العميق بسبب الدقة التي تقدمها لمجموعة متنوعة من التطبيقات.نظرا للتأثير البيئي الكبير للبيئة للتعلم العميق، تم اقتراح تحليل التكلفة والفائدة بما في ذلك بصمة الكربون وكذلك تدابير الدقة لتحسين توثيق استخدام أساليب NLP للبحث أو النشر.في هذه الورقة، نراجع الأدوات المتاحة لقياس استخدام الطاقة وانبعاثات ثاني أكسيد الكربون لأساليب NLP.نحن تصف نطاق التدابير المقدمة ومقارنة استخدام ستة أدوات (تعقب الكربون، تعقب تأثير التجريب، الخوارزميات الخضراء، تأثير ثاني أكسيد الكربون، واستخدام الطاقة والاستزمي) على تجارب التعرف على الكيان المسماة المنجزة على إعدادات حسابية مختلفة (الخادم المحليمقابل مرفق الحوسبة).بناء على هذه النتائج، نقترح توصيات قابلة للتنفيذ لقياس الأثر البيئي بدقة تجارب NLP.
Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications. Due to the significant environmental impact of deep learning, cost-benefit analysis including carbon footprint as well as accuracy measures has been suggested to better document the use of NLP methods for research or deployment. In this paper, we review the tools that are available to measure energy use and CO2 emissions of NLP methods. We describe the scope of the measures provided and compare the use of six tools (carbon tracker, experiment impact tracker, green algorithms, ML CO2 impact, energy usage and cumulator) on named entity recognition experiments performed on different computational set-ups (local server vs. computing facility). Based on these findings, we propose actionable recommendations to accurately measure the environmental impact of NLP experiments.
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