{"id":38448,"date":"2019-10-31T22:23:47","date_gmt":"2019-10-31T19:23:47","guid":{"rendered":"https:\/\/prohoster.info\/blog\/github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda\/"},"modified":"2019-10-31T22:23:47","modified_gmt":"2019-10-31T19:23:47","slug":"github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda","status":"publish","type":"post","link":"https:\/\/prohoster.info\/es\/blog\/news\/github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda","title":{"rendered":"GitHub ha abierto su trabajo sobre el uso de aprendizaje autom\u00e1tico para la b\u00fasqueda y an\u00e1lisis de c\u00f3digo.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

GitHub present\u00f3<\/a><\/noindex> iniciado hace varios a\u00f1os CodeSearchNet<\/a><\/noindex>, que ha preparado modelos de aprendizaje autom\u00e1tico y conjuntos de datos necesarios para el an\u00e1lisis, clasificaci\u00f3n y estudio de c\u00f3digo en varios lenguajes de programaci\u00f3n. CodeSearchNet, an\u00e1logamente a ImageNet<\/a><\/noindex>, incluye una gran colecci\u00f3n de fragmentos de c\u00f3digo, acompa\u00f1ados de anotaciones que formalizan las acciones ejecutadas por el c\u00f3digo. Los componentes para entrenar los modelos y ejemplos de uso de CodeSearchNet est\u00e1n escritos en Python utilizando el marco Tensorflow y se distribuye<\/a><\/noindex> bajo licencia MIT.<\/p>\n

Al crear CodeSearchNet, se utilizaron tecnolog\u00edas de an\u00e1lisis de texto en lenguaje natural, que permiten a los sistemas de aprendizaje autom\u00e1tico considerar no solo las caracter\u00edsticas sint\u00e1cticas, sino tambi\u00e9n el sentido de las acciones ejecutadas por el c\u00f3digo. En GitHub, el sistema se utiliza<\/a><\/noindex> en experimentos de organizaci\u00f3n de b\u00fasqueda sem\u00e1ntica de c\u00f3digo utilizando consultas en lenguaje natural<\/a><\/noindex> (por ejemplo, al solicitar «ordenar una lista de cadenas», se muestra el c\u00f3digo que implementa los algoritmos correspondientes).<\/p>\n

El conjunto de datos propuesto incluye m\u00e1s de 2 millones de pares «c\u00f3digo-comentario», preparados en base a los textos fuente de bibliotecas abiertas existentes. El c\u00f3digo abarca el texto completo de funciones o m\u00e9todos individuales, mientras que el comentario describe las acciones realizadas por la funci\u00f3n (se presenta una documentaci\u00f3n detallada). Actualmente, se han preparado conjuntos de datos para los lenguajes Python, JavaScript, Ruby, Go, Java y PHP. Se proporcionan ejemplos de uso de los conjuntos de datos propuestos para entrenar diferentes tipos de redes neuronales, incluyendo Neural-Bag-Of-Words<\/a><\/noindex>, RNN<\/a><\/noindex>, Self-Attention<\/a><\/noindex> (BERT) y 1D-CNN+Self-Attention Hybrid<\/a><\/noindex>.<\/p>\n

Para desarrollar mecanismos de b\u00fasqueda en lenguaje natural, se ha preparado adicionalmente un conjunto CodeSearchNet Challenge, que incluye
\n99 consultas tipo<\/a><\/noindex> con alrededor de 4 mil anotaciones de expertos que describen los enlaces m\u00e1s probables al c\u00f3digo en el conjunto de datos CodeSearchNet Corpus, que abarca alrededor de 6 millones de m\u00e9todos y funciones (el tama\u00f1o del conjunto<\/a><\/noindex> alrededor de 20 GB). CodeSearchNet Challenge puede servir como referencia para evaluar la efectividad de diversos m\u00e9todos de b\u00fasqueda de c\u00f3digo en lenguaje natural. Usando la herramienta Kubeflow<\/a><\/noindex> preparado
\nejemplo<\/a><\/noindex> motor para la b\u00fasqueda de c\u00f3digo.<\/p>\n

<\/a><\/noindex><\/p>\n

Fuente: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

GitHub \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b \u043f\u0440\u043e\u0435\u043a\u0442 CodeSearchNet, \u0432 \u0440\u0430\u043c\u043a\u0430\u0445 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u044b \u043c\u043e\u0434\u0435\u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u043d\u0430\u0431\u043e\u0440\u044b \u0434\u0430\u043d\u043d\u044b\u0445, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0434\u043b\u044f \u0440\u0430\u0437\u0431\u043e\u0440\u0430, \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u0438 \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u043a\u043e\u0434\u0430 \u043d\u0430 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u044f\u0437\u044b\u043a\u0430\u0445 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f. CodeSearchNet, \u043f\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0438 \u0441 ImageNet, \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0431\u043e\u043b\u044c\u0448\u0443\u044e \u043a\u043e\u043b\u043b\u0435\u043a\u0446\u0438\u044e \u043e\u0442\u0440\u044b\u0432\u043a\u043e\u0432 \u043a\u043e\u0434\u0430, \u0441\u043d\u0430\u0431\u0436\u0451\u043d\u043d\u044b\u0445 \u0430\u043d\u043d\u043e\u0442\u0430\u0446\u0438\u044f\u043c\u0438, \u0444\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0443\u044e\u0449\u0438\u043c\u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c\u044b\u0435 \u043a\u043e\u0434\u043e\u043c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f. \u041a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u044b \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f CodeSearchNet \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u044b \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-38448","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47GitHub ha abierto sus desarrollos sobre la aplicaci\u00f3n de aprendizaje autom\u00e1tico para la b\u00fasqueda y an\u00e1lisis de c\u00f3digo | ProHoster","description":"GitHub present\u00f3 el 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