Large-scale license text recognition (internship)/fr - Revision history

StefanoZacchiroli at 15:11, 4 February 2024

2024-02-04T15:11:03Z

← Older revision		Revision as of 15:11, 4 February 2024
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	\|mentors=		\|mentors=
	* Stefano Zacchiroli <zack@upsilon.cc>		* Stefano Zacchiroli <zack@upsilon.cc> (Zack on [[Matrix]])
	}}		}}

StefanoZacchiroli at 08:58, 18 January 2021

2021-01-18T08:58:43Z

← Older revision		Revision as of 08:58, 18 January 2021
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	* connaissance de un ou plusieurs framework d'apprentissage automatique (p.ex., [https://keras.io/ Keras], [https://www.tensorflow.org/ TensorFlow], [https://scikit-learn.org/stable/ scikit-learn])		* connaissance de un ou plusieurs framework d'apprentissage automatique (p.ex., [https://keras.io/ Keras], [https://www.tensorflow.org/ TensorFlow], [https://scikit-learn.org/stable/ scikit-learn])

	Et considéré comme un plus:		Est considéré comme un plus:
	* une expérience avec la [https://fr.wikipedia.org/wiki/Traitement_automatique_des_langues traitement automatique des langues (TAL)]		* une expérience avec la [https://fr.wikipedia.org/wiki/Traitement_automatique_des_langues traitement automatique des langues (TAL)]

StefanoZacchiroli at 08:58, 18 January 2021

2021-01-18T08:58:29Z

← Older revision		Revision as of 08:58, 18 January 2021
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	* savoir développer en Python		* savoir développer en Python
	* une expérience avec l'apprentissage automatique		* une expérience avec l'apprentissage automatique
			* connaissance de un ou plusieurs framework d'apprentissage automatique (p.ex., [https://keras.io/ Keras], [https://www.tensorflow.org/ TensorFlow], [https://scikit-learn.org/stable/ scikit-learn])

	Et considéré comme un plus:		Et considéré comme un plus:

StefanoZacchiroli: add french translation

2021-01-18T08:54:51Z

add french translation

← Older revision		Revision as of 08:54, 18 January 2021
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			(see: [[Large-scale_license_text_recognition_(internship)\|english version]] of this internship description)
			----
	{{Internship		{{Internship
	\|description=~~A number of free/~~open source ~~software (FOSS) tools are available to automatically detect software licenses declared in~~ source ~~code files~~, e.g., [https://www.fossology.org/ Fossology], [https://github.com/nexB/scancode-toolkit ScanCode], [http://ninka.turingmachine.org/ Ninka].		\|description=Plusieurs solutions open source existent pour reconnaître la licence logicielle déclarée dans un fichier source, p.ex.: [https://www.fossology.org/ Fossology], [https://github.com/nexB/scancode-toolkit ScanCode], [http://ninka.turingmachine.org/ Ninka].
	~~Most of them rely on carefully maintained heuristics that have been tuned over many years to detect~~ [https://spdx.org/licenses/ ~~licenses~~] (~~FOSS or otherwise~~) ~~that can be found in the wild~~.		La pluspart des ces solutions sont basées sur des heuristiques qui ont été maintenues au fil des années sur la base des [https://spdx.org/licenses/ licences] (open source ou pas) existantes.
	~~Only relatively recently machine-learning~~ techniques ~~have been applied to the license-detection problem~~, in prototypes ~~like~~ [https://github.com/fossology/FOSSologyML FOSSologyML].		Seulement récemment les techniques d'apprentissage automatiques ont été appliquées au problème de reconnaissance des licences, avec des prototypes comme [https://github.com/fossology/FOSSologyML FOSSologyML].
	~~The goal of this internship is to apply machine-learning~~ techniques ~~to a limited sub~~-~~problem of license-detection~~, ~~i.e.~~, ~~recognizing full license texts as they are commonly found in top-level files such as~~ <code>LICENSE</code>, <code>COPYING</code>, etc., ~~at the scale of~~ Software Heritage.		Le but de ce stage est d'appliquer des techniques d'apprentissage automatique à un sous-problème spécifique de la reconnaissance des licences: classifier un texte complet de licence (et pas seulement une déclaration courte de licence, comme on peut le trouver dans les entêtes des fichiers source), comme on peut le trouver dans des fichiers comme <code>LICENSE</code>, <code>COPYING</code>, etc., à la racine des dépôts logiciels, et de le faire à l'échelle de Software Heritage.
	'~~'All'' such files will be extracted from the~~ archive, ~~and suitable machine learning models will be designed and tested~~ on ~~the obtained corpus~~.		Un jeu de données de ces fichiers sera extrait depuis l'archive, et on expérimentera avec plusieurs techniques d'apprentissage automatique pour identifier la méthode la plus efficace et performante.

	\|skills=		\|skills=
	* Python ~~development~~		* savoir développer en Python
	* ~~machine learning training and experience~~		* une expérience avec l'apprentissage automatique
	* working knowledge of one or more machine learning frameworks (e.g., [https://keras.io/ Keras], [https://www.tensorflow.org/ TensorFlow], [https://scikit-learn.org/stable/ scikit-learn])

	~~Will be considered a~~ plus:		Et considéré comme un plus:
	* ~~natural language processing~~ (~~NLP~~) ~~training and experience~~		* une expérience avec la [https://fr.wikipedia.org/wiki/Traitement_automatique_des_langues traitement automatique des langues (TAL)]

	\|mentors=		\|mentors=
	* Stefano Zacchiroli <zack@upsilon.cc>		* Stefano Zacchiroli <zack@upsilon.cc>
	}}		}}

StefanoZacchiroli: Created page with "{{Internship |description=A number of free/open source software (FOSS) tools are available to automatically detect software licenses declared in source code files, e.g., [http..."

2021-01-18T08:41:33Z

Created page with "{{Internship |description=A number of free/open source software (FOSS) tools are available to automatically detect software licenses declared in source code files, e.g., [http..."

New page

{{Internship
|description=A number of free/open source software (FOSS) tools are available to automatically detect software licenses declared in source code files, e.g., [https://www.fossology.org/ Fossology], [https://github.com/nexB/scancode-toolkit ScanCode], [http://ninka.turingmachine.org/ Ninka].
Most of them rely on carefully maintained heuristics that have been tuned over many years to detect [https://spdx.org/licenses/ licenses] (FOSS or otherwise) that can be found in the wild.
Only relatively recently machine-learning techniques have been applied to the license-detection problem, in prototypes like [https://github.com/fossology/FOSSologyML FOSSologyML].
The goal of this internship is to apply machine-learning techniques to a limited sub-problem of license-detection, i.e., recognizing full license texts as they are commonly found in top-level files such as <code>LICENSE</code>, <code>COPYING</code>, etc., at the scale of Software Heritage.
''All'' such files will be extracted from the archive, and suitable machine learning models will be designed and tested on the obtained corpus.

|skills=
* Python development
* machine learning training and experience
* working knowledge of one or more machine learning frameworks (e.g., [https://keras.io/ Keras], [https://www.tensorflow.org/ TensorFlow], [https://scikit-learn.org/stable/ scikit-learn])

Will be considered a plus:
* natural language processing (NLP) training and experience

|mentors=
* Stefano Zacchiroli <zack@upsilon.cc>
}}