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

StefanoZacchiroli at 15:10, 4 February 2024

2024-02-04T15:10:42Z

← Older revision		Revision as of 15:10, 4 February 2024
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	\|mentors=		\|mentors=
	* Stefano Zacchiroli <zack@upsilon.cc> (~~zack~~ on [[~~IRC~~]])		* Stefano Zacchiroli <zack@upsilon.cc> (Zack on [[Matrix]])
	}}		}}

	[[Category:Available internship]]		[[Category:Available internship]]

StefanoZacchiroli: add IRC nicknames

2021-03-02T10:34:55Z

add IRC nicknames

← Older revision		Revision as of 10:34, 2 March 2021
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	\|mentors=		\|mentors=
	* Stefano Zacchiroli <zack@upsilon.cc>		* Stefano Zacchiroli <zack@upsilon.cc> (zack on [[IRC]])
	}}		}}

	[[Category:Available internship]]		[[Category:Available internship]]

StefanoZacchiroli at 08:57, 18 January 2021

2021-01-18T08:57:36Z

← Older revision		Revision as of 08:57, 18 January 2021
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	Will be considered a plus:		Will be considered a plus:
	* natural language processing (NLP) training and experience		* [https://en.wikipedia.org/wiki/Natural_language_processing natural language processing (NLP)] training and experience

	\|mentors=		\|mentors=

StefanoZacchiroli: add link to french version

2021-01-18T08:55:40Z

add link to french version

← Older revision		Revision as of 08:55, 18 January 2021
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			(voir aussi: [[Large-scale license text recognition (internship)/fr\|version française]] du sujet)
			----
	{{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=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].

StefanoZacchiroli at 17:18, 16 February 2020

2020-02-16T17:18:06Z

← Older revision		Revision as of 17:18, 16 February 2020
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	[[Category:Available internship]]		[[Category:Available internship]]
	~~[[Category:Internship]]~~
	~~[[Category:Lang:English]]~~

StefanoZacchiroli at 15:49, 31 January 2020

2020-01-31T15:49:55Z

← Older revision		Revision as of 15:49, 31 January 2020
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	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.		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].		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 `LICENSE`, `COPYING`, etc., at the scale of Software Heritage.		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.		''All'' such files will be extracted from the archive, and suitable machine learning models will be designed and tested on the obtained corpus.

StefanoZacchiroli: StefanoZacchiroli moved page Large-scale license text detection (internship) to Large-scale license text recognition (internship) without leaving a redirect

2020-01-31T15:48:53Z

StefanoZacchiroli moved page Large-scale license text detection (internship) to Large-scale license text recognition (internship) without leaving a redirect

← Older revision	Revision as of 15:48, 31 January 2020
(No difference)

StefanoZacchiroli: StefanoZacchiroli moved page Large-scale license detection (internship) to Large-scale license text detection (internship) without leaving a redirect

2020-01-31T15:48:39Z

StefanoZacchiroli moved page Large-scale license detection (internship) to Large-scale license text detection (internship) without leaving a redirect

← Older revision	Revision as of 15:48, 31 January 2020
(No difference)

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..."

2020-01-31T15:47:42Z

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 `LICENSE`, `COPYING`, 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>
}}

[[Category:Available internship]]
[[Category:Internship]]
[[Category:Lang:English]]