Or in using nltk.download(), which use downloader.py class.
This way works if you have an internet access or a proxy available : nltk.set_proxy('xxx')
However, most of time in big compagny, you don't have these access, the environment can be too sensitive, and you have to guarantee compatibility between several librairies and modules, etc.
Thanks to conda, we have a real delivery in a repository and artefact management, so we can get the different modules by a package manager, which resolve dependencies compatiblities. We can manage them by a repository, and not just by a curl.
Do you know if something is planned for NLTK Data, to manage a delivery for NLTK modules?
Hi,
I have a question on how to make the modules availables (pretrained models, dictionnary...)
Today we can get these modules directly by the URL :
Or in using
nltk.download(), which usedownloader.pyclass.This way works if you have an internet access or a proxy available :
nltk.set_proxy('xxx')However, most of time in big compagny, you don't have these access, the environment can be too sensitive, and you have to guarantee compatibility between several librairies and modules, etc.
Today, data science has plenty of librairies, and it's difficult to maintain compatibility between them, to do that, librairie like spaCy use conda repository and conda package manager.
Example : https://github.com/explosion/spacy-models/releases/ =>https://anaconda.org/conda-forge/spacy-model-de_core_news_sm
Thanks to conda, we have a real delivery in a repository and artefact management, so we can get the different modules by a package manager, which resolve dependencies compatiblities. We can manage them by a repository, and not just by a curl.
Do you know if something is planned for NLTK Data, to manage a delivery for NLTK modules?