![]() ![]() The language models for installation and their licences can be found at spaCy language models. Size: Package size indicator ( sm, md or lg ).Genre: The type of text on which the pipeline is trained, e.g. ![]() ![]() core for general-purpose pipeline with vocabulary, syntax, entities and word vectors, or dep for vocab and syntax only) In this code, _ is the model name under spaCy's naming convention. You can install the language models using the following command: It is left for the Site Administrator to install the model of the language in which their main content is written (and which the model licence allows). The spaCy language models are available under variety of licences and not all of them can be distributed with the Recommenders engine. The spaCy lookups data and the Stopwords ISO are listed in the requirements.txt file and will be installed when the Recommenders engine is set up. The engine chooses the best available resource for a language to clean and lemmatise the text in the order of spaCy language models, spaCy lookups data, and Stopwords ISO the spaCy language models being the best resource. The library langdetect is used for detecting the language of the text and is installed through the requirements.txt file and distributed under the Apache MIT licence. The Recommender engine can utilise one of the three types of language resources to clean and transform the free text for each language. The extent of text processing this block does depends on the type of external resources available on the machine the engine is running on. These resources are distributed under a variety of licences for each language that Totara supports. This block uses some external resources to process the text. This block processes the natural language and transforms the processed text into a TF-IDF matrix which will eventually be used by the machine learning model. The Full data processor block gets engaged when the Site Administrator has chosen the Full Hybrid mode from Quick-access menu > Plugins > Machine learning settings > Recommender engine, which will be accessible when the machine learning plugins are enabled. ![]()
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