Monolingual | Remove unnecessary language resources from macOS | Date Time Utils library

How can we use a different pretrained model for the text classifier in the ktrain library? When using:

model = text.text_classifier('bert', (x_train, y_train) , preproc=preproc)

This uses the multilangual pretrained model

However, I want to try out a monolingual model as well. Namely the Dutch one: ''wietsedv/bert-base-dutch-cased', which is also used in other k-train implementations, for example.

However, when trying to use this command in the text classifier it does not work:

I'm using a hosted weblate.

I'm in the "Files" section of my component configuration. Below is my setup.

Translation files:

• File format: gettext PO file
• Filemask: src/translations/*.po
• Language filter: ^[^.]+$

Monolingual translations:

• All fields empty
• Edit base file checked

Adding new languages:

• Template for new translations: src/translations/template.pot
• Adding new translation: Create new language file
• Language code style: Default based on the file format

I can't validate these settings, I have an error below fields "Template for new translations" and "Adding new translation": The error is "Unrecognized base file for new translations".

I'm am 100% sure the pot file exists on the branch weblate is configured to use and also on master and that the path is correct.

Here are the first few lines of the pot file:

I am cleaning the monolingual corpus of Europarl for French (http://data.statmt.org/wmt19/translation-task/fr-de/monolingual/europarl-v7.fr.gz). The original raw data in .gzfile (I downloaded using wget). I want to extract the text and see how it looks like in order to further process the corpus.

Using the following code to extract the text from gzip, I obtained data with the class being bytes.

I face problems when trying to get a numpy array from a tensorflow tensor. I use a tensorflow hub module but I don't want to use tensorflow in downstream tasks but rather need a numpy array.

I know that I have to call the 'eval()' method on the tensor from within a tensorflow session. But unfortuantely I cannot get it to work... :( It tells me that the "tables are not initialized". I tried to add 'sess.run(tf.tables_initializer())' but then I get the error: 'NotFoundError: Resource localhost/module_1/embeddings_morph_specialized/class tensorflow::Var does not exist'. I am not sure what to try next. I have also tried 'sess.run()' but have also been unsuccessful.

This is a seemingly simple question that I can't find an answer to.

I have a dataframe

I'm trying to implement Sultan Monolingual Aligner in finding a synsets using NLTK wordnet synsets.

And I have two lists :

We are working on implementing an autocompletion feature for an e-commerce site. We currently favorite the SuggestComponent implementation, but could probably try out some other options (FacetComponent,...). We have a single core with monolingual content.

Our problem is: Autocomplete query suggestions need to be restricted to several segments with their respective requirements, such as user role (new customer, special customer of group A, etc.), campaigns (products available during a special campaign, flash sales...), geographical target groups etc.

I have been researching the web, but haven't found any solutions for this use case, unfortunately. It actually goes beyond access control to documents according to user roles: there mustn't even be suggestions of terms that only lead to restricted content.

I'd be really glad if somebody had some hints, advice, best practices for me. Thank you in advance!