Comments (2)
Evgeny-Egorov-Projects
commented on July 26, 2024
Hi there.
If you interested in obtaining vectors with their corresponding indices (words in topic or topics in the document) then it can be done by calling get_phi and get_theta methods from TopicModel (make sure that it is not a DummyModel you are working with). Those methods mainly use the code from BigARTM backbone in retrieving word-topic and topic-document distribution respectively. Note that unlike BigARTM (that uses batch vectorizer) we use dataset class from our library to extract topic-document distribution. Note that there are realizations for obtaining those distributions in dense/sparse matrices. Details on that methods can be found in documentation
from topicnet.
Evgeny-Egorov-Projects
commented on July 26, 2024
I suggest current issure is resolved
from topicnet.
Related Issues (20)
- We need to provide datasets in BOW and Natural-Order forms HOT 1
- Model's _fit should accept Dataset also, not just BatchVectorizer HOT 6
- Add some kind of Contacts section in README? HOT 5
- Junk stuff accumulates in dataset internals folder
- Dataset_manager - Connection refused HOT 3
- TopicNet's BaseRegularizer can't be used in cube settings?!
- Thetaless sometimes have troubles with modalities HOT 2
- Incorrect link to dataset HOT 1
- Downloading `postnauka` dataset produces error HOT 3
- WinError: Failed to load artm shared library from artm.dll
- Making-Decorrelation-and-Topic-Selection-Friends.ipynb Doesn't work in google colab from scratch HOT 2
- Dataset "ruwiki_good" does not want to be downloaded HOT 1
- Custom needs more custom handling HOT 1
- Where are the docs, TopDocs? HOT 1
- Datasets not loading HOT 2
- New Pandas can't work with old Dataset
- Reference HuggingFace Hosted Datasets
- Link to BigARTM Tutorial in Colab
- Wrong "K" in RTL-Wiki download link in demo notebook HOT 2
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from topicnet.