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Top2Vec learns jointly embedded topic, document and word vectors.

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When I install top2vec with the sentence encoders option, I get the error an unexpected keyword argument error for `embedding_model'. I think this is a gensim package clash version, but...

A few days ago the problem with `ValueError: numpy.ndarray size changed, may indicate binary incompatibility.` occurred during executing the code that worked one week ago without any problems. The same...

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) /home/sajjad/projects/binj/contextual_search/main.ipynb Cell 3' in () ----> [1](vscode-notebook-cell://ssh-remote%2Bpc-anydesk/home/sajjad/projects/binj/contextual_search/main.ipynb#ch0000002vscode-remote?line=0) from top2vec import Top2Vec File ~/miniconda3/envs/contextual/lib/python3.10/site-packages/top2vec/__init__.py:1, in ----> [1](file:///home/sajjad/miniconda3/envs/contextual/lib/python3.10/site-packages/top2vec/__init__.py?line=0) from top2vec.Top2Vec import Top2Vec [3](file:///home/sajjad/miniconda3/envs/contextual/lib/python3.10/site-packages/top2vec/__init__.py?line=2) __version__ =...

Maybe something goes wrong with umap-learn>=0.5.1 version. /opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead from cryptography.utils import int_from_bytes /opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead from cryptography.utils import...

Hello, I was wondering how feasible it would be to adapt top2vec or other topic modeling methods to make them online, as in you can update the model with new...

Thanks for your initial work! Please consider this small PR

`hierarchical_topic_reduction()` can sometimes fail with a `KeyError` if one of the topics has no documents associated with it. I can only reliably get it to reproduce with a pretty large...

I have more of a question than an issue. Can the same word vector be assigned to multiple topics in this model?

Thanks for a wonderful package for both topic modeling and clustering tasks. A huge huge step-up from earlier packages. I'm currently trying to fit trained model of 1.000.000 social media...

I got this error in self.document_vectors = self._embed_documents(documents) with this example: ``` import numpy as np import pandas as pd import json import os import ipywidgets as widgets from IPython.display...