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I am trying to use pyLDAvis to visualize the result of a topic modeling project. I notice that the scale of the axis for the frequency bar does not change when the length of the bars change as I slide…
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There are some default hyperparameters that cause errors every time they are used. For instance, the 'average' hyperparameter of Sklearn Imputer, will always fail for categorical features (we can't ca…
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Hola, no puedo avanzar :c
Cuando intento hacer `CountVectorizer().fit_transform(corpus).toarray()` como sale en el enunciado, se cae en el `toarray()` porque intenta alocar más de 40 GB de RAM. En mi…
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After setting the ngram_range=(2,2), the trained BERTopic model generates topics with 2-gram phrases such as Topic_1: {"Model Router", "Network Setup", etc}, but the individual words of each 2-gram ar…
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Hi
I have installed biterm on pycharm, and have the following imports in my code:
import numpy as np
import pyLDAvis
from biterm.cbtm import oBTM
from sklearn.feature_extraction.text import …
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https://grafana.com/
El aspecto es flipante, pero para que saque todos esos gráficos, habría que pasarle muchísimos datos, tipo, a lo largo del día entero ¿no? Y me preocupa la limitación de twitter.…
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I am getting results arranged according to the importance
```
def keyword_exctraction(self,new_text):
eng_stopwords = stopwords.words('english')
hinglish_stopwords=pd.read_csv("…
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* [Intuitive Guide to Latent Dirichlet Allocation](https://towardsdatascience.com/light-on-math-machine-learning-intuitive-guide-to-latent-dirichlet-allocation-437c81220158)
* [Spark LDA: A Complete …
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I understand that bert somehow did not need the pre-processing, but I did not want some words identified as a topic because the goal of my topic modeling. How can I achieve this?
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Hello,
I am curious for your thoughts on basic clustering of the UMAP results. It would essentially just be taking the a distance matrix of the embeddings and popping out the top 10 closest entitie…