dataman-git

## install
#!pip install bertopic
# You may want to install more depending on the transformers and language backends that you will be using. The possible installations are:
#!pip install bertopic[flair]
#!pip install bertopic[gensim]
#!pip install bertopic[spacy]
#!pip install bertopic[use]

## visualize_barchart
topic_model.visualize_barchart()

## visualize_hierarchy
topic_model.visualize_hierarchy()

## visualize_topics
topic_model.visualize_topics()

## get_document_info
topic_model.get_document_info(docs)

## get_topic
topic_model.get_topic(0)

## get_topic_info
topic_model.get_topic_info()

## save
topic_model.save("/content/gdrive/My Drive/data/gensim/ag_news_bertopic")

## topic_model
from bertopic import BERTopic
docs = train['Description']
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)

## load
import pandas as pd
import numpy as np
pd.set_option('display.max_colwidth', -1)
train = pd.read_csv("/content/gdrive/My Drive/data/gensim/ag_news_train.csv")
	#!pip install bertopic
	# You may want to install more depending on the transformers and language backends that you will be using. The possible installations are:
	#!pip install bertopic[flair]
	#!pip install bertopic[gensim]
	#!pip install bertopic[spacy]
	#!pip install bertopic[use]
	from bertopic import BERTopic
	docs = train['Description']
	topic_model = BERTopic()
	topics, probs = topic_model.fit_transform(docs)
	import pandas as pd
	import numpy as np
	pd.set_option('display.max_colwidth', -1)
	train = pd.read_csv("/content/gdrive/My Drive/data/gensim/ag_news_train.csv")