class_docstr: Creates a topic model from a set of key terms based on sentence level co-occurrence.
class_docstr_tok: ["Creates","a","topic","model","from","a","set","of","key","terms","based","on","sentence","level","co-occurrence","."]
code: def _fit_model(self, pipe):
      if pipe is None:
          pipe = Pipeline(
              [
                  ("tfidf", TfidfTransformer(sublinear_tf=True)),
                  (
                      "nmf",
                      (
                          NMF(
                              n_components=30,
                              l1_ratio=0.5,
                              random_state=0,
                          )
                      ),
                  ),
              ]
          )
      pipe.fit_transform(self.sentX)
      return pipe
  
file_path: scattertext/scattertext/topicmodel/SentencesForTopicModeling.py
imports: import numpy
  import pandas
  from sklearn.decomposition import NMF
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.pipeline import Pipeline
  from scattertext.CSRMatrixTools import CSRMatrixFactory
  from scattertext.ParsedCorpus import ParsedCorpus
  from scattertext.termscoring.RankDifference import RankDifference
label_desc: Use image node_id 5 for calling the SentencesForTopicModeling obj's underlying member method code with example usage: obj._fit_model(pipe) and returns: pipe