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BERTopic_vjuly

This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.

Usage

To use this model, please install BERTopic:

pip install -U bertopic

You can use the model as follows:

from bertopic import BERTopic
topic_model = BERTopic.load("shantanudave/BERTopic_vjuly")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 18
  • Number of training documents: 8526
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
0 payment - pay - card - bank - money 742 Payment Issues Detection
1 load - slow - search - article - doesnt 705 Slow Search Function
2 clothes - clothing - size - fashion - large size 683 Large Size Quality Clothing
3 bon - - - - 668 bon documents collection
4 clear - intuitive - clear easy - recommend - selection 665 Easy Clear Navigation
5 - - - - 649 Keyword-Driven Document Analysis
6 shopping - staff - friendly - store - satisfy 578 Friendly staff satisfaction
7 delivery - fast delivery - fast - shipping - ship 563 Fast Delivery Quality
8 cart - shop cart - log - password - add 548 Shopping Cart Issues
9 easy use - easy - use - use easy - quick easy 531 Quick & Easy Solutions
10 awesome - excellent - think - clearly - phenomenal 462 Really Phenomenal Clear Thinking
11 quality - price - quality quality - price quality - comfortable 454 Excellent Quality Price
12 work work - work - work quickly - flawlessly - work flawlessly 390 Efficient Flawless Work
13 super super - super - superb - superb super - super friendly 349 Superb Friendly Coat
14 really simple - ra - solve problem - control - satisfied easy 145 User-Friendly Problem Solver
15 clear clear - clear - fast clear - clear fast - super clear 144 Clear and Transparent Working
16 discover - stuff good - stuff - fact - clearly 129 Discovering Interesting Facts
17 satisfied - satisfaction - totally satisfied - satisfied good - completely satisfied 121 Utmost Satisfaction

Training hyperparameters

  • calculate_probabilities: True
  • language: None
  • low_memory: False
  • min_topic_size: 10
  • n_gram_range: (1, 1)
  • nr_topics: None
  • seed_topic_list: None
  • top_n_words: 10
  • verbose: True
  • zeroshot_min_similarity: 0.7
  • zeroshot_topic_list: None

Framework versions

  • Numpy: 1.23.5
  • HDBSCAN: 0.8.33
  • UMAP: 0.5.5
  • Pandas: 1.3.5
  • Scikit-Learn: 1.4.1.post1
  • Sentence-transformers: 2.6.1
  • Transformers: 4.41.2
  • Numba: 0.59.1
  • Plotly: 5.22.0
  • Python: 3.10.13
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