BERTopic_v20240630_184948

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_v20240630_184948")

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