rag-topic-model

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("Maximgolubov/rag-topic-model")

topic_model.get_topic_info()

Topic overview

• Number of topics: 5
• Number of training documents: 168

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	for - my - to - klarna - the	11	-1_for_my_to_klarna
0	the - klarna - my - for - to	38	0_the_klarna_my_for
1	samsung - the - it - for - and	76	1_samsung_the_it_for
2	my - details - klarna - and - call	23	2_my_details_klarna_and
3	my - to - time - you - one	20	3_my_to_time_you

Training hyperparameters

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

Framework versions

• Numpy: 1.26.4
• HDBSCAN: 0.8.40
• UMAP: 0.5.8
• Pandas: 2.3.0
• Scikit-Learn: 1.7.0
• Sentence-transformers: 5.0.0
• Transformers: 4.45.2
• Numba: 0.61.2
• Plotly: 6.2.0
• Python: 3.11.9