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("labdmitriy/rag-topic-model")

topic_model.get_topic_info()

Topic overview

• Number of topics: 5
• Number of training documents: 201

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	my - for - to - account - payment	13	-1_my_for_to_account
0	refund - nike - my - store - for	35	0_refund_nike_my_store
1	my - the - for - klarna - payment	72	1_my_the_for_klarna
2	email - to - my - account - the	45	2_email_to_my_account
3	card - klarna - it - to - need	36	3_card_klarna_it_to

Training hyperparameters

• calculate_probabilities: False
• 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: 2.1.3
• HDBSCAN: 0.8.40
• UMAP: 0.5.7
• Pandas: 2.2.3
• Scikit-Learn: 1.6.1
• Sentence-transformers: 3.1.1
• Transformers: 4.45.2
• Numba: 0.61.0
• Plotly: 6.0.0
• Python: 3.11.5