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("paulperry/rag-topic-model")

topic_model.get_topic_info()

Topic overview

Number of topics: 6
Number of training documents: 168

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	klarna - for - my - to - the	11	-1_klarna_for_my_to
0	klarna - declined - my - in - ve	58	0_klarna_declined_my_in
1	to - payment - the - my - for	33	1_to_payment_the_my
2	my - details - klarna - and - call	25	2_my_details_klarna_and
3	it - store - the - for - ago	23	3_it_store_the_for
4	the - sneakers - they - shoes - ago	18	4_the_sneakers_they_shoes

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: 2.0.2
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: 0.60.0
Plotly: 6.0.0
Python: 3.9.6