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("przadka/rag-topic-model")

topic_model.get_topic_info()

Topic overview

Number of topics: 4
Number of training documents: 203

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	on - card - my - charge - account	54	-1_on_card_my_charge
0	refund - my - nike - for - store	16	0_refund_my_nike_for
1	to - my - klarna - email - and	77	1_to_my_klarna_email
2	my - the - payment - klarna - for	56	2_my_the_payment_klarna

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: False
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: 3.10.12