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("apostolosfilippas/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 - klarna - to - my - the	15	-1_for_klarna_to_my
0	klarna - my - declined - for - ve	51	0_klarna_my_declined_for
1	payment - to - the - my - for	50	1_payment_to_the_my
2	my - klarna - and - details - account	28	2_my_klarna_and_details
3	store - the - it - refund - back	24	3_store_the_it_refund

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: 0.5.7
Pandas: 2.2.3
Scikit-Learn: 1.6.1
Sentence-transformers: 3.4.1
Transformers: 4.50.1
Numba: 0.61.2
Plotly: 6.0.1
Python: 3.11.7