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("juanpprim/rag-topic-model")

topic_model.get_topic_info()

Topic overview

Number of topics: 3
Number of training documents: 168

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	for - klarna - the - card - just	26	-1_for_klarna_the_card
0	my - the - to - for - klarna	22	0_my_the_to_for
1	my - klarna - and - details - account	120	1_my_klarna_and_details

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: 2.3.0+4.g1dfc98e16a
Scikit-Learn: 1.6.1
Sentence-transformers: 4.1.0
Transformers: 4.42.2
Numba: 0.60.0
Plotly: 6.1.2
Python: 3.9.22