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aaa961/
rag-topic-model

bertopic

text-classification

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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:

python

from bertopic import BERTopic
topic_model = BERTopic.load("aaa961/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	to - my - klarna - for - the	12	-1_to_my_klarna_for
0	klarna - my - declined - in - for	62	0_klarna_my_declined_in
1	my - details - klarna - and - call	34	1_my_details_klarna_and
2	the - payment - for - to - pay	24	2_the_payment_for_to
3	the - store - it - for - ago	19	3_the_store_it_for
4	the - ago - sneakers - and - shoes	17	4_the_ago_sneakers_and

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: 3.1.1
Transformers: 4.42.2
Numba: 0.60.0
Plotly: 6.1.2
Python: 3.9.22