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jkdamilola/
rag-topic-model

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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("jkdamilola/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 - klarna - for - my - the	10	-1_to_klarna_for_my
0	klarna - declined - my - in - ive	63	0_klarna_declined_my_in
1	payment - the - to - for - pay	33	1_payment_the_to_for
2	my - details - klarna - and - call	27	2_my_details_klarna_and
3	store - refund - back - the - credit	23	3_store_refund_back_the
4	the - shoes - ago - havent - sneakers	12	4_the_shoes_ago_havent

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: 3.1.1
Transformers: 4.42.2
Numba: 0.60.0
Plotly: 6.1.2
Python: 3.9.6