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3lv27/
rag-topic-model

Name: rag-topic-model
Availability: InStock

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("3lv27/rag-topic-model")

topic_model.get_topic_info()

Topic overview

Number of topics: 5
Number of training documents: 201

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	my - for - was - payment - it	17	-1_my_for_was_payment
0	refund - nike - my - store - for	41	0_refund_nike_my_store
1	my - the - payment - app - balance	72	1_my_the_payment_app
2	to - email - my - account - the	37	2_to_email_my_account
3	card - klarna - details - to - do	34	3_card_klarna_details_to

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: 2.0.2
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: 0.60.0
Plotly: 6.0.0
Python: 3.9.6