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Maximgolubov/rag-topic-model

sourceHugging Faceupdated 5mo agoView on Hugging Face

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tags:

—bertopic libraryname: bertopic pipelinetag: text-classification ---

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("Maximgolubov/rag-topic-model")

topic_model.get_topic_info()

Topic overview

—Number of topics: 5
—Number of training documents: 168

<details> <summary>Click here for an overview of all topics.</summary>

Topic ID	Topic Keywords	Topic Frequency	Label
-1	for - my - to - klarna - the	11	-1formytoklarna
0	the - klarna - my - for - to	38	0theklarnamyfor
1	samsung - the - it - for - and	76	1samsungtheitfor
2	my - details - klarna - and - call	23	2mydetailsklarnaand
3	my - to - time - you - one	20	3mytotimeyou

</details>

Training hyperparameters

—calculate_probabilities: False
—language: None
—low_memory: False
—mintopicsize: 10
—ngramrange: (1, 1)
—nr_topics: auto
—seedtopiclist: None
—topnwords: 10
—verbose: False
—zeroshotminsimilarity: 0.7
—zeroshottopiclist: None

Framework versions

—Numpy: 1.26.4
—HDBSCAN: 0.8.40
—UMAP: 0.5.8
—Pandas: 2.3.0
—Scikit-Learn: 1.7.0
—Sentence-transformers: 5.0.0
—Transformers: 4.45.2
—Numba: 0.61.2
—Plotly: 6.2.0
—Python: 3.11.9