BERTopic_andattakstruk_1

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("GiganticLemon/BERTopic_andattakstruk_1")

topic_model.get_topic_info()

Topic overview

• Number of topics: 10
• Number of training documents: 16559

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	the - and - to - of - in	329	-1_the_and_to_of
0	her - and - to - the - she	8589	0_her_and_to_the
1	the - to - and - of - is	1950	1_the_to_and_of
2	the - of - to - and - in	1740	2_the_of_to_and
3	the - to - and - of - in	1283	3_the_to_and_of
4	the - and - to - of - he	645	4_the_and_to_of
5	the - of - to - and - in	564	5_the_of_to_and
6	the - to - and - of - his	527	6_the_to_and_of
7	the - and - to - of - in	482	7_the_and_to_of
8	the - and - to - of - is	450	8_the_and_to_of

Training hyperparameters

• calculate_probabilities: False
• language: english
• low_memory: False
• min_topic_size: 10
• n_gram_range: (1, 1)
• nr_topics: None
• seed_topic_list: None
• top_n_words: 10
• verbose: True
• zeroshot_min_similarity: 0.7
• zeroshot_topic_list: None

Framework versions

• Numpy: 2.0.2
• HDBSCAN: 0.8.40
• UMAP: 0.5.7
• Pandas: 2.2.2
• Scikit-Learn: 1.6.1
• Sentence-transformers: 3.4.1
• Transformers: 4.51.3
• Numba: 0.60.0
• Plotly: 5.24.1
• Python: 3.11.12