MARTINI_enrich_BERTopic_ConPy

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("AIDA-UPM/MARTINI_enrich_BERTopic_ConPy")

topic_model.get_topic_info()

Topic overview

Number of topics: 9
Number of training documents: 921

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	tucker - cuomo - supreme - immigration - subtitles	38	-1_tucker_cuomo_supreme_immigration
0	fauci - unvaxed - tyranny - jab - mandates	374	0_fauci_unvaxed_tyranny_jab
1	tucker - fauci - tonight - taliban - democrats	155	1_tucker_fauci_tonight_taliban
2	tucker - foxnews - tonight - presidential - 2020	86	2_tucker_foxnews_tonight_presidential
3	tucker - tonight - texas - migrants - governor	71	3_tucker_tonight_texas_migrants
4	jfk - undercover - ufo - documentary - assassination	62	4_jfk_undercover_ufo_documentary
5	tucker - carlson - zelensky - tonight - invading	55	5_tucker_carlson_zelensky_tonight
6	fauci - unvaxed - david - podcast - tyranny	41	6_fauci_unvaxed_david_podcast
7	crimea - sanctions - ussr - ukrainian - sovereignty	39	7_crimea_sanctions_ussr_ukrainian

Training hyperparameters

calculate_probabilities: True
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: 1.26.4
HDBSCAN: 0.8.40
UMAP: 0.5.7
Pandas: 2.2.3
Scikit-Learn: 1.5.2
Sentence-transformers: 3.3.1
Transformers: 4.46.3
Numba: 0.60.0
Plotly: 5.24.1
Python: 3.10.12