MARTINI_enrich_BERTopic_publicannouncement602967921

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_publicannouncement602967921")

topic_model.get_topic_info()

Topic overview

Number of topics: 7
Number of training documents: 838

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	vaccine - nhs - bbc - censorship - agenda	22	-1_vaccine_nhs_bbc_censorship
0	constables - allegations - warwickshire - informant - arrested	416	0_constables_allegations_warwickshire_informant
1	vaccinated - vaers - mhra - injections - myocarditis	160	1_vaccinated_vaers_mhra_injections
2	nhs - vaccinated - broadyorkshirelaw - autism - pupils	96	2_nhs_vaccinated_broadyorkshirelaw_autism
3	ukcitizen2021 - amendments - parliament - supranational - monkeypox	51	3_ukcitizen2021_amendments_parliament_supranational
4	bbcisthevirus - marches - antifa - banners - blff	48	4_bbcisthevirus_marches_antifa_banners
5	donotconsent - unite - parliament - unlawful - sworn	45	5_donotconsent_unite_parliament_unlawful

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