MARTINI_enrich_BERTopic_vaxxpasssolutions

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_vaxxpasssolutions")

topic_model.get_topic_info()

Topic overview

Number of topics: 7
Number of training documents: 801

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	vaccinated - pfizer - cancers - symptoms - dna	45	-1_vaccinated_pfizer_cancers_symptoms
0	vaccines - myocarditis - died - strokes - injected	288	0_vaccines_myocarditis_died_strokes
1	vaccinated - cøvid - passport - zertifikate - qr	133	1_vaccinated_cøvid_passport_zertifikate
2	giftcards - atm - cash - cloned - 70k	119	2_giftcards_atm_cash_cloned
3	hydroxychloroquine - fenbendazole - 250mg - amoxicillin - budesonide	101	3_hydroxychloroquine_fenbendazole_250mg_amoxicillin
4	covid - misinformation - monkeypox - freedom - satan	61	4_covid_misinformation_monkeypox_freedom
5	dmt - pills - 100mg - oxycodone - adderall	54	5_dmt_pills_100mg_oxycodone

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