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AIDA-UPM/
MARTINI_enrich_BERTopic_vakcinacija

Name: MARTINI_enrich_BERTopic_vakcinacija
Availability: InStock

bertopic

text-classification

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MARTINI_enrich_BERTopic_vakcinacija

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("AIDA-UPM/MARTINI_enrich_BERTopic_vakcinacija")

topic_model.get_topic_info()

Topic overview

Number of topics: 6
Number of training documents: 685

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	koronavirusu - pfizer - kraujagysliu - 5g - 2022	30	-1_koronavirusu_pfizer_kraujagysliu_5g
0	pfizer - vaers - ivermektinas - trombocitopenija - nepageidaujamu	403	0_pfizer_vaers_ivermektinas_trombocitopenija
1	konstitucijos - reguliavimas - profilaktikos - privalo - kontraindikaciju	125	1_konstitucijos_reguliavimas_profilaktikos_privalo
2	vaccibody - antigenas - nanodaleliu - mrna - baltyma	61	2_vaccibody_antigenas_nanodaleliu_mrna
3	antivakcinacijos - vakseriai - imuniteto - nepasitikejimo - virusai	35	3_antivakcinacijos_vakseriai_imuniteto_nepasitikejimo
4	vakcinologu - fauci - omicron - hospitalizaciju - efektyvumo	31	4_vakcinologu_fauci_omicron_hospitalizaciju

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