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AIDA-UPM/
MARTINI_enrich_BERTopic_covid

Name: MARTINI_enrich_BERTopic_covid
Availability: InStock

bertopic

text-classification

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MARTINI_enrich_BERTopic_covid

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_covid")

topic_model.get_topic_info()

Topic overview

Number of topics: 12
Number of training documents: 1269

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	fauci - vaccinated - injections - monkeypox - misinformation	21	-1_fauci_vaccinated_injections_monkeypox
0	vaccinated - deaths - pfizer - lancet - pericarditis	676	0_vaccinated_deaths_pfizer_lancet
1	omicron - booster - antibodies - shots - harvard	135	1_omicron_booster_antibodies_shots
2	fauci - coronaviruses - darpa - funded - origins	78	2_fauci_coronaviruses_darpa_funded
3	pandemic - gates - bannon - misinformation - klaus	66	3_pandemic_gates_bannon_misinformation
4	vaccinate - mrna - microbiologist - plasmid - bhakdi	61	4_vaccinate_mrna_microbiologist_plasmid
5	pfizer - whistleblower - davos - lawsuits - stephane	55	5_pfizer_whistleblower_davos_lawsuits
6	quarantine - china - shenzhen - passport - red	48	6_quarantine_china_shenzhen_passport
7	nattokinase - pfizer - bromelain - bleeding - exosomes	39	7_nattokinase_pfizer_bromelain_bleeding
8	ivermectin - remdesivir - hydroxychloroquine - penicillin - medications	37	8_ivermectin_remdesivir_hydroxychloroquine_penicillin
9	strokes - encephalitis - neurologists - aphasia - astrazeneca	31	9_strokes_encephalitis_neurologists_aphasia
10	australia - unjabbed - djokovic - banned - andrews	22	10_australia_unjabbed_djokovic_banned

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