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:
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
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