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MARTINI_enrich_BERTopic_HarveyRisch
AIDA-UPM/MARTINI_enrich_BERTopic_HarveyRisch
MARTINI_enrich_BERTopic_HarveyRisch is a text classification model from AIDA-UPM. Use it when you need a label for a piece of text. It is set up for bertopic.
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.
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At a glance
- Task
- Text Classification
- Library
- bertopic
- Access
- Public
- Created
- Jan 13, 2025
- Updated
- Jan 13, 2025
- SHA
- c0df3948
Try a prompt
Model card
From the Hugging Face model README
MARTINI_enrich_BERTopic_HarveyRisch
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_HarveyRisch")
topic_model.get_topic_info()
Topic overview
- Number of topics: 8
- Number of training documents: 715
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | vaccinated - pfizer - deaths - misinformation - kennedy | 28 | -1_vaccinated_pfizer_deaths_misinformation |
| 0 | vaccinated - pfizer - antibodies - plasmid - injection | 394 | 0_vaccinated_pfizer_antibodies_plasmid |
| 1 | vax - myocarditis - cordis - deaths - mri | 75 | 1_vax_myocarditis_cordis_deaths |
| 2 | doctors - harvey - marik - integrative - webinar | 74 | 2_doctors_harvey_marik_integrative |
| 3 | ivermectin - hydroxychloroquine - remdesivir - penicillin - prescribing | 46 | 3_ivermectin_hydroxychloroquine_remdesivir_penicillin |
| 4 | unvaccinated - polio - children - allergies - cnbc | 36 | 4_unvaccinated_polio_children_allergies |
| 5 | masks - omicron - hospitals - mandatory - texas | 31 | 5_masks_omicron_hospitals_mandatory |
| 6 | fauci - wuhan - coronaviruses - bioweapons - conspired | 31 | 6_fauci_wuhan_coronaviruses_bioweapons |
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
- Task
- Text Classification
- Library
- bertopic
- Created
- Jan 13, 2025
- Updated
- Jan 13, 2025