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MARTINI_enrich_BERTopic_DrTessLawrie
AIDA-UPM/MARTINI_enrich_BERTopic_DrTessLawrie
MARTINI_enrich_BERTopic_DrTessLawrie 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
- a40573c9
Try a prompt
Model card
From the Hugging Face model README
MARTINI_enrich_BERTopic_DrTessLawrie
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_DrTessLawrie")
topic_model.get_topic_info()
Topic overview
- Number of topics: 11
- Number of training documents: 824
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | vaccines - homeopathy - drtesslawrie - newsroom - jam | 21 | -1_vaccines_homeopathy_drtesslawrie_newsroom |
| 0 | betterwayconference - speakers - virtual - veronika - neil | 454 | 0_betterwayconference_speakers_virtual_veronika |
| 1 | vaccinated - pfizer - pharmacovigilance - injections - drtesslawrie | 94 | 1_vaccinated_pfizer_pharmacovigilance_injections |
| 2 | worldivermectinday - hydroxychloroquine - ivm - julian - bifidobacteria | 66 | 2_worldivermectinday_hydroxychloroquine_ivm_julian |
| 3 | outreach - empowering - healthier - hello - donate | 40 | 3_outreach_empowering_healthier_hello |
| 4 | thegreatfreeset - brainwashing - sovereign - democracy - beings | 38 | 4_thegreatfreeset_brainwashing_sovereign_democracy |
| 5 | parliamentlive - westminster - amendments - petition - uwebtqpm | 23 | 5_parliamentlive_westminster_amendments_petition |
| 6 | pandemic - supranational - amendments - monopolni - poder | 22 | 6_pandemic_supranational_amendments_monopolni |
| 7 | vaccine - plasmids - contaminated - findings - panelists | 22 | 7_vaccine_plasmids_contaminated_findings |
| 8 | healthiwealthi - chiropractic - fisslinger - dailyclout - whistleblowing | 22 | 8_healthiwealthi_chiropractic_fisslinger_dailyclout |
| 9 | webinar - peace - nzdt - importance - psychotherapy | 22 | 9_webinar_peace_nzdt_importance |
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