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MARTINI_enrich_BERTopic_UKcitizen2021
AIDA-UPM/MARTINI_enrich_BERTopic_UKcitizen2021
MARTINI_enrich_BERTopic_UKcitizen2021 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 14, 2025
- Updated
- Jan 14, 2025
- SHA
- 6dbeff32
Try a prompt
Model card
From the Hugging Face model README
MARTINI_enrich_BERTopic_UKcitizen2021
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_UKcitizen2021")
topic_model.get_topic_info()
Topic overview
- Number of topics: 11
- Number of training documents: 721
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | lockdown - wef - monkeypox - agenda - nuremberg | 22 | -1_lockdown_wef_monkeypox_agenda |
| 0 | vaccinated - pfizer - vaxx - worldcouncilforhealth - injections | 313 | 0_vaccinated_pfizer_vaxx_worldcouncilforhealth |
| 1 | ukcitizen2021 - amendments - supranational - pandemic - wgihr | 89 | 1_ukcitizen2021_amendments_supranational_pandemic |
| 2 | constable - arrested - victims - allegations - thamesvalley | 73 | 2_constable_arrested_victims_allegations |
| 3 | bbc - matt - tomorrow - southampton - everywhere | 51 | 3_bbc_matt_tomorrow_southampton |
| 4 | vaccines - mhra - parliamentary - claims - chope | 42 | 4_vaccines_mhra_parliamentary_claims |
| 5 | ukcitizen2021 - councils - mobilise - responses - nottinghamshire | 35 | 5_ukcitizen2021_councils_mobilise_responses |
| 6 | ivermectin - hydroxychloroquine - quercetin - iodine - denied | 26 | 6_ivermectin_hydroxychloroquine_quercetin_iodine |
| 7 | solicitors - mhra - regulatory - bayliss - allegations | 25 | 7_solicitors_mhra_regulatory_bayliss |
| 8 | vaccination - nhs - consent - compulsory - jobsnotjabs | 23 | 8_vaccination_nhs_consent_compulsory |
| 9 | digital - controligarchs - england - passport - currency | 22 | 9_digital_controligarchs_england_passport |
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 14, 2025
- Updated
- Jan 14, 2025