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--- |
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tags: |
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- bertopic |
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library_name: bertopic |
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pipeline_tag: text-classification |
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--- |
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# MARTINI_enrich_BERTopic_UKcitizen2021 |
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. |
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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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## Usage |
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To use this model, please install BERTopic: |
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``` |
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pip install -U bertopic |
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``` |
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You can use the model as follows: |
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```python |
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from bertopic import BERTopic |
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topic_model = BERTopic.load("AIDA-UPM/MARTINI_enrich_BERTopic_UKcitizen2021") |
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topic_model.get_topic_info() |
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``` |
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## Topic overview |
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* Number of topics: 11 |
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* Number of training documents: 721 |
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<details> |
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<summary>Click here for an overview of all topics.</summary> |
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| Topic ID | Topic Keywords | Topic Frequency | Label | |
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|----------|----------------|-----------------|-------| |
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| -1 | lockdown - wef - monkeypox - agenda - nuremberg | 22 | -1_lockdown_wef_monkeypox_agenda | |
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| 0 | vaccinated - pfizer - vaxx - worldcouncilforhealth - injections | 313 | 0_vaccinated_pfizer_vaxx_worldcouncilforhealth | |
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| 1 | ukcitizen2021 - amendments - supranational - pandemic - wgihr | 89 | 1_ukcitizen2021_amendments_supranational_pandemic | |
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| 2 | constable - arrested - victims - allegations - thamesvalley | 73 | 2_constable_arrested_victims_allegations | |
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| 3 | bbc - matt - tomorrow - southampton - everywhere | 51 | 3_bbc_matt_tomorrow_southampton | |
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| 4 | vaccines - mhra - parliamentary - claims - chope | 42 | 4_vaccines_mhra_parliamentary_claims | |
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| 5 | ukcitizen2021 - councils - mobilise - responses - nottinghamshire | 35 | 5_ukcitizen2021_councils_mobilise_responses | |
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| 6 | ivermectin - hydroxychloroquine - quercetin - iodine - denied | 26 | 6_ivermectin_hydroxychloroquine_quercetin_iodine | |
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| 7 | solicitors - mhra - regulatory - bayliss - allegations | 25 | 7_solicitors_mhra_regulatory_bayliss | |
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| 8 | vaccination - nhs - consent - compulsory - jobsnotjabs | 23 | 8_vaccination_nhs_consent_compulsory | |
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| 9 | digital - controligarchs - england - passport - currency | 22 | 9_digital_controligarchs_england_passport | |
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</details> |
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## Training hyperparameters |
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* calculate_probabilities: True |
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* language: None |
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* low_memory: False |
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* min_topic_size: 10 |
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* n_gram_range: (1, 1) |
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* nr_topics: None |
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* seed_topic_list: None |
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* top_n_words: 10 |
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* verbose: False |
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* zeroshot_min_similarity: 0.7 |
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* zeroshot_topic_list: None |
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## Framework versions |
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* Numpy: 1.26.4 |
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* HDBSCAN: 0.8.40 |
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* UMAP: 0.5.7 |
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* Pandas: 2.2.3 |
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* Scikit-Learn: 1.5.2 |
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* Sentence-transformers: 3.3.1 |
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* Transformers: 4.46.3 |
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* Numba: 0.60.0 |
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* Plotly: 5.24.1 |
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* Python: 3.10.12 |
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