MARTINI_enrich_BERTopic_Qnews
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_Qnews")
topic_model.get_topic_info()
Topic overview
- Number of topics: 14
- Number of training documents: 1105
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | obama - republicans - washington - conspiracy - ballot | 21 | -1_obama_republicans_washington_conspiracy |
| 0 | iowa - caucus - speech - patriots - 45th | 565 | 0_iowa_caucus_speech_patriots |
| 1 | illegals - texas - border - governor - reinforcements | 143 | 1_illegals_texas_border_governor |
| 2 | zelensky - nordstream - kakhovka - podcast - russell | 65 | 2_zelensky_nordstream_kakhovka_podcast |
| 3 | twitter - fbi - tyranny - australia - banned | 45 | 3_twitter_fbi_tyranny_australia |
| 4 | bidenomics - huckabee - joe - delaware - higher | 44 | 4_bidenomics_huckabee_joe_delaware |
| 5 | polls - republican - leading - kudlow - nbc | 37 | 5_polls_republican_leading_kudlow |
| 6 | verdict - prosecutor - kamala - dismissed - georgia | 32 | 6_verdict_prosecutor_kamala_dismissed |
| 7 | voters - disenfranchise - cheated - results - missouri | 31 | 7_voters_disenfranchise_cheated_results |
| 8 | soleimani - hamas - jerusalem - egypt - accords | 29 | 8_soleimani_hamas_jerusalem_egypt |
| 9 | autoworkers - electric - joe - repeal - towing | 25 | 9_autoworkers_electric_joe_repeal |
| 10 | republican - globalists - 2024 - jeb - upcoming | 24 | 10_republican_globalists_2024_jeb |
| 11 | desantis - cronies - veto - resigned - flip | 22 | 11_desantis_cronies_veto_resigned |
| 12 | agenda - defunded - america - abolish - revolutionize | 22 | 12_agenda_defunded_america_abolish |
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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