| |
|
| | --- |
| | tags: |
| | - bertopic |
| | library_name: bertopic |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | # MARTINI_enrich_BERTopic_PascalNajadiNEWS |
| | |
| | This is a [BERTopic](https://github.com/MaartenGr/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: |
| | |
| | ```python |
| | from bertopic import BERTopic |
| | topic_model = BERTopic.load("AIDA-UPM/MARTINI_enrich_BERTopic_PascalNajadiNEWS") |
| | |
| | topic_model.get_topic_info() |
| | ``` |
| | |
| | ## Topic overview |
| | |
| | * Number of topics: 14 |
| | * Number of training documents: 1141 |
| | |
| | <details> |
| | <summary>Click here for an overview of all topics.</summary> |
| | |
| | | Topic ID | Topic Keywords | Topic Frequency | Label | |
| | |----------|----------------|-----------------|-------| |
| | | -1 | trump - monde - commandant - democide - jetzt | 22 | -1_trump_monde_commandant_democide | |
| | | 0 | fauci - mondiale - democide - jamais - biologiques | 521 | 0_fauci_mondiale_democide_jamais | |
| | | 1 | gaulle - republique - allez - presentateurs - hiver | 103 | 1_gaulle_republique_allez_presentateurs | |
| | | 2 | gendarmerie - republique - natale - bonjour - wahrheit | 96 | 2_gendarmerie_republique_natale_bonjour | |
| | | 3 | dɪsˈkloʊʒər - disclosure - desclos - definition - terbuka | 85 | 3_dɪsˈkloʊʒər_disclosure_desclos_definition | |
| | | 4 | impfschaden - injektionen - pfizer - strafanzeige - swiss | 58 | 4_impfschaden_injektionen_pfizer_strafanzeige | |
| | | 5 | geneve - documentaire - serpente - fauci - milioni | 56 | 5_geneve_documentaire_serpente_fauci | |
| | | 6 | usaf - luftstreitkrafte - nigthwatch - trump - commandant | 49 | 6_usaf_luftstreitkrafte_nigthwatch_trump | |
| | | 7 | pfizer - lawsuit - ny - scotus - mccarthy | 29 | 7_pfizer_lawsuit_ny_scotus | |
| | | 8 | biden - inauguration - ejecutado - treason - janvier | 27 | 8_biden_inauguration_ejecutado_treason | |
| | | 9 | ukraine - zelensky - schweizer - paradeplatz - mikhail | 26 | 9_ukraine_zelensky_schweizer_paradeplatz | |
| | | 10 | democratie - schweiz - militarisation - thedocuments - toujours | 24 | 10_democratie_schweiz_militarisation_thedocuments | |
| | | 11 | swissmedic - sworn - soldiers - verfassungsmaßige - gezwungen | 23 | 11_swissmedic_sworn_soldiers_verfassungsmaßige | |
| | | 12 | unabhangigkeitskrieges - federalize - delaware - decembre - 1776 | 22 | 12_unabhangigkeitskrieges_federalize_delaware_decembre | |
| | |
| | </details> |
| | |
| | ## 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 |
| | |
