| |
|
| | --- |
| | tags: |
| | - bertopic |
| | library_name: bertopic |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | # MARTINI_enrich_BERTopic_shadowman2020 |
| | |
| | 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_shadowman2020") |
| | |
| | topic_model.get_topic_info() |
| | ``` |
| | |
| | ## Topic overview |
| | |
| | * Number of topics: 8 |
| | * Number of training documents: 656 |
| | |
| | <details> |
| | <summary>Click here for an overview of all topics.</summary> |
| | |
| | | Topic ID | Topic Keywords | Topic Frequency | Label | |
| | |----------|----------------|-----------------|-------| |
| | | -1 | antifa - britain - veterans - boris - invasion | 27 | -1_antifa_britain_veterans_boris | |
| | | 0 | coronavirus - pandemic - pfizer - nhs - masks | 274 | 0_coronavirus_pandemic_pfizer_nhs | |
| | | 1 | protesters - palestine - barricades - dublin - stabbed | 103 | 1_protesters_palestine_barricades_dublin | |
| | | 2 | illegals - migrant - britain - dover - invaded | 71 | 2_illegals_migrant_britain_dover | |
| | | 3 | biden - votes - recount - michigan - republicans | 60 | 3_biden_votes_recount_michigan | |
| | | 4 | syria - airstrikes - mercenaries - karabakh - pentagon | 44 | 4_syria_airstrikes_mercenaries_karabakh | |
| | | 5 | london - veterans - poppies - cenotaph - doherty | 39 | 5_london_veterans_poppies_cenotaph | |
| | | 6 | telegram - patrioticcrusader - youtube - subscribed - banned | 38 | 6_telegram_patrioticcrusader_youtube_subscribed | |
| | |
| | </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 |
| | |
