MARTINI_enrich_BERTopic_PatAltNorthWest

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_PatAltNorthWest")

topic_model.get_topic_info()

Topic overview

• Number of topics: 6
• Number of training documents: 286

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	salford - britain - blackpool - migrants - downes	20	-1_salford_britain_blackpool_migrants
0	merseyside - litter - pinewoods - alderley - lakeside	153	0_merseyside_litter_pinewoods_alderley
1	whites - lewthwaite - yorkshire - propaganda - bnp	38	1_whites_lewthwaite_yorkshire_propaganda
2	patriotic - merseyside - attended - memorials - peralta	27	2_patriotic_merseyside_attended_memorials
3	oldham - mohammed - murders - bbc - sexually	24	3_oldham_mohammed_murders_bbc
4	migrants - britannia - merseyside - pontins - protest	24	4_migrants_britannia_merseyside_pontins

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