AIDA-UPM/MARTINI_enrich_BERTopic_PascalNajadiNEWS

tags:

• —bertopic libraryname: bertopic pipelinetag: text-classification ---

MARTINIenrichBERTopic_PascalNajadiNEWS

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_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>

</details>

Training hyperparameters

• —calculate_probabilities: True
• —language: None
• —low_memory: False
• —mintopicsize: 10
• —ngramrange: (1, 1)
• —nr_topics: None
• —seedtopiclist: None
• —topnwords: 10
• —verbose: False
• —zeroshotminsimilarity: 0.7
• —zeroshottopiclist: 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