vidric/bertopic_after_tuning

tags:

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

bertopicaftertuning

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("vidric/bertopic_after_tuning")

topic_model.get_topic_info()

Topic overview

• —Number of topics: 68
• —Number of training documents: 20977

<details> <summary>Click here for an overview of all topics.</summary>

</details>

Training hyperparameters

• —calculate_probabilities: False
• —language: indonesian
• —low_memory: False
• —mintopicsize: 10
• —ngramrange: (1, 1)
• —nr_topics: auto
• —seedtopiclist: None
• —topnwords: 10
• —verbose: False

Framework versions

• —Numpy: 1.23.5
• —HDBSCAN: 0.8.29
• —UMAP: 0.5.3
• —Pandas: 1.5.3
• —Scikit-Learn: 1.2.1
• —Sentence-transformers: 2.2.2
• —Transformers: 4.24.0
• —Numba: 0.56.4
• —Plotly: 5.9.0
• —Python: 3.10.9