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karinegabsschon/BERTopic_Legal

sourceHugging Faceupdated 1y agoView on Hugging Face

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Model Card

tags:

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

BERTopic_Legal

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:

python

from bertopic import BERTopic
topic_model = BERTopic.load("karinegabsschon/BERTopic_Legal")

topic_model.get_topic_info()

Topic overview

—Number of topics: 9
—Number of training documents: 199

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

Topic ID	Topic Keywords	Topic Frequency	Label
-1	electric - vehicles - ev - electric vehicles - charging	6	-1electricvehiclesevelectric vehicles
0	cars - vehicles - electric - car - parking	58	0carsvehicleselectriccar
1	chinese - electric - byd - china - cars	30	1chineseelectricbydchina
2	charging - charge - ev - public - electric	27	2chargingchargeevpublic
3	tesla - musk - dollars - elon - elon musk	23	3teslamuskdollarselon
4	new - electric - vehicles - car - drivers	21	4newelectricvehiclescar
5	porsche - taycan - car - electric - garage	13	5porschetaycancarelectric
6	foxconn - mitsubishi - japanese - nissan - electric	11	6foxconnmitsubishijapanesenissan
7	nikola - bankruptcy - lucid - northvolt - assets	10	7nikolabankruptcylucidnorthvolt

</details>

Training hyperparameters

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

Framework versions

—Numpy: 2.0.2
—HDBSCAN: 0.8.40
—UMAP: 0.5.8
—Pandas: 2.2.2
—Scikit-Learn: 1.6.1
—Sentence-transformers: 4.1.0
—Transformers: 4.53.0
—Numba: 0.60.0
—Plotly: 5.24.1
—Python: 3.11.13