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:

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

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	electric - vehicles - ev - electric vehicles - charging	6	-1_electric_vehicles_ev_electric vehicles
0	cars - vehicles - electric - car - parking	58	0_cars_vehicles_electric_car
1	chinese - electric - byd - china - cars	30	1_chinese_electric_byd_china
2	charging - charge - ev - public - electric	27	2_charging_charge_ev_public
3	tesla - musk - dollars - elon - elon musk	23	3_tesla_musk_dollars_elon
4	new - electric - vehicles - car - drivers	21	4_new_electric_vehicles_car
5	porsche - taycan - car - electric - garage	13	5_porsche_taycan_car_electric
6	foxconn - mitsubishi - japanese - nissan - electric	11	6_foxconn_mitsubishi_japanese_nissan
7	nikola - bankruptcy - lucid - northvolt - assets	10	7_nikola_bankruptcy_lucid_northvolt

Training hyperparameters

calculate_probabilities: False
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: True
zeroshot_min_similarity: 0.7
zeroshot_topic_list: 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