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sdantonio/
BERTopic_teyakkuzhaber

Name: BERTopic_teyakkuzhaber
Availability: InStock

bertopic

text-classification

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BERTopic_teyakkuzhaber

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("sdantonio/BERTopic_teyakkuzhaber")

topic_model.get_topic_info()

Topic overview

Number of topics: 4
Number of training documents: 2382

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	depremde - yu - vatandas - kaybedem - sondaki	28	-1_depremde_yu_vatandas_kaybedem
0	alıs - kılıc - abd - yas - kars	1	0_alıs_kılıc_abd_yas
1	osmaniye - endonezya - konya - sondakika - adıyaman	2323	1_osmaniye_endonezya_konya_sondakika
2	984tl - 971tl - 1093tl - kırılmaya - anlık	30	2_984tl_971tl_1093tl_kırılmaya

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: False
zeroshot_min_similarity: 0.7
zeroshot_topic_list: None

Framework versions

Numpy: 1.23.5
HDBSCAN: 0.8.38.post1
UMAP: 0.5.6
Pandas: 2.2.2
Scikit-Learn: 1.5.1
Sentence-transformers: 3.0.1
Transformers: 4.44.2
Numba: 0.60.0
Plotly: 5.24.0
Python: 3.10.12