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AIDA-UPM/
MARTINI_enrich_BERTopic_ilkelhaber

Name: MARTINI_enrich_BERTopic_ilkelhaber
Availability: InStock

bertopic

text-classification

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MARTINI_enrich_BERTopic_ilkelhaber

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("AIDA-UPM/MARTINI_enrich_BERTopic_ilkelhaber")

topic_model.get_topic_info()

Topic overview

Number of topics: 7
Number of training documents: 440

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	freebet - yatırıma - kripto - kanalımız - kılıcdaroglu	25	-1_freebet_yatırıma_kripto_kanalımız
0	kılıcdaroglu - ataturk - cumhurbaskanı - fatih - mustafa	148	0_kılıcdaroglu_ataturk_cumhurbaskanı_fatih
1	bıcakladı - sarıkaya - diyarbakır - sahıslardan - cagatay	102	1_bıcakladı_sarıkaya_diyarbakır_sahıslardan
2	fiyatları - iphone - 2023 - redmi - isıkhan	53	2_fiyatları_iphone_2023_redmi
3	tasınır - sikayetci - amacım - ahmet - saldırdı	46	3_tasınır_sikayetci_amacım_ahmet
4	israil - hamas - netanyahu - basbakanı - kayıplarımız	38	4_israil_hamas_netanyahu_basbakanı
5	inisiyatifleriyle - tiktok - twitter - sosyal - kullanıcısı	28	5_inisiyatifleriyle_tiktok_twitter_sosyal

Training hyperparameters

calculate_probabilities: True
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: 1.26.4
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