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:
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
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