metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
BERTopic_mincevicius
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_mincevicius")
topic_model.get_topic_info()
Topic overview
- Number of topics: 3
- Number of training documents: 10133
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| 0 | vyriausybe - paskelbe - pries - rusijos - ukrainos | 8779 | 0_vyriausybe_paskelbe_pries_rusijos |
| 1 | vyriausybe - pries - visis - rusijos - ukrainos | 1336 | 1_vyriausybe_pries_visis_rusijos |
| 2 | republics - pedophiles - awakenedspecies - booster - wins | 18 | 2_republics_pedophiles_awakenedspecies_booster |
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