Instructions to use lgdias/tces_v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BERTopic
How to use lgdias/tces_v1.5 with BERTopic:
from bertopic import BERTopic model = BERTopic.load("lgdias/tces_v1.5") - Notebooks
- Google Colab
- Kaggle
tces_v1.5
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("lgdias/tces_v1.5")
topic_model.get_topic_info()
Topic overview
- Number of topics: 2
- Number of training documents: 11927
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| 0 | art - portaria - goias - estadual - lei | 10090 | 0_art_portaria_goias_estadual |
| 1 | suplementar - fonte - saldo - orcamentaria - despesa | 1837 | 1_suplementar_fonte_saldo_orcamentaria |
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: 2.2.6
- HDBSCAN: 0.8.39
- UMAP: 0.5.9.post2
- Pandas: 2.3.3
- Scikit-Learn: 1.7.2
- Sentence-transformers: 5.1.1
- Transformers: 4.57.0
- Numba: 0.61.2
- Plotly: 6.3.1
- Python: 3.10.18
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