|
|
| ---
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| tags:
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| - bertopic
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| library_name: bertopic
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| pipeline_tag: text-classification
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| ---
|
|
|
| # bertopic_openai_emb_model
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|
|
| This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
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| BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
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|
|
| ## Usage
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|
|
| To use this model, please install BERTopic:
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|
|
| ```
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| pip install -U bertopic
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| ```
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|
|
| You can use the model as follows:
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|
|
| ```python
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| from bertopic import BERTopic
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| topic_model = BERTopic.load("MaximSIMO/bertopic_openai_emb_model")
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|
|
| topic_model.get_topic_info()
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| ```
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|
|
| ## Topic overview
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|
|
| * Number of topics: 3
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| * Number of training documents: 100
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|
|
| <details>
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| <summary>Click here for an overview of all topics.</summary>
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|
|
| | Topic ID | Topic Keywords | Topic Frequency | Label |
|
| |----------|----------------|-----------------|-------|
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| | -1 | Evening TV Programming | 13 | -1_Evening TV Programming |
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| | 0 | Elettrodotti e ambiente | 15 | 0_Elettrodotti e ambiente |
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| | 1 | Political Tensions | 72 | 1_Political Tensions |
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|
|
| </details>
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|
|
| ## Training hyperparameters
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|
|
| * calculate_probabilities: False
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| * language: multilingual
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| * low_memory: False
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| * min_topic_size: 10
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| * n_gram_range: (1, 1)
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| * nr_topics: None
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| * seed_topic_list: None
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| * top_n_words: 10
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| * verbose: True
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| * zeroshot_min_similarity: 0.7
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| * zeroshot_topic_list: None
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|
|
| ## Framework versions
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|
|
| * Numpy: 2.2.6
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| * HDBSCAN: 0.8.41
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| * UMAP: 0.5.11
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| * Pandas: 2.3.3
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| * Scikit-Learn: 1.7.2
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| * Sentence-transformers: 5.2.2
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| * Transformers: 5.1.0
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| * Numba: 0.63.1
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| * Plotly: 6.5.2
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| * Python: 3.10.19
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| |
