| |
|
| | ---
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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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| | ---
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| |
|
| | # bertopic_openai_emb_model
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| |
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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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| |
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| | ## Usage
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| |
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| | To use this model, please install BERTopic:
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| |
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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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| |
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| | topic_model.get_topic_info()
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| | ```
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| |
|
| | ## Topic overview
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| |
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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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| |
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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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| | |
