transformers_issues_topics

transformers_issues_topics

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("Ruslan10/transformers_issues_topics")

topic_model.get_topic_info()

Topic overview

• Number of topics: 30
• Number of training documents: 9000

<details> <summary>Click here for an overview of all topics.</summary> </details>

Training hyperparameters

• calculate_probabilities: False
• language: english
• low_memory: False
• min_topic_size: 10
• n_gram_range: (1, 1)
• nr_topics: 30
• seed_topic_list: None
• top_n_words: 10
• verbose: True
• zeroshot_min_similarity: 0.7
• zeroshot_topic_list: None

Framework versions

• Numpy: 2.0.2
• HDBSCAN: 0.8.41
• UMAP: 0.5.11
• Pandas: 2.2.2
• Scikit-Learn: 1.6.1
• Sentence-transformers: 5.2.0
• Transformers: 4.57.6
• Numba: 0.60.0
• Plotly: 5.24.1
• Python: 3.12.12