metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
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("belenedgar/transformers_issues_topics")
topic_model.get_topic_info()
Topic overview
- Number of topics: 5
- Number of training documents: 156
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | malware - malicious - viruses - ransomware - adware | 25 | -1_malware_malicious_viruses_ransomware |
| 0 | phishing - fraudsters - theft - scammers - security | 25 | 0_phishing_fraudsters_theft_scammers |
| 1 | addiction - cyber - screen - gaming - persona | 48 | 1_addiction_cyber_screen_gaming |
| 2 | cyberbullying - bullying - cyber - cyberstalking - harassment | 32 | 2_cyberbullying_bullying_cyber_cyberstalking |
| 3 | profanity - derogatory - vulgarity - hate - offensive | 26 | 3_profanity_derogatory_vulgarity_hate |
Training hyperparameters
- calculate_probabilities: False
- language: english
- 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
Framework versions
- Numpy: 1.24.4
- HDBSCAN: 0.8.33
- UMAP: 0.5.3
- Pandas: 2.0.3
- Scikit-Learn: 1.3.0
- Sentence-transformers: 2.2.2
- Transformers: 4.31.0
- Numba: 0.57.1
- Plotly: 5.15.0
- Python: 3.10.10