metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
rag-topic-model
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("ivanleomk/rag-topic-model")
topic_model.get_topic_info()
Topic overview
- Number of topics: 5
- Number of training documents: 243
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | the - verification - my - for - code | 24 | -1_the_verification_my_for |
| 0 | klarna - to - my - and - the | 19 | 0_klarna_to_my_and |
| 1 | my - the - return - store - still | 98 | 1_my_the_return_store |
| 2 | card - onetime - my - it - for | 69 | 2_card_onetime_my_it |
| 3 | payment - my - due - date - the | 33 | 3_payment_my_due_date |
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: False
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 2.0.2
- HDBSCAN: 0.8.40
- UMAP: 0.5.7
- Pandas: 2.2.3
- Scikit-Learn: 1.5.2
- Sentence-transformers: 3.3.1
- Transformers: 4.46.3
- Numba: 0.60.0
- Plotly: 5.24.1
- Python: 3.9.6